Image processing method and apparatus

Abstract: An operation of an iris filter is carried out on an original image signal representing an image, and the degree of centralization of gradients of the original image signal with respect to a picture element is thereby calculated, each of picture elements constituting the image being taken as the picture element. An image portion, which is associated with a high degree of centralization, in the image is detected in accordance with the calculated degree of centralization. Image emphasis processing is then selectively carried out on the detected image portion. Alternatively, a morphology operation is carried out on an original image signal Dorg by using a multiply structure element Bi and a scale factor λ, and a morphology signal Dmor is thereby obtained. The morphology signal Dmor represents whether each of picture elements constituting the image is or is not the one corresponding to an image portion, at which the original image signal Dorg fluctuates in a spatially narrower range than the multiply structure element Bi. Image emphasis processing is then carried out on the original image signal Dorg and in accordance with the morphology signal Dmor.

Description: BACKGROUND OF THE INVENTION Field of the Invention This invention relates to an image processing method. This invention particularly relates to an image processing method, wherein a specific image portion, such as an abnormal pattern or a high-contrast image portion, which is embedded in an image, is emphasized selectively. Description of the Prior Art Image processing, such as gradation processing or frequency processing, has heretofore been carried out on an image signal, which represents an image and has been obtained with one of various image obtaining methods, such that a visible image having good image quality can be reproduced and used as an effective tool in, particularly, the accurate and efficient diagnosis of an illness. Particularly, in the field of medical images, such as radiation images of human bodies serving as objects, it is necessary for specialists, such as doctors, to make an accurate diagnosis of an illness or an injury of the patient in accordance with the obtained image. Therefore, it is essential to carry out the image processing in order that a visible image having good image quality can be reproduced and used as an effective tool in the accurate and efficient diagnosis of an illness. As one of the image processing, frequency emphasis processing has been disclosed in, for example, Japanese Unexamined Patent Publication No. 61(1986)-169971. With the disclosed frequency emphasis processing, an image signal (i.e., an original image signal) Dorg representing the image density value of an original image is converted into an image signal Dproc with Formula (36). Dproc=Dorg + β × (Dorg-Dus) wherein β represents the frequency emphasis coefficient, and Dus represents the unsharp mask signal. The unsharp mask signal Dus comprises super-low frequency components obtained by setting a mask, i.e. an unsharp mask, constituted of a picture element matrix, which has a size of N columns × N rows (wherein N represents an odd number) and has its center at the picture element represented by the original image signal Dorg, in a two-dimensional array of picture elements. The unsharp mask signal Dus is calculated with, for example, Formula (37). Dus= (∑Dorg)/N 2 wherein ∑Dorg represents the sum of the image signal values representing the picture elements located within the unsharp mask. The value of (Dorg-Dus) in the parenthesis of the second term of Formula (36) is obtained by subtracting the unsharp mask signal, which represents the super-low frequency components, from the original image signal. Therefore, comparatively high frequency components can be extracted selectively by subtracting the super-low frequency components from the original image signal. The comparatively high frequency components are then multiplied by the frequency emphasis coefficient β, and the obtained product is added to the original image signal. In this manner, the comparatively high frequency components can be emphasized. Also, iris filter processing (hereinbelow often referred to as the operation of the iris filter) has heretofore been known as the operation processing for selectively extracting only a specific image portion, such as an abnormal pattern, from an image. [Reference should be made to "Detection of Tumor Patterns in DR Images (Iris Filter)," Obata, et al., Collected Papers of The Institute of Electronics and Communication Engineers of Japan, D-II, Vol. J75-D-II, No. 3, pp. 663-670, March 1992.] The iris filter processing has been studied as a technique efficient for detecting, particularly, a tumor pattern, which is one of characteristic forms of mammary cancers. However, the image to be processed with the iris filter is not limited to the tumor pattern in a mammogram, and the iris filter processing is applicable to any kind of image having the characteristics such that the gradients of the image signal representing the image are centralized. How the processing for detecting the image portion with the iris filter is carried out will be described hereinbelow by taking the processing for the detection of the tumor pattern as an example. It has been known that, for example, in a radiation image recorded on a negative X-ray film (i.e., an image yielding an image signal of a high signal level for a high image density), the density values of a tumor pattern are slightly smaller than the density values of the surrounding image areas. The density values of the tumor pattern are distributed such that the density value becomes smaller from the periphery of an approximately circular tumor pattern toward the center point of the tumor pattern. Therefore, in the tumor pattern, gradients of the density values can be found in local areas, and the gradient lines (i.e., gradient vectors) centralize in the directions heading toward the center point of the tumor pattern. With the iris filter, the gradients of image signal values, which are represented by the density values, are calculated as gradient vectors, the degree of centralization of the gradient vectors is calculated, and a tumor pattern is detected in accordance with the calculated degree of centralization of the gradient vectors. Specifically, the gradient vector at an arbitrary picture element in a tumor pattern is directed to the vicinity of the center point of the tumor pattern. On the other hand, in an elongated pattern, such as a blood vessel pattern, gradient vectors do not centralize upon a specific point. Therefore, the distributions of the directions of the gradient vectors in local areas may be evaluated, and a region, in which the gradient vectors centralize upon a specific point, may be detected. The thus detected region may be taken as a prospective tumor pattern, which is considered as being a tumor pattern. The processing with the iris filter is based on such fundamental concept. Steps of algorithms of the iris filter will be described hereinbelow. (Step 1) Calculation of gradient vectors For each picture element j among all of the picture elements constituting a given image, the direction 8 of the gradient vector of the image signal representing the image is calculated with Formula (38).  = tan-1 (f 3 + f 4 + f 5 + f 6 + f 7 ) - (f 11 + f 12 + f 13 + f 14 + f 15 ) (f 1 + f 2 + f 3 + f 15 + f 16 ) - (f 7 + f 8 + f 9 + f 10 + f 11 ) As illustrated in Figure 5, f 1 through f 16 in Formula (38) represent the density values (i.e., the image signal values) corresponding to the picture elements located at the peripheral areas of a mask, which has a size of five picture elements (located along the column direction of the picture element array) × five picture elements (located along the row direction of the picture element array) and which has its center at the picture element j. (Step 2) Calculation of the degree of centralization of gradient vectors Thereafter, for each picture element among all of the picture elements constituting the given image, the picture element is taken as a picture element of interest, and the degree of centralization C of the gradient vectors with respect to the picture element of interest is calculated with Formula (39). As illustrated in Figure 6, in Formula (39), N represents the number of the picture elements located in the region inside of a circle, which has its center at the picture element of interest and has a radius R, and j represents the angle made between the straight line, which connects the picture element of interest and each picture element j located in the circle, and the gradient vector at the picture element j, which gradient vector has been calculated with Formula (38). Therefore, in cases where the directions of the gradient vectors of the respective picture elements j centralize upon the picture element of interest, the degree of centralization C represented by Formula (39) takes a large value. The gradient vector of each picture element j, which is located in the vicinity of a tumor pattern, is directed approximately to the center portion of the tumor pattern regardless of the level of the contrast of the tumor pattern. Therefore, it can be regarded that the picture element of interest associated with the degree of centralization C, which takes a large value, is the picture element located at the center portion of the tumor pattern. On the other hand, in a linear pattern, such as a blood vessel pattern, the directions of the gradient vectors are biased to a certain direction, and therefore the value of the degree of centralization C is small. Accordingly, a tumor pattern can be detected by taking each of all picture elements, which constitute the image, as the picture element of interest, calculating the value of the degree of centralization C with respect to the picture element of interest, and rating whether the value of the degree of centralization C is or is not larger than a predetermined threshold value. Specifically, the processing with the iris filter has the features over an ordinary difference filter in that the processing with the iris filter is not apt to be adversely affected by blood vessel patterns, mammary gland patterns, or the like, and can efficiently detect tumor patterns. In actual processing, such that the detection performance unaffected by the sizes and shapes of tumor patterns may be achieved, it is contrived to adaptively change the size and the shape of the filter. Figure 7 shows an example of the filter. The filter is different from the filter shown in Figure 6. With the filter of Figure 7, the degree of centralization is rated only with the picture elements, which are located along radial lines extending radially from a picture element of interest in M kinds of directions at 2π/M degree intervals. (In Figure 7, by way of example, 32 directions at 11.25 degree intervals are shown.) In cases where the picture element of interest has the coordinates (k, l), the coordinates ([x], [y]) of the picture element, which is located along an i'th radial line and is the n'th picture element as counted from the picture element of interest, are given by Formulas (40) and (41). x = k+n cos {2π (i-1/M} y = l+n sin {2π (i-1)/M} wherein [x] represents the maximum integer, which does not exceed x, and [y] represents the maximum integer, which does not exceed y. Also, for each of the radial lines, the output value obtained for the picture elements ranging from the picture element of interest to a picture element, which is located along the radial line and at which the maximum degree of centralization is obtained, is taken as the degree of centralization with respect to the direction of the radial line. The mean value of the degrees of centralization, which have been obtained for all of the radial lines, is then calculated. The mean value of the degrees of centralization having thus been calculated is taken as the degree of centralization C of the gradient vector group with respect to the picture element of interest. Specifically, the degree of centralization Ci(n), which is obtained for the picture elements ranging from the picture element of interest to the n'th picture element located along the i'th radial line, is calculated with Formula (42). wherein Rmin and Rmax respectively represent the minimum value and the maximum value having been set for the radius of the tumor pattern, which is to be detected. The calculation of the degree of centralization Ci(n) may be carried out by using Formula (42') in lieu of Formula (42). Specifically, with Formula (42'), the degree of centralization Ci(n) is obtained for the picture elements, which are located along the i'th radial line and fall within the range from an Rmin'th picture element, that corresponds to the minimum value Rmin, as counted from the picture element of interest, to an n' th picture element, that falls within the range from the Rmin'th picture element to an Rmax'th picture element corresponding to the maximum value Rmax, as counted from the picture element of interest. Thereafter, the degree of centralization C of the gradient vector group is calculated with Formulas (43) and (44). Formula (43) represents the maximum value of the degree of centralization Ci(n) obtained for each of the radial lines with Formula (42) or (42'). Therefore, the region from the picture element of interest to the picture element associated with the degree of centralization Ci(n), which takes the maximum value, may be considered as being the region of the prospective tumor pattern. By the detection of such regions for all of the radial lines with Formula (43), it is possible to judge the shape of the peripheral edge of the region, which may be regarded as the prospective tumor pattern. With Formula (43), the maximum values of the degrees of centralization within the aforesaid regions are calculated for all directions of the radial lines. Thereafter, with Formula (44), the mean value of the maximum values of the degrees of centralization within the aforesaid regions, which maximum values have been given by Formula (43) for all directions of the radial lines, is calculated. The calculated mean value is compared with a predetermined threshold value T. From the results of the comparison, a judgment is made as to whether there is or is not a probability that the region having its center at the picture element of interest will be the abnormal pattern. The region, in which the degree of centralization C of the gradient vector group with Formula (44) is rated, is similar to the iris of the human's eye, which expands or contracts in accordance with the brightness of the external field. The size and the shape of the region is changed adaptively in accordance with the distribution of the gradient vectors. Therefore, the filter used is referred to as the iris filter. (Step 3) Rating of the shape and form of the tumor pattern In general, patterns of malignant tumors have the characteristics of the shapes and forms described below. 1) The side edges are irregular. 2) The shape is close to an ellipse. 3) The region inside of the pattern has a convex or concave density distribution. Therefore, a judgment is made as to the shape and form by considering these characteristics such that patterns of normal tissues may be eliminated from the detected prospective pattern, and such that only the pattern considered as being a tumor pattern, can be detected. The characteristic measures used in making the judgment include the spreadness, the elongation, the roughness of side edges, the circularity, and the degree of convexity or concavity (i.e., the entropy) of the density distribution in the region inside of the pattern. For example, the circularity may be employed as the characteristic measure for the shape judgment. In such cases, when the degrees of centralization are binarized, the distribution of the binarized degrees of centralization corresponding to the tumor pattern ordinarily takes a shape close to a circle. The diameter of the circle having the same area as the area of the region obtained from the binary conversion is represented by Le. Also, the lengths of the longitudinal side and the lateral side of a square, which has the minimum area capable of accommodating the region, are respectively represented by a and b. In such cases, the circularity d circ is defined by Formula (45). d circ =Le/(a+b) wherein Le=2(S/π) 1/2 In cases where the value of the circularity is smaller than a predetermined threshold value, it is judged that the region is not a tumor pattern, and the region is not detected as the tumor pattern. In cases where the value of the circularity is not smaller than the predetermined threshold value, it is judged that the region is a tumor pattern, and the region is detected as the tumor pattern. By carrying out the steps described above, the iris filter can efficiently detect a tumor pattern from a radiation image. Further, processing based upon the algorithm of morphology (hereinbelow referred to as the morphology operation or the morphology processing) has heretofore been known as the operation processing for selectively extracting only a specific image portion, such as an abnormal pattern, from an image. The morphology processing has been studied as a technique efficient for detecting, particularly, a small calcified pattern, which is one of characteristic forms of mammary cancers. However, the image to be processed with the morphology processing is not limited to the small calcified pattern in a mammogram, and the morphology processing is applicable to any kind of image, in which the size and the shape of a specific image portion (i.e., an abnormal pattern, or the like) to be detected are known previously. The morphology processing is carried out by using a multi-scale λ and a structure element (i.e., a mask) B. The morphology processing has the features in that, for example, (1) it is efficient for extracting a calcified pattern itself, (2) it is not affected by complicated background information, and (3) the extracted calcified pattern does not become distorted. Specifically, the morphology processing is advantageous over ordinary differentiation processing in that it can more accurately detect the geometrical information concerning the size, the shape, and the density distribution of the calcified pattern. How the morphology processing is carried out will be described hereinbelow by taking the detection of a small calcified pattern in a mammogram as an example. (Fundamental operation of morphology processing) In general, the morphology processing is expanded as the theory of sets in an N-dimensional space. As an aid in facilitating the intuitive understanding, the morphology processing will be described hereinbelow with reference to a two-dimensional gray level image. The gray level image is considered as a space, in which a point having coordinates (x, y) has a height corresponding to a density value f(x, y). In this case, it is assumed that the image signal representing the density value f(x, y) is a high luminance-high signal level type of image signal, in which a low density (i.e., a high luminance when the image is displayed on a CRT display device) is represented by a high image signal level. Firstly, as an aid in facilitating the explanation, a one-dimensional function f(x) corresponding to the cross section of the two-dimensional gray level image is considered. It is assumed that structure element g used in the morphology operation is a symmetric function of Formula (46), which is symmetric with respect to the origin. g s (X) = g (-X) It is also assumed that the value is 0 in a domain of definition G, which is represented by Formula (47). G = {-m, -m+1, ···, -1, 0, 1, ···, m-1, m} In such cases, the fundamental forms of the morphology operation are very simple operations carried out with Formulas (48), (49), (50), and (51). dilation; [f ⊕ G s ] (i) = max {f(i-m),···,f(i),···,f(i+m)} erosion; [f ⊖ G s ] (i) = min {f(i-m),···,f(i),···,f(i+m)} opening ; f g = (f ⊖ g s ) ⊕ g closing; f g = (f ⊕ g s ) ⊖ g Specifically, as illustrated in Figure 14A, the dilation processing is the processing for retrieving the maximum value in the range of a width of ±m (which is the value determined in accordance with a structure element B and corresponding to the mask size shown in Figure 14A) having its center at a picture element of interest. As illustrated in Figure 14B, the erosion processing is the processing for retrieving the minimum value in the range of the width of ±m having its center at the picture element of interest. The signal (indicated by the broken line in Figure 14A), which is obtained from the dilation processing, or the signal (indicated by the broken line in Figure 14B), which is obtained from the erosion processing, may then be subtracted from the original image signal. In this manner, as indicated by the portions hatched in Figure 14A or Figure 14B, it is possible to obtain a morphology signal Dmor having values other than zero only for an image edge portion, at which the signal value changes sharply, and an image portion, at which the signal values fluctuate within a range spatially smaller than the structure element. The opening processing is equivalent to the processing in which the dilation processing is carried out after the erosion processing, i.e., the processing in which the maximum value is searched after the searching of the minimum value. Also, the closing processing is equivalent to the processing in which the erosion processing is carried out after the dilation processing, i.e., the processing in which the minimum value is searched after the searching of the maximum value. More specifically, as illustrated in Figure 14C, the opening processing is equivalent to the processing for smoothing the density curve f(x) from the low luminance side, and removing a convex density fluctuating portion (i.e., the portion at which the luminance is higher than that of the surrounding portions), which fluctuates in a range spatially narrower than the mask size of 2m. Also, as illustrated in Figure 14D, the closing processing is equivalent to the processing for smoothing the density curve f(x) from the high luminance side, and removing a concave density fluctuating portion (i.e., the portion at which the luminance is lower than that of the surrounding portions), which fluctuates in the range spatially narrower than the mask size of 2m. The signal (indicated by the broken line in Figure 14C), which is obtained from the opening processing, or the signal (indicated by the broken line in Figure 14D), which is obtained from the closing processing, may then be subtracted from the original image signal. In this manner, as indicated by the portions hatched in Figure 14C or Figure 14D, it is possible to obtain a morphology signal Dmor having values other than zero only for a characteristic image portion, at which the signal values fluctuate within a range spatially smaller than the structure element. In cases where the structure element g is not symmetric with respect to the origin, the dilation operation with Formula (48) is referred to as the Minkowski sum, and the erosion operation with Formula (49) is referred to as the Minkowski difference. In cases where the image signal representing the density value f(x) is a high density-high signal level type of image signal, in which a high density is represented by a high image signal level, the relationship between the density value f(x) and the image signal value becomes reverse to the relationship between the density value f(x) and the image signal value in the high luminance-high image signal level type of image signal. Therefore, the dilation processing, which is carried out on the high density-high signal level type of image signal, coincides with the erosion processing, which is carried out on the high luminance-high signal level type of image signal as shown in Figure 14B. The erosion processing, which is carried out on the high density-high signal level type of image signal, coincides with the dilation processing, which is carried out on the high luminance-high signal level type of image signal as shown in Figure 14A. The opening processing, which is carried out on the high density-high signal level type of image signal, coincides with the closing processing, which is carried out on the high luminance-high signal level type of image signal as shown in Figure 14D. Also, the closing processing, which is carried out on the high density-high signal level type of image signal, coincides with the opening processing, which is carried out on the high luminance-high signal level type of image signal as shown in Figure 14C. The morphology processing is herein described with respect to the high luminance-high signal level type of image signal. (Application to detection of calcified patterns) In order for a calcified pattern to be detected, it is considered to employ a difference method, in which a smoothed image signal is subtracted from the original image signal. However, with a simple smoothing method, it is difficult to discriminate the calcified pattern from an elongated non-calcified pattern (for example, a pattern of the mammary gland, a blood vessel, mammary gland supporting tissues, or the like). Therefore, Obata of Tokyo University of Agriculture and Technology, et al. have proposed a morphology filter, which is represented by Formula (52) and is based upon the opening operation using a multiply structure element. [Reference should be made to "Extraction of Small Calcified Patterns with A Morphology Filter Using A Multiply Structure Element," Collected Papers of The Institute of Electronics and Communication Engineers of Japan, D-II, Vol. J75-D-II, No. 7, pp. 1170-1176, July 1992; and "Fundamentals of Morphology and Its Application to Mammogram Processing," Medical Imaging Technology, Vol. 12, No. 1, January 1994.] In Formula (52), Bi (wherein i=1, 2, ..., M) represents M number of linear structure elements (M=4 in the example shown in Figure 12). (The M number of structure elements, as a whole, will hereinbelow be referred to as the multiply structure element. Also, the multiply structure element will often be simply referred to as the structure element, including the cases where i=1.) In cases where the structure element Bi is set to be larger than the calcified pattern to be detected, a calcified pattern, which is a convex signal change portion finer than the structure element Bi (i.e., which is an image portion fluctuating in a spatially narrow range) and has luminance values larger than the luminance values of the surrounding portions, is removed in the opening processing. On the other hand, an elongated non-calcified pattern is longer than the structure element Bi. Therefore, in cases where the inclination of the non-calcified pattern (i.e, the direction along which the non-calcified pattern extends) coincides with one of the directions of the four structure elements Bi, the non-calcified pattern remains unremoved after the opening processing, i.e. the operation of the second term of Formula (52), has been carried out. Therefore, when the smoothed image signal obtained from the opening processing (i.e. the signal representing the image, from which only the calcified pattern has been removed) is subtracted from the original image signal f, an image can be obtained which contains only the small calcified pattern. This is the concept behind Formula (52). As described above, in cases where the image signal is of the high density-high signal level type, the density value of the calcified pattern is smaller than the density values of the surrounding image portions, and the calcified pattern constitutes a concave signal change portion with respect to the surrounding portions. Therefore, the closing processing is applied in lieu of the opening processing, and Formula (53) is applied in lieu of Formula (52). As described above, in order that a visible image having good image quality can be reproduced and used as an effective tool in, particularly, the accurate and efficient diagnosis of an illness, it is essential to carry out the image processing on the given image. However, in cases where the image processing is carried out for the entire area of the image as in the conventional techniques or in cases where the emphasis processing merely depending on the image density is carried out as disclosed in, for example, Japanese Unexamined Patent Publication No. 2(1990)-1078, components adversely affecting the image quality, such as radiation noise components in a mammogram, are also emphasized. As a result, the image quality of the image and its capability of serving as an effective tool in, particularly, the efficient and accurate diagnosis of an illness become low. Also, as disclosed in, for example, Japanese Patent Publication No. 60(1985)-192482, Japanese Unexamined Patent Publication No. 2(1990)-120985, and Japanese Patent Application Publication No. 3(1991)-502975, in cases where emphasis processing depending upon the value of variance of an image signal is carried out, an image portion having a locally large change in density is emphasized to a high extent. Therefore, the problems occur in that undershooting and overshooting become relatively perceptible in the vicinity of the image portion. Particularly, as for X-ray images, an artifact is apt to occur on the high density side. Systems and Computers in Japan, vol. 24, no. 11, 1993, USA, pages 66-74, XP000447455 Jin H R & Kobatake H: "Extraction of Microcalcifications from Mammograms using Morphological Filter with Multiple Structuring Elements" discloses a morphologic operation being carried out with regard to an image signal. This document also discloses that the morphology signal represents whether a pixel of the image corresponds to an image portion at which an original image signal meets a spatial condition. The document does not disclose to carry out image emphasis processing in accordance with the morphology signal. Computers and Biomedical Research, vol. 25, no. 3, June 1992, USA, pages 218-237, XP000574508 Shun Leung Ng & Bischof W F: "Automated Detection and Classification of Breast Tumors" discloses to carry out a filter operation on an input image. Gradients and the degree of centralization of the gradients are calculated. Regions with a high degree of centralization are identified. US-A-4 317 179 (Kato et al.) discloses emphasizing desired frequency components using unsharp mask signals. This document does not disclose to use signals other than unsharp mask signals. According to this document, only the desired frequency component is emphasized. According to this document, no specific information regarding the shape of the component to be emphasized is available. SUMMARY OF THE INVENTION The primary object of the present invention is to provide an image processing method, wherein components unnecessary for a diagnosis, or the like, such as noise components, are not emphasized, and only a specific image portion of interest is emphasized efficiently. Another object of the present invention is to provide an image processing method, wherein components unnecessary for a diagnosis, or the like, such as noise components, are not emphasized, only a specific image portion of interest is emphasized efficiently, and the occurrence of an artifact is restricted. In the first image processing method in accordance with the present invention, the image emphasis processing in accordance with the morphology signal Dmor may be carried out with Formula (2). Dproc=Dorg+α • f (Dmor) wherein α represents the emphasis coefficient, and f(Dmor) represents the function of Dmor. As illustrated in Figure 13, the function f(Dmor) should preferably be set to convert such that the output f(Dmor) may be kept to be equal to 0 (zero) with respect to a region C1, in which the value of the morphology signal |Dmor| is small, such that the output f(Dmor) may be monotonously increased with respect to |Dmor| for a region C2, in which the value of the morphology signal |Dmor| is comparatively large, and such that the output f(Dmor) may be kept to be equal to the upper limit value with respect to a region C3, in which the value of the morphology signal |Dmor| is very large. In such cases, in the region C1, in which the value of Dmor is small, high-frequency radiation noise detected by the morphology filter can be reduced. Also, in the region C3 having a certain extent of contrast, excessive emphasis processing can be prevented from being carried out. In this manner, the image quality of a reproduced visible image and its capability of serving as an effective tool in, particularly, the efficient and accurate diagnosis of an illness can be kept high. Also, as the structure element B which constitutes the multiply structure element Bi, by way of example, a bisymmetric element having a form of a square, a rectangle, a circle, an ellipse, a rhombus, or the like, is preferable. This is because the image signal is actually distributed in two-dimensional directions. Further, as the function f(Dmor) in accordance with the morphology signal Dmor, as represented by Formula (3), the morphology signal Dmor itself may be employed. f (Dmor) =Dmor However, as represented by Formula (4), an unsharp mask signal corresponding to super-low frequency of the morphology signal Dmor should preferably be utilized as the function f(Dmor). f(Dmor) = (∑ ∑Dmor)/N 2 wherein ∑ ∑ Dmor represents the sum of the morphology signal values with respect to the picture elements located within a picture element matrix, which has a size of N columns × N rows (where N represents an odd number) and has its center at the picture element, that is associated with the morphology signal Dmor, in a two-dimensional array of picture elements. Specifically, as described above, the value of the morphology signal Dmor sharply becomes large when the signal change portion in a spatially narrower range than the size of the structure element B is detected selectively. Therefore, the morphology signal Dmor takes on the form of a pulsed signal. If the pulsed signal itself is multiplied by the emphasis coefficient α, in cases where the degree of emphasis is high, i.e. in cases where the value of the emphasis coefficient α is large, there will be the risk that the signal value changes sharply at an image area, which connects the signal change portion in a spatially narrower range than the size of the structure element B and the other portions with each other, and that a processed image signal Dproc is obtained which represents an unnatural image. For example, as illustrated in Figure 19, in cases where the processed image signal Dproc is obtained by carrying out the calculation with Formula (3) on the original image signal Dorg and the morphology signal Dmor, the value of the processed image signal Dproc changes sharply at the image area, which connects the signal change portion in a spatially narrower range than the size of the structure element B and the other portions with each other. Therefore, there often occurs the risk that an artifact occurs at the connecting image area. However, in cases where the function of Formula (4) is employed as the function f(Dmor) in accordance with the morphology signal Dmor, the morphology signal Dmor is converted to a signal representing the arithmetic mean value of the morphology signal values with respect to the picture elements located within a picture element matrix, which has a size of N columns × N rows and has its center at the picture element, that is associated with the morphology signal Dmor. Therefore, the pulsed characteristics of the morphology signal Dmor become unsharp. As a result, a processed image signal Dproc in accordance with the function f(Dmor) can be obtained such that it may represent an image, in which the occurrence of an artifact has been reduced and which gives a natural feeling. Also, as the image emphasis processing in accordance with the morphology signal Dmor, in lieu of the processing carried out with Formula (2), the processing carried out with Formula (5) or Formula (6) may be employed. Dproc=Dorg+ β (Dorg) × (Dorg-Dmor) wherein β(Dorg) represents the function of Dorg. Dproc=Dorg+ β (Dmor) × (Dorg-Dmor) wherein β(Dmor) represents the function of Dmor. In cases where the image emphasis processing in accordance with the morphology signal Dmor is carried out with Formula (5), the function β(Dorg) should preferably be set in the manner described below. (i) In cases where the image emphasis processing is to be carried out for an image portion (for example, a calcified pattern represented by the high luminance-high signal level type of image signal), in which the value of the original image signal Dorg is larger than the image signal values representing the surrounding image areas (i.e., the image portion is convex) and at which the image signal fluctuates in a spatially narrower range than the multiply structure element Bi, as illustrated in Figure 15A, the function β(Dorg) should preferably be set to be a function monotonously increasing with respect to Dorg. Also, (ii) in cases where the image emphasis processing is to be carried out for an image portion (for example, a calcified pattern represented by the high density-high signal level type of image signal), in which the value of the original image signal Dorg is smaller than the image signal values representing the surrounding image areas (i.e., the image portion is concave) and at which the image signal fluctuates in a spatially narrower range than the multiply structure element Bi, as illustrated in Figure 15B, the function β(Dorg) should preferably be set to be a function monotonously decreasing with respect to Dorg. As illustrated in Figure 16, in cases where the image emphasis processing in accordance with the morphology signal Dmor is carried out with Formula (6), the function β(Dmor) should preferably be set to be a function monotonously increasing with respect to Dmor. The aforesaid morphology operation may be carried out with Formula (7) shown below. In such cases, an image portion, at which the original image signal Dorg fluctuates in a spatially narrower range than the multiply structure element Bi and in which the value of the original image signal Dorg is larger than the image signal values representing the surrounding image areas, can be extracted, and the extracted image portion can be selectively subjected to the image emphasis processing. wherein the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X, and the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X. Alternatively, the aforesaid morphology operation may be carried out with Formula (8) shown below. In such cases, an image portion, at which the original image signal Dorg fluctuates in a spatially narrower range than the multiply structure element Bi and in which the value of the original image signal Dorg is smaller than the image signal values representing the surrounding image areas, can be extracted, and the extracted image portion can be selectively subjected to the image emphasis processing. wherein the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X, and the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X. As another alternative, the aforesaid morphology operation may be carried out with Formula (7') shown below. In such cases, an image portion, at which the original image signal Dorg fluctuates in a spatially narrower range than the multiply structure element Bi and in which the value of the original image signal Dorg is larger than the image signal values representing the surrounding image areas, can be extracted, and the extracted image portion can be selectively subjected to the image emphasis processing. wherein the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X. As a further alternative, the aforesaid morphology operation may be carried out with Formula (8') shown below. In such cases, an image portion, at which the original image signal Dorg fluctuates in a spatially narrower range than the multiply structure element Bi and in which the value of the original image signal Dorg is. smaller than the image signal values representing the surrounding image areas, can be extracted, and the extracted image portion can be selectively subjected to the image emphasis processing. wherein the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X. The present invention still further provides a second image processing method comprising the steps of: i) carrying out a morphology operation on an original image signal Dorg, which represents an image, by using a multiply structure element Bi and a scale factor λ, a morphology signal Dmor being thereby obtained, the morphology signal Dmor representing whether each of picture elements constituting the image is or is not the one corresponding to an image portion, at which the original image signal Dorg fluctuates in a spatially narrower range than the multiply structure element Bi, ii) calculating an unsharp mask signal Dus, which corresponds to super-low frequency, from the original image signal Dorg, and iii) carrying out an operation with Formula (9) Dproc=Dorg+ β(Dmor) ×f (Dorg-Dus) on the original image signal Dorg by using a function β (Dmor), which is in accordance with the morphology signal Dmor, and a function f(Dorg-Dus), which is in accordance with a difference signal (Dorg-Dus) between the original image signal Dorg and the unsharp mask signal Dus, whereby image emphasis processing is selectively carried out on the image portion. As illustrated in Figure 22A or Figure 22B, the function β(Dmor) is a function, wherein the output is restricted to a small value with respect to the region, in which the value of |Dmor| is mall. Also, in the second image processing method in accordance with the present invention, as the structure element B which constitutes the multiply structure element Bi, by way of example, a bisymmetric element having a form of a square, a rectangle, a circle, an ellipse, a rhombus, or the like, is preferable. Further, in the second image processing method in accordance with the present invention, as the morphology operation, one of various operations carried out with Formulas (7), (8), and (10) to (14) shown below may be employed. wherein the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X, and the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X. wherein the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X, and the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X. wherein the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X. wherein the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X. wherein the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X, the expression (X ⊖ λY) Y represents that the opening operation with the structure element Y is carried out on the image signal (X ⊖ λY), and represents the sum of sets of λ=0, 1, ..., N. wherein the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X, the expression (X ⊕ λY) Y represents that the closing operation with the structure element Y is carried out on the image signal (X + λY), and represents the sum of sets of λ =0, 1, ..., N. wherein the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X, and the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X. Specifically, by the application of the morphology operation carried out with Formula (7), it is possible to extract, as the morphology signal Dmor, a signal representing the picture elements constituting an image portion, in which the value of the original image signal Dorg is larger than the image signal values representing the surrounding image areas, and at which the image signal fluctuates in a spatially narrower range than the multiply structure element Bi (for example, the calcified pattern in cases where the image signal is of the high luminance-high signal level type). Therefore, the extracted image portion can be efficiently processed with the image emphasis processing. Also, by the application of the morphology operation carried out with Formula (8), it is possible to extract, as the morphology signal Dmor, a signal representing the picture elements constituting an image portion, in which the value of the original image signal Dorg is smaller than the image signal values representing the surrounding image areas, and at which the image signal fluctuates in a spatially narrower range than the multiply structure element Bi (for example, the calcified pattern in cases where the image signal is of the high density-high signal level type). Therefore, the extracted image portion can be efficiently processed with the emphasis processing. Further, by the application of the morphology operation carried out with Formula (10), it is possible to extract, as the morphology signal Dmor, a signal representing the picture elements constituting an image portion, in which the value of the original image signal Dorg is larger than the image signal values representing the surrounding image areas, and at which the image signal fluctuates in a spatially narrower range than the multiply structure element Bi, and an image edge portion at which the luminance (or the density) changes sharply. Therefore, the extracted image portion can be efficiently processed with the emphasis processing. By the application of the morphology operation carried out with Formula (11), it is possible to extract, as the morphology signal Dmor, a signal representing the picture elements constituting an image portion, in which the value of the original image signal Dorg is smaller than the image signal values representing the surrounding image areas, and at which the image signal fluctuates in a spatially narrower range than the multiply structure element Bi, and an image edge portion at which the luminance (or the density) changes sharply. Therefore, the extracted image portion can be efficiently processed with the emphasis processing. By the application of the morphology operation carried out with Formula (12), it is possible to extract, as the morphology signal Dmor, a signal representing the picture elements constituting an image portion, in which the value of the original image signal Dorg is larger than the image signal values representing the surrounding image areas, and at which the image signal fluctuates in a spatially narrower range than the multiply structure element Bi and a change in the density (or a change in the luminance) is large (for example, a skeleton pattern in the image represented by the original image signal Dorg). Therefore, the extracted image portion (for example, the skeleton pattern) can be efficiently processed with the emphasis processing. Figure 23 shows an example of the skeleton processing carried out with Formula (12). As illustrated in Figure 23, a calculation is made to find a difference signal between a signal representing an image, which has been obtained by carrying out the erosion processing on an original image X with a structure element B (in this case, a circular structure having a radius of r), and a signal representing an image, which has been obtained by carrying out the opening processing on the image obtained from the erosion processing. The sum of sets of the difference signals obtained from the λ number of operations (wherein λ=1, 2, ..., N) represents a skeleton patterns a and b. By the application of the morphology operation carried out with Formula (13), it is possible to extract, as the morphology signal Dmor, a signal representing the picture elements constituting an image portion, in which the value of the original image signal Dorg is smaller than the image signal values representing the surrounding image areas, and at which the image signal fluctuates in a spatially narrower range than the multiply structure element Bi and a change in the density (or a change in the luminance) is large (for example, a skeleton pattern in the image represented by the original image signal Dorg). Therefore, the extracted image portion (for example, the skeleton pattern) can be efficiently processed with the emphasis processing. The morphology operation carried out with Formula (12) or (13) is ordinarily referred to as the skeleton processing. With the skeleton processing, particularly in cases where it is applied to an image signal representing a bone trabecula pattern in an image, only the skeleton element can be selectively and efficiently emphasized. In cases where the morphology operation is carried out with one of Formulas (7), (8), and (10) to (13), the function f(Dorg-Dus) may be set such that f(Dorg-Dus)=Dorg-Dus. By the application of the morphology operation carried out with Formula (14), it is possible to extract, as the morphology signal Dmor, a signal representing the picture elements constituting an image portion, in which a local change in the luminance (or a change in the density) is large. Therefore, the extracted image portion can be efficiently processed with the emphasis processing. In cases where the morphology operation is carried out with Formula (14), in order for the undershooting and overshooting to be restricted, the function f(Dorg-Dus) should be set to be, for example, a function as shown in Figure 24, which restricts the output with respect to the range of the Dorg values not larger than a predetermined value or with respect to the range of the Dorg values not smaller than a predetermined value. With the first image processing method in accordance with the present invention, wherein the morphology operation is carried out on the original image signal Dorg, which represents an image, by using the multiply structure element Bi and the scale factor λ, only the image signal representing the specific image portion, at which the original image signal Dorg fluctuates in a spatially narrower range than the multiply structure element Bi, can be extracted from the original image signal Dorg. In accordance with the morphology signal Dmor obtained from the morphology operation, the image emphasis processing is carried out on the original image signal Dorg. Therefore, even if unnecessary components, such as high frequency noise, have been superposed upon the original image signal Dorg, only the image portion, which is represented by the extracted image signal, can be emphasized selectively. Specifically, the morphology signal Dmor takes a large value with respect to the picture element constituting the specific image portion, at which the image signal fluctuates in a spatially narrower range than the multiply structure element Bi. Also, the morphology signal Dmor takes a small value or a value of zero with respect to picture elements other than the picture element constituting the specific image portion. Therefore, by the carrying out of the image emphasis processing in accordance with the morphology signal Dmor, the degree of emphasis becomes high with respect to the specific image portion and becomes low with respect to the image portions other than the specific image portion. In the first image processing method in accordance with the present invention, as the function f(Dmor) in accordance with the morphology signal Dmor, as represented by Formula (4), the unsharp mask signal corresponding to the super-low frequency of the morphology signal Dmor may be utilized. In such cases, as illustrated in Figure 20A, the morphology signal Dmor, which has sharp pulsed characteristics, is converted to a signal representing the arithmetic mean value of the morphology signal values with respect to the picture elements surrounding the picture element, that is associated with the morphology signal Dmor. Therefore, the pulsed characteristics of the morphology signal Dmor become unsharp. As a result, a processed image signal Dproc in accordance with the function f(Dmor) can be obtained such that it may represent an image, in which the occurrence of an artifact at an image area (i.e., the boundary area) connecting the specific image portion (at which the image signal fluctuates in a spatially narrower range than the size of the multiply structure element Bi) and the other portions with each other, has been reduced and which gives a natural feeling. An example of the morphology operation, which is carried out with Formula (8), will hereinbelow be described in detail. As an aid in facilitating the explanation, the scale factor λ and the number of the structure elements constituting the multiply structure element Bi are set such that λ=1 and i=1. Specifically, the morphology operation is carried out on the density value Dorg, which is represented by the high density-high signal level type of image signal. With the morphology operation, the operation for finding the Minkowski sum (Dorg ⊕ B) is carried out on the image signal, which has a distribution of the density value Dorg indicated by, for example, the solid line in Figure 17A, by using a linear structure element B, which is constituted of three picture elements and is shown in Figure 17B. As a result, a density value D i of a certain picture element of interest is converted into D i ', which takes the maximum value D i+1 of the values of the three adjacent picture elements (determined by the structure element B) having their center at the picture element of interest. The operation is carried out for all of the picture elements constituting the image, each of them being taken as the picture element of interest. In this manner, the image signal having the distribution of the density value Dorg indicated by the solid line in Figure 17A is converted into the image signal having the distribution indicated by the broken line in Figure 17A. Thereafter, the operation for finding the Minkowski difference {(Dorg ⊕ B) ⊖ B} is carried out on the density value (Dorg ⊕ B), which has been obtained from the operation for finding the Minkowski sum, by using the structure element B. As a result, the density value Di' of the picture element of interest indicated by the broken line in Figure 17A is converted into D i " (=D i ), which takes the minimum value D i-1 ' of the values of the three adjacent picture elements having their center at the picture element of interest. The operation is carried out for all of the picture elements constituting the image, each of them being taken as the picture element of interest. In this manner, the image signal having the distribution indicated by the broken line in Figure 17A is converted into the image signal having the distribution of the density value Dorg" indicated by the chained line in Figure 17A. The image signal indicated by the chained line in Figure 17A has the distribution such that the image portion corresponding to the signal change portion, at which the original image signal Dorg fluctuates in a spatially narrower range than the size of the structure element B, has been eliminated, and such that the image portion corresponding to the signal change portion, at which the original image signal Dorg fluctuates in a spatially wider range than the size of the structure element B, and the image portion, at which the original image signal Dorg does not fluctuates, are kept in the original forms. More specifically, the aforesaid processing (i.e., the closing operation) serves as the processing for smoothing the image density distribution from the high density side. The value {(Dorg ⊕ B) ⊖ B} is thus obtained from the closing operation, in which the operation for finding the Minkowski sum is carried out on the original image signal Dorg and the operation for finding the Minkowski difference is then carried out. Thereafter, the obtained value {(Dorg ⊕ B) ⊖ B} is subtracted from the original image signal Dorg, and a value Dmor is thereby obtained. The thus obtained value Dmor represents the image portion corresponding to the signal change portion, at which the signal value fluctuates in a spatially narrower range than the size of the structure element B and which has been eliminated by the aforesaid closing operation. Fundamentally, an image signal represents spatial coordinates (x, y), which constitute a two-dimensional element, and a signal value f(x, y), which constitutes a third dimensional element. However, in the foregoing, as an aid in facilitating the understanding, the morphology operation is described with respect to the one-dimensional image signal distribution curve, which appears in a predetermined cross section of the image expanded in the two-dimensional plane. Therefore, actually, in the first image processing method in accordance with the present invention and those that follow, the foregoing explanation is extended and applied to a two-dimensional image. Also, the term "multiply structure element Bi" as used herein means a set of the i number of structure elements B, which are prepared as the structure elements B extending in different directions in the two-dimensional plane in cases where the morphology operation in the cross section of the image is extended and applied to the two-dimensional plane. As a result of the closing operation carried out with all of the structure elements B constituting the multiply structure element Bi, an image portion may be found, which extends in the same direction as the direction of a structure element B and at which the signal value fluctuates in a spatially wider range than the size of the structure element B. As for such an image portion, the value of the second term [min{(Dorg ⊕ λBi) ⊖ λBi}] of Formula (8) becomes equal to Dorg, and therefore the value of Dmor becomes equal to zero. Accordingly, the image portion is not emphasized. The scale factor λ represents the number of the operations for finding the Minkowski sum and the operations for finding the Minkowski difference. As the number of the operations becomes large, the extent of smoothing becomes higher. (Ordinarily, it is sufficient for the scale factor λ to be equal to 1.) Figure 18 shows the results of the comparison of the image emphasis processing, which utilizes the morphology operation and is carried out with the first image processing method in accordance with the present invention, and the image emphasis processing, which utilizes the conventional unsharp mask processing. In cases where the morphology operation is carried out with Formula (7) or Formula (7'), an image portion, in which the value of the original image signal Dorg is larger than the image signal values representing the surrounding image areas, can be extracted, and the extracted image portion can be selectively subjected to the image emphasis processing. In cases where the morphology operation is carried out with Formula (8) or Formula (8'), an image portion, in which the value of the original image signal Dorg is smaller than the image signal values representing the surrounding image areas, can be extracted, and the extracted image portion can be selectively subjected to the image emphasis processing. Particularly, in cases where the operation is carried out with Formula (7') [or with Formula (8')], as illustrated in Figure 20B, the entire area of the image portion can be emphasized. Or, as illustrated in Figure 20C, the edges of the concave part of the image portion [as for Formula (8'), the edge of the convex part of the image portion] can be emphasized. With the second image processing method in accordance with the present invention, the morphology operation is carried out on the original image signal Dorg, which represents an image, by using the multiply structure element Bi and the scale factor λ, and the morphology signal Dmor is thereby obtained. The morphology signal Dmor represents whether each of picture elements constituting the image is or is not the one corresponding to the image portion, at which the original image signal Dorg fluctuates in a spatially narrower range than the multiply structure element Bi. Further, by the calculation of the second term of Formula (9), the super-low frequency components Dus of the original image signal Dorg are subtracted from the original image signal Dorg. Therefore, comparatively high frequency components (i.e., the components other than the super-low frequency components) can be extracted selectively from the original image signal Dorg. The comparatively high frequency components having thus been extracted also contain radiation noise, which is composed of high frequency components. However, as represented by Formula (9), the comparatively high frequency components (Dorg-Dus) are emphasized with the emphasis coefficient β (Dmor). The emphasis coefficient β(Dmor) is in accordance with the morphology signal Dmor representing whether each of picture elements constituting the image is or is not the one constituting the specific image portion (such as the calcified pattern), at which the original image signal Dorg fluctuates in a spatially narrower range than the multiply structure element Bi and which is obtained from the morphology operation. Therefore, even if unnecessary components, such as quantum noise, are contained in the high frequency components (Dorg-Dus), in cases where the picture element is not the one constituting the image portion, such as the calcified pattern, the value of β (Dmor) with respect to the picture element will be small, and the degree of emphasis with respect to the picture element will be kept low. In cases where the picture element is the one constituting the image portion, such as the calcified pattern, the value of β(Dmor) with respect to the picture element is large, and therefore the degree of emphasis with respect to the picture element is kept high. Therefore, regardless of whether radiation noise is or is not contained in the high frequency components (Dorg-Dus) of the image, the specific image portion, such as the calcified pattern, can be selectively emphasized with the function β(Dmor), which has a value in accordance with whether the image area is or is not the specific image portion, such as the calcified pattern. Furthermore, with the second image processing method in accordance with the present invention, the specific image portion is emphasized regardless of the magnitude of a density change. Therefore, undershooting and overshooting can be restricted, and the occurrence of an artifact can be reduced. Moreover, in lieu of the processing for calculating the variance value of the image signal, the processing for calculating the maximum value or the minimum value is carried out. Therefore, the operation time required to carry out the calculation processing can be kept short. Figure 25 shows the results of the comparison of the image emphasis processing, which utilizes the morphology operation and is carried out with the fourth image processing method in accordance with the present invention, and the image emphasis processing, which utilizes the conventional unsharp mask processing. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a block diagram showing an apparatus for computer aided diagnosis of medical images, in which an image processing method is employed, that will be discussed so as to facilitate the understanding of the invention; Figure 2A is an explanatory view showing a radiation image of the mamma (i.e., a mammogram), which is subjected to diagnosis carried out with the apparatus for computer aided diagnosis of medical images shown in Figure 1, Figure 2B is an explanatory view showing the degree of centralization of gradient vectors in a tumor pattern, Figure 2C is an explanatory view showing the degree of centralization of gradient vectors in a blood vessel pattern, or the like, Figure 3 is a graph showing the distribution of density values in a cross section of the mammogram taken along line I-I of Figure 2, Figure 4 is a graph showing a function representing a conversion table, which is incorporated in a local area limited image emphasizing means 70, Figure 5 is an explanatory view showing a mask, which is used for calculating directions of gradient vectors in an iris filter, Figure 6 is an explanatory view showing the concept behind the degree of centralization of a gradient vector with respect to a picture element of interest, Figure 7 is an explanatory view showing the picture elements, which are located along a plurality of radial lines extending radially from a picture element of interest and for which the degree of centralization of a gradient vector is rated, Figure 8 is a block diagram showing an apparatus for compute aided diagnosis of medical images, in which a further image processing method is employed, Figure 9 is a graph showing a function representing a conversion table, Figure 10 is a block diagram showing an apparatus for computer aided diagnosis of medical images, in which a first embodiment of the image processing method in accordance with the present invention is employed, Figure 11A is an explanatory view showing a radiation image of the mamma (i.e., a mammogram), which is subjected to diagnosis carried out with the apparatus for computer aided diagnosis of medical images shown in Figure 10, Figure 11B is a graph showing the distribution of density values of the mammogram in the cross section taken along line I-I of Figure 11A, Figure 11C is an enlarged view showing a portion of the distribution shown in Figure 11B, Figure 12 is an explanatory view showing four linear structure elements, which constitute a multiply structure element employed in a morphology filter, Figure 13 is a graph showing a function representing a conversion table, which is incorporated in a local area limited image emphasizing means 270, Figure 14A is a graph showing how a dilation processing, which is one of fundamental morphology operations, is carried out, Figure 14B is a graph showing how an erosion processing, which is one of fundamental morphology operations, is carried out. Figure 14C is a graph showing how an opening processing, which is one of fundamental morphology operations, is carried out, Figure 14D is a graph showing how a closing processing, which is one of fundamental morphology operations, is carried out, Figures 15A and 15B are graphs showing emphasis functions β(Dorg) in accordance with an original image signal Dorg, Figure 16 is a graph showing an emphasis function β(Dmor) in accordance with a morphology signal Dmor, Figure 17A is a graph showing a distribution of density values, the graph serving as an aid in explaining how the processing with morphology operation is carried out, Figure 17B is an explanatory view showing a linear structure element B, which is constituted of three picture elements, Figure 18 is an explanatory view showing the results of comparison of image emphasis processing, which is carried out with the third embodiment of the image processing method in accordance with the present invention, and image emphasis processing, which utilizes conventional unsharp mask processing, Figure 19 is an explanatory view showing how a signal value changes in cases where image emphasis processing is carried out merely in accordance with a morphology signal Dmor, Figure 20A is an explanatory view showing how a processed image signal Dproc is obtained in cases where image emphasis processing is carried out in accordance with an unsharp mask signal calculated from a morphology signal Dmor, Figure 20B is an explanatory view showing how the entire area of an image portion, at which the signal value fluctuates in a spatially very narrow range, is emphasized, Figure 20C is an explanatory view showing how the edges of a concave image portion, at which the signal value fluctuates in a spatially comparatively wide range, are emphasized, Figure 21 is a block diagram showing an apparatus for computer aided diagnosis of medical images, in which a second embodiment of the image processing method in accordance with the present invention is employed, Figures 22A and 22B are graphs showing functions representing conversion tables, Figure 23 is an explanatory view showing how a skeleton processing is carried out, Figure 24 is a graph showing a function f(Dorg-Dus), which restricts overshooting and undershooting, and Figure 25 is an explanatory view showing the results of comparison of image emphasis processing, which is carried out with the fourth embodiment of the image processing method in accordance with the present invention, and image emphasis processing, which utilizes conventional unsharp mask processing. DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will hereinbelow be described in further detail with reference to the accompanying drawings. Figure 1 is a block diagram showing an apparatus for computer aided diagnosis of medical images, in which an image processing method in is employed. Figure 2A is an explanatory view showing a radiation image of the mamma (i.e., a mammogram), which is subjected to diagnosis carried out with the apparatus for computer aided diagnosis of medical images shown in Figure 1. The apparatus for computer aided diagnosis of medical images comprises an entire area image memory 10 for storing an image signal (i.e., an entire area image signal) S, which represents an entire radiation image (i.e., an entire area image) P of the mamma serving as an object and is made up of a series of density values Dorg of picture elements constituting the image, and an entire area image displaying means 30, which may be constituted of a CRT display device, or the like, and which displays the entire area image P of the object in accordance with the entire area image signal S received directly from the exterior or having been stored in the entire area image memory 10. The apparatus for computer aided diagnosis of medical images also comprises an abnormal pattern detecting means 40 for detecting an abnormal pattern P 1 in the entire area image P in accordance with the entire area image signal S, which has been stored in the entire area image memory 10, and a judgment means 50 for making a judgment as to whether the abnormal pattern P 1 has been or has not been detected by the abnormal pattern detecting means 40. The apparatus for computer aided diagnosis of medical images further comprises a local area extracting means 60 which, in cases where the judgment means 50 has judged that the abnormal pattern P 1 has been detected, extracts an image signal (i.e., a local area limited image signal) S 2 representing a local area limited image P 2 containing the abnormal pattern P 1 from the entire area image signal S having been stored in the entire area image memory 10. The apparatus for computer aided diagnosis of medical images still further comprises a local area limited image emphasizing means 70 for carrying out image emphasis processing on an image signal representing the abnormal pattern (i.e., an abnormal pattern image signal) S 1 such that the abnormal pattern P 1 in the local area limited image P 2 , which is represented by the local area limited image signal S 2 having been extracted by the local area extracting means 60, may have better image quality and higher capability of serving as an effective tool in, particularly, the efficient and accurate diagnosis of an illness than the entire area image P, which is displayed on the entire area image displaying means 30. The apparatus for computer aided diagnosis of medical images also comprises a local area limited image displaying means 90, which may be constituted of a CRT display device, or the like, and which displays the local area limited image P 2 in accordance with the local area limited image signal S 2 having been obtained from the image emphasis processing. A tumor pattern is taken as the abnormal pattern. Also, an iris filter, which carries out an operation of iris filter, is employed as the abnormal pattern detecting means 40. However, in the image processing method in accordance with the present invention, the image to be processed is not limited to the medical image as in this embodiment and those that follow, and may be an image for inspection of an industrial product, or the like. For example, as for X-ray images of castings having a blow-hole therein, the abnormal pattern may be the pattern of the blow-hole. Also, the term "local area" as used in the first embodiment means the region, which is located in the vicinity of the tumor pattern taken as the abnormal pattern and contains the tumor pattern. The image signal value representing each of the picture element constituting the image is represented by a density signal value Dorg. Also, the image signal, which represents the region constituted of the set of the picture elements, is represented by the image signal S. The density value Dorg is the high density-high signal level type of signal value. Further, in this embodiment, the tumor pattern has the characteristics such that the density value Dorg may become smaller towards the center point of the pattern. As described above, the processing with the iris filter 40 is carried out with the detection processing algorithm for detecting a specific image portion. However, in this embodiment, the iris filter 40 does not indicate the algorithm itself and indicates the means for carrying out the processing for detecting the tumor pattern with the algorithm. The local area limited image emphasizing means 70 is provided with a conversion table for converting a variation Diris of the density value, which represents the tumor pattern extracted by the iris filter 40, into an output g(Diris). Specifically, as illustrated in Figure 4, as for a region C1, in which the variation Diris is very small, and a region C3, in which the variation Diris is larger than a predetermined value, the conversion table converts the variation Diris such that the output g(Diris) may be restricted with respect to the variation Diris. Also, as for an intermediate region C2 between the region C1 and the region C3, the conversion table converts the variation Diris such that the output g(Diris) may be in proportion to the variation Diris. The output g(Diris) obtained from the conversion table is then multiplied by a predetermined emphasis coefficient α, and the resulting product is added to the original density value Dorg of the local area in the vicinity of the tumor pattern. In this manner, of the local area limited image P 2 , only the tumor pattern P 1 is selectively emphasized. The calculation is carried out with Formula (55'). Dproc=Diris+ α • g(Diris) wherein g(x) represents a function of x. How the apparatus for computer aided diagnosis of medical images, in which the first embodiment of the image processing method in accordance with the present invention is employed, operates will be described hereinbelow. The entire area image signal S, which represents the entire area image P of the mamma having the tumor therein and serving as the object, is fed from an external storage medium, such as a magneto-optical disk, an image read-out apparatus, or the like, into the entire area image memory 10. Also, the entire area image signal S is fed directly from the exterior into the entire area image displaying means 30 (along a line A shown in Figure 1). Alternatively, the entire area image signal S having been stored in the entire area image memory 10 may be fed from the entire area image memory 10 into the entire area image displaying means 30 (along a line B shown in Figure 1). The entire area image displaying means 30 displays the entire area image P in accordance with the entire area image signal S. The entire area image signal S having been stored in the entire area image memory 10 is also fed into the iris filter 40. In accordance with the procedure described above, with respect to the received entire area image signal S, the iris filter 40 rates the degree of centralization of the gradient vector in accordance with the density value Dorg. The iris filter 40 thereby detects the image signal (hereinbelow referred to as the tumor pattern image signal) S 1 , which represents the tumor pattern P 1 . Specifically, the density value Dorg of the tumor pattern P 1 in the mammogram, which is shown in Figure 2A, becomes smaller towards the center point of the pattern. Therefore, as illustrated in Figure 2B, the direction of the gradient vector represented by Formula (38) centralizes upon the center point of the pattern. On the other hand, as for an image P 3 of a blood vessel, the mammary gland, or the like, the density value Dorg becomes smaller towards the center line of the pattern. Therefore, as illustrated in Figure 2C, the direction of the gradient vector represented by Formula (38) is directed in the same direction and does not centralize upon a single point as in the tumor pattern shown in Figure 2B. The iris filter 40 rates the degree of centralization of the gradient vectors and rates the shape in Step 3 described above. In this manner, the iris filter 40 specifies the picture element (and its position), which corresponds to the image signal S 1 representing the tumor pattern P 1 . The judgment means 50 judges that the tumor pattern image signal S 1 representing the tumor pattern P 1 has been detected by the iris filter 40. Also, the judgment means 50 feeds a position signal (hereinbelow referred to as the tumor picture element position signal) D 1 , which specifies the position of the picture element represented by the tumor pattern image signal S 1 , into the local area extracting means 60. In cases where it has been judged that the tumor pattern image signal S 1 representing the tumor pattern P 1 has not been detected by the iris filter 40, the tumor picture element position signal D 1 , which specifies the position of the picture element represented by the tumor pattern image signal S 1 , is not fed out, and the processing is finished. In cases where it has been judged that the tumor pattern image signal S 1 has been detected, the entire area image signal S having been stored in the entire area image memory 10 is also fed into the local area extracting means 60. In accordance with the received entire area image signal S and the received tumor picture element position signal D 1 , the local area extracting means 60 specifies the picture elements (i.e., the local area constituted of the set of these picture elements), which include the picture elements corresponding to the tumor pattern image signal S 1 and are located in the vicinity of them, according to a predetermined processing procedure. The local area extracting means 60 thus extracts the local area limited image signal S 2 , which represents the local area limited image P 2 , from the entire area image signal S. The extracted local area limited image signal S 2 is fed into the local area limited image emphasizing means 70. By use of the conversion table shown in Figure 4, the local area limited image emphasizing means 70 converts the variation Diris of the density value of the tumor pattern P 1 into the output g(Diris) in accordance with the tumor picture element position signal D 1 in the received local area limited image signal S 2 . With the conversion table, the output g(Diris) is restricted to a small value with respect to the region C 1 , in which the value of the variation Diris is small. Therefore high frequency radiation noise which is detected in the region C1, can be reduced. Specifically, actually, as illustrated in Figure 3, radiation noise has been superposed upon the distribution curve of the density value Dorg along the cross section of the mammogram shown in Figure 2A, which cross section is taken along, for example, line I-I. Radiation noise is also emphasized by the image emphasis processing carried out with Formula (55') and, as a result, an image in which radiation noise has been emphasized is obtained. Therefore, the image quality of the image and its capability of serving as an effective tool in, particularly, the efficient and accurate diagnosis of an illness cannot be kept high. However, with the conversion table, by appropriate setting of the value of the boundary between the regions C1 and C2, it is possible to prevent radiation noise from being emphasized. In the region C3, the change in the output g(Diris) is restricted with respect to the change in the variation Diris. The restriction is done in order to prevent the image portion, which already has a certain level of contrast, from being emphasized excessively. If the excessive emphasis is carried out, the contrast of the image areas other than the image portion will become comparatively low, and therefore the image quality of the image and its capability of serving as an effective tool in the efficient and accurately diagnosis of an illness will become low. As described above, the signal value g(Diris) having been obtained from the conversion table is multiplied by the predetermined emphasis coefficient α, and the resulting product is added to the original density value Diris of the local area in the vicinity of the tumor pattern. In this manner, of the local area limited image P 2 , only the tumor pattern P 1 is selectively emphasized. The signal having thus been obtained is fed into the local area limited image displaying means 90. The local area limited image displaying means 90 reproduces the local area limited image P 2 , in which the tumor pattern P 1 has been emphasized, from the received signal and displays it. In this manner, of the entire area image P, only the local area limited image P 2 containing the tumor pattern P 1 is independently displayed on the local area limited image displaying means 90. Therefore, the person, who views the radiation image, can concentrate his attention on the local area limited image P 2 , which is displayed on the local area limited image displaying means 90. As a result, the efficiency and the accuracy of the diagnosis, or the like, can be kept high. The entire area image displaying means 30 may also serve as the local area limited image displaying means 90. In such cases, of the entire area image P displayed, only the tumor pattern P 1 is emphasized selectively. Therefore, the efficiency and the accuracy of the diagnosis, or the like, can be kept high. A further image processing method will be described hereinbelow. Figure 8 is a block diagram showing an apparatus for computer aided diagnosis of medical images, in which an image processing method is employed. In Figure 8, similar elements are numbered with the same reference numerals with respect to Figure 1. A tumor pattern is taken as the abnormal pattern. Also, an iris filter, which carries out an operation of iris filter, is employed as an abnormal pattern detecting means 140. The iris filter 140 feeds out the information representing the degree of centralization C, which has been calculated with Formula (44) with respect to the picture element corresponding to the tumor pattern, as an iris filter signal Giris representing whether the picture element is or is not the one constituting the tumor pattern. The processing with the iris filter 140 is carried out with the detection processing algorithm for detecting a specific image portion in accordance with Step 1 to Step 3 described above. However, the iris filter 140 in this embodiment does not indicate the algorithm itself and indicates the means for carrying out the processing for detecting the tumor pattern with the algorithm, i.e., the processing carried out with Formula (56). Also, a local area limited image emphasizing means 170 comprises a high frequency component calculating means 171. The high frequency component calculating means 171 carries out an operation on each picture element value (the density value Dorg), which constitutes the local area limited image signal S 2 , in order to calculate an unsharp mask signal Dus with respect to an unsharp mask constituted of a picture element matrix, which has a size of N columns × N rows (wherein N represents an odd number, for example, 5) and has its center at the picture element, the unsharp mask signal Dus being calculated with Formula (37) Dus= (∑Dorg)/N 2 wherein ∑Dorg represents the sum of the image signal values representing the picture elements located within the unsharp mask. The high frequency component calculating means 171 then calculates high frequency components (Dorg-Dus) by subtracting the unsharp mask signal value Dus from the density value Dorg. The local area limited image emphasizing means 170 also comprises a conversion table 172 for converting the iris filter signal Giris, which has been obtained with respect to the picture element representing the tumor pattern extracted by the iris filter 140, into an emphasis coefficient β(Giris), which increases monotonously and is shown in Figure 9. The local area limited image emphasizing means 170 further comprises a tumor pattern emphasizing means 173 for multiplying the high frequency components (Dorg-Dus) by the emphasis coefficient β(Giris) and carrying out a frequency emphasis processing to a high extent on the tumor pattern, which has a density value lower than the density value of the surrounding area. How the apparatus for computer aided diagnosis of medical images, in which the second embodiment of the image processing method in accordance with the present invention is employed, operates will be described hereinbelow. The entire area image signal S, which represents the entire area image P of the mamma having the tumor therein and serving as the object, is fed from an external storage medium, such as a magneto-optical disk, an image read-out apparatus, or the like, into the entire area image memory 10. Also, the entire area image signal S is fed directly from the exterior into the entire area image displaying means 30 (along a line A shown in Figure 8). Alternatively, the entire area image signal S having been stored in the entire area image memory 10 may be fed from the entire area image memory 10 into the entire area image displaying means 30 (along a line B shown in Figure 8). The entire area image displaying means 30 displays the entire area image P in accordance with the entire area image signal S. The entire area image signal S having been stored in the entire area image memory 10 is also fed into the iris filter 140. In accordance with the procedure described above, with respect to the received entire area image signal S, the iris filter 140 rates the degree of centralization of the gradient vector with Formula (44) in accordance with the density value Dorg. The iris filter 140 thereby detects the image signal (hereinbelow referred to as the tumor pattern image signal) S 1 , which represents the tumor pattern P 1 . The iris filter 140 rates the degree of centralization C of the gradient vectors, which has been described above with reference to Figures 2A, 2B, and 2C, and rates the shape in Step 3 described above. In this manner, the iris filter 140 specifies the picture element (and its position), which corresponds to the image signal S 1 representing the tumor pattern P 1 , and feeds out the information representing the degree of centralization C as the iris filter signal Giris, which represents whether the picture element is or is not the one constituting the tumor pattern. The judgment means 50 judges that the tumor pattern image signal S 1 representing the tumor pattern P 1 has been detected by the iris filter 140. Also, the judgment means 50 feeds a position signal (hereinbelow referred to as the tumor picture element position signal) D 1 , which specifies the position of the picture element represented by the tumor pattern image signal S 1 , and the iris filter signal Giris into the local area extracting means 60. In cases where it has been judged that the tumor pattern image signal S 1 representing the tumor pattern P 1 has not been detected by the iris filter 140, the tumor picture element position signal D 1 , which specifies the position of the picture element represented by the tumor pattern image signal S 1 , is not fed out, and the processing is finished. In cases where it has been judged that the tumor pattern image signal S 1 has been detected, the entire area image signal S having been stored in the entire area image memory 10 is also fed into the local area extracting means 60. In accordance with the received entire area image signal S and the received tumor picture element position signal D 1 , the local area extracting means 60 specifies the picture elements (i.e., the local area constituted of the set of these picture elements), which include the picture elements corresponding to the tumor pattern image signal S 1 and are located in the vicinity of them, according to a predetermined processing procedure. The local area extracting means 60 thus extracts the local area limited image signal S 2 , which represents the local area limited image P 2 , from the entire area image signal S. The extracted local area limited image signal S 2 and the iris filter signal Giris are fed into the local area limited image emphasizing means 170. With respect to each picture element (the density value Dorg) constituting the local area limited image signal S 2 having been fed into the local area limited image emphasizing means 170, the high frequency component calculating means 171 calculates the unsharp mask signal Dus. The high frequency component calculating means 171 then calculates the high frequency components (Dorg-Dus). Thereafter, with the conversion table 172, the iris filter signal Giris having been received from the iris filter 140 is converted into the emphasis coefficient β(Giris). As illustrated in Figure 9, the conversion table 172 is constituted of the monotonously increasing function. Specifically, the iris filter signal Giris represents the degree of centralization C calculated with Formula (44), and a large value of the degree of centralization C represents that the picture element is the one corresponding to the tumor pattern. Therefore, when the picture element is the one corresponding to the tumor pattern, a large value of the emphasis coefficient β (Giris) is fed out from the conversion table 172. The tumor pattern emphasizing means 173 calculates the product β(Giris)×(Dorg-Dus) of the emphasis coefficient β(Giris), which has been obtained from the conversion table 172, and the high frequency components (Dorg-Dus), which have been obtained from the high frequency component calculating means 171. The tumor pattern emphasizing means 173 then adds the density value Dorg of the original image to the product and thus carries out the frequency emphasis processing with Formula (1). With the frequency emphasis processing, the high frequency components (Dorg-Dus) are emphasized with the emphasis coefficient β(Giris) obtained in accordance with the iris filter signal Giris, which is obtained from the iris filter 140 and indicates whether the picture element is or is not the one constituting the tumor pattern. Therefore, even if unnecessary components, such as quantum noise, are contained in the high frequency components (Dorg-Dus), in cases where the picture element is not the one constituting the image portion, such as the tumor pattern, (for example, in cases where the picture element is the one constituting the blood vessel pattern, or the like), the value of β(Giris) with respect to the picture element will be small, and the degree of emphasis with respect to the picture element will be kept low. In cases where the picture element is the one constituting the image portion, such as the tumor pattern, the value of β(Giris) with respect to the picture element is large, and therefore the degree of emphasis with respect to the picture element is kept high. Therefore, regardless of whether radiation noise is or is not contained in the high frequency components (Dorg-Dus) of the image, the specific image portion, such as the tumor pattern, can be selectively emphasized with the function β(Giris), which has a value in accordance with whether the image area is or is not the specific image portion. The local area limited image displaying means 90 displays an image, in which the tumor pattern P 1 in the local area limited image P 2 has been emphasized by the local area limited image emphasizing means 170. In this manner, of the entire area image P, only the local area limited image P 2 containing the tumor pattern P 1 is independently displayed on the local area limited image displaying means 90. Therefore, the person, who views the radiation image, can concentrate his attention on the local area limited image P 2 , which is displayed on the local area limited image displaying means 90. As a result, the efficiency and the accuracy of the diagnosis, or the like, can be kept high. The entire area image displaying means 30 may also serve as the local area limited image displaying means 90. In such cases, of the entire area image P displayed, only the tumor pattern P 1 is emphasized selectively. Therefore, the efficiency and the accuracy of the diagnosis, or the like, can be kept high. A first embodiment of the image processing method in accordance with the present invention will be described hereinbelow. Figure 10 is a block diagram showing an apparatus for computer aided diagnosis of medical images, in which the first embodiment of the image processing method in accordance with the present invention is employed. In Figure 10, similar elements are numbered with the same reference numerals with respect to Figure 1. The term "abnormal pattern" as used in the third embodiment means a calcified pattern. Also, a morphology filter, which carries out a morphology operation, is employed as an abnormal pattern detecting means 240. The morphology filter 240 feeds out the information representing the value, which has been calculated with Formula (8) with respect to the picture element corresponding to the calcified pattern, as a morphology signal Dmor representing whether the picture element is or is not the one constituting the calcified pattern. Also, the term "local area" as used in the third embodiment means the region, which is located in the vicinity of the calcified pattern taken as the abnormal pattern and contains the calcified pattern. In the first embodiment, the image signal value representing each of the picture element constituting the image is represented by a density value Dorg. Also, the image signal, which represents the region constituted of the set of the picture elements, is represented by the image signal S. The density value Dorg is the high density-high signal level type of signal value. Further, in this embodiment, the calcified pattern has the characteristics such that the density value Dorg may be smaller than the density values of the surrounding image areas, and the size of the calcified pattern is small. As described above, the processing with the morphology filter 240 is carried out with the detection processing algorithm for detecting a specific image portion of a radiation image by utilizing a multiply structure element. However, in this embodiment, the morphology filter 240 does not indicate the algorithm itself and indicates the means for carrying out the processing for detecting the calcified pattern with the algorithm. Specifically, the morphology filter 240 carries out the morphology operation on the density value Dorg of each picture element by using a multiply structure element Bi, the size of which is larger than a small calcified pattern P 1 and which is shown in Figure 12, and a scale factor λ. In this manner, the small calcified pattern is extracted. The morphology operation is carried out with Formula (8). A local area limited image emphasizing means 270 is provided with a conversion table for converting the variation Dmor of the density value, which represents the small calcified pattern extracted by the morphology filter 240, into an output f(Dmor). Specifically, as illustrated in Figure 13, as for a region C1, in which the variation Dmor is very small, the conversion table converts the variation Dmor such that the output f(Dmor) may be kept to be equal to zero. Also, as for a region C2, in which the variation Dmor has a certain level of value, the conversion table converts the variation Dmor such that the output f(Dmor) may be monotonously increased with respect to the variation Dmor. Further, as for a region C3, in which the variation Dmor is very large, the conversion table converts the variation Dmor such that the output f(Dmor) may be kept to be equal to the upper limit value. The output f(Dmor) obtained from the conversion table is then multiplied by a predetermined emphasis coefficient α (α is equal to, for example, 2), and the resulting product is added to the original density value Dorg of the local area in the vicinity of the small calcified pattern. In this manner, of the local area limited image P 2 , only the small calcified pattern P 1 is selectively emphasized. The calculation is carried out with Formula (2). Dproc=Dorg+α • f(Dmor) How the apparatus for computer aided diagnosis of medical images, in which the third embodiment of the image processing method in accordance with the present invention is employed, operates will be described hereinbelow. The entire area image signal S, which represents the entire area image P of the mamma having the small calcified portion therein and serving as the object, is fed from an external storage medium, such as a magneto-optical disk, an image read-out apparatus, or the like, into the entire area image memory 10. Also, the entire area image signal S is fed directly from the exterior into the entire area image displaying means 30 (along a line A shown in Figure 10). Alternatively, the entire area image signal S having been stored in the entire area image memory 10 may be fed from the entire area image memory 10 into the entire area image displaying means 30 (along a line B shown in Figure 10). The entire area image displaying means 30 displays the entire area image P in accordance with the entire area image signal S. The entire area image signal S having been stored in the entire area image memory 10 is also fed into the morphology filter 240. In accordance with the procedure described above, with respect to the received entire area image signal S, the morphology filter 240 carries out the closing processing with Formula (8) on the density value Dorg and detects the image signal (hereinbelow referred to as the calcified pattern image signal) S 1 , which represents a small calcified pattern P 1 . Specifically, density values Dorg of the mammogram in the cross section taken along line I-I of Figure 11A are distributed in the pattern shown in Figure 11B. At the small calcified pattern P 1 , the density value fluctuates in a spatially narrower range than a structure element B and is smaller than the density values of the surrounding image areas. Therefore, the morphology signal Dmor, which is calculated with Formula (8), takes a predetermined value other than zero, and the pattern is smoothed by the closing processing. On the other hand, at a pattern P 3 of the blood vessel or the mammary gland, at which the density value fluctuates in a spatially wider range than the structure element B, the value of the morphology signal Dmor becomes equal to zero. Therefore, the pattern is not smoothed by the closing operation and is kept unremoved. In this manner, the morphology filter 240 specifies the picture element (and its position), which corresponds to the image signal S 1 representing the small calcified pattern P 1 . The judgment means 50 judges that the calcified pattern image signal S 1 representing the small calcified pattern P 1 has been detected by the morphology filter 240. Also, the judgment means 50 feeds a position signal (hereinbelow referred to as the calcified picture element position signal) D 1 , which specifies the position of the picture element represented by the calcified pattern image signal S 1 , and the morphology signal Dmor, which represents the variation of the density value of the small calcified pattern P 1 having been extracted by the morphology filter 240, into the local area extracting means 60. In cases where it has been judged that the calcified pattern image signal S 1 representing the small calcified pattern P 1 has not been detected by the morphology filter 240, the calcified picture element position signal D 1 , which specifies the position of the picture element represented by the calcified pattern image signal S 1 , is not fed out, and the processing is finished. In cases where it has been judged that the calcified pattern image signal S 1 has been detected, the entire area image signal S having been stored in the entire area image memory 10 is also fed into the local area extracting means 60. In accordance with the received entire area image signal S and the received calcified picture element position signal D 1 , the local area extracting means 60 specifies the picture elements (i.e., the local area constituted of the set of these picture elements), which include the picture elements corresponding to the calcified pattern image signal S 1 and are located in the vicinity of them, according to a predetermined processing procedure. The local area extracting means 60 thus extracts the local area limited image signal S 2 , which represents the local area limited image P 2 , from the entire area image signal S. The extracted local area limited image signal S 2 and the morphology signal Dmor, which represents the variation of the density value of the small calcified pattern P 1 having been extracted by the morphology filter 240, are fed into the local area limited image emphasizing means 270. By use of the conversion table shown in Figure 13, the local area limited image emphasizing means 270 converts the variation Dmor of the density value of the small calcified pattern P 1 into the output f(Dmor). With the conversion table, the output f(Dmor) is equal to zero with respect to the region C1. Therefore, as for the region C1, no image emphasis is carried out, and the processed image signal Dproc becomes equal to the original image signal Dorg. Specifically, actually, as illustrated in Figure 11C, radiation noise has been superposed upon the distribution curve of the density value shown in Figure 11B. However, as indicated by the broken line in Figure 11C, the radiation noise is smoothed by the closing operation of the second term in Formula (8). Therefore, the value of Dmor is varied very finely. However, the amount of the variation is smaller than the variation Dmor due to the small calcified pattern P 1 . Accordingly, by appropriate setting of the value of the boundary between the regions C1 and C2, it is possible to prevent the high frequency noise from being emphasized. In the region C3, the change in the output f(Dmor) is restricted with respect to the change in the value of Dmor. The restriction is done in order to prevent the image portion, which already has a certain level of contrast, from being emphasized excessively. If the excessive emphasis is carried out, the contrast of the image areas other than the image portion will become comparatively low, and therefore the image quality of the image and its capability of serving as an effective tool in the efficient and accurately diagnosis of an illness will become low. As described above, the signal value f(Dmor) having been obtained from the conversion table is multiplied by the predetermined emphasis coefficient α, and the resulting product is added to the original density value Dorg of the local area in the vicinity of the small calcified pattern. In this manner, of the local area limited image P 2 , only the small calcified pattern P 1 is selectively emphasized. The signal having thus been obtained is fed into the local area limited image displaying means 90. The local area limited image displaying means 90 reproduces the local area limited image P 2 , in which the small calcified pattern P 1 has been emphasized, from the received signal and displays it. In this manner, of the entire area image P, only the local area limited image P 2 containing the small calcified pattern P 1 is independently displayed on the local area limited image displaying means 90. Therefore, the person, who views the radiation image, can concentrate his attention on the local area limited image P 2 , which is displayed on the local area limited image displaying means 90. As a result, the efficiency and the accuracy of the diagnosis, or the like, can be kept high. In this embodiment, the entire area image displaying means 30 may also serve as the local area limited image displaying means 90. In such cases, of the entire area image P displayed, only the small calcified pattern P 1 is emphasized selectively. Therefore, overshooting and undershooting can be restricted, an artifact due to them can be reduced, and a reproduced image can be obtained, which has good image quality and can serve as an effective tool in the efficient and accurate diagnosis of an illness. With the processing for detecting the small calcified pattern alone, which is carried out by the morphology filter 240 in accordance with Formula (8), it will often occur that a pattern resembling the small calcified pattern is also detected as a calcified pattern. Specifically, an image (hereinbelow referred to as the non-calcified pattern) will often be detected, which is other than the calcified pattern and has approximately the same size as the calcified pattern and for which the value of Dmor calculated with Formula (8) does not become equal to zero. If the emphasis processing is carried out on such a non-calcified pattern, an accurate diagnosis cannot be made. Therefore, such that the non-calcified pattern may be prevented from being detected together with the calcified pattern and only the calcified pattern can be detected accurately, the morphology filter 240 may be provided with the discriminating function described below. Specifically, the differential operation based upon the morphology operation is carried out with Formula (63) shown below. A larger value of Mgrad represents a higher possibility that the picture element will be the one constituting the calcified pattern. Therefore, in lieu of Formula (8), the logical operation is then carried out with Formula (64). Mgrad = (1/2) • (Dorg ⊕ λB - Dorg ⊖ λ B) if Dmor(x,y) ≧ T 1 and Mgrad≧ T 2 then Dmor(x,y) = Dmor else Dmor(x,y) = 0 In cases where the value of Dmor obtained with Formula (64) is equal to zero, the picture element is the one constituting the non-calcified pattern. Therefore, in such cases, the emphasis processing with Formula (2) is not carried out. In cases where the value of Dmor obtained with Formula (64) is not equal to zero, the picture element is the one constituting the calcified pattern. Therefore, in such cases, the emphasis processing with Formula (2) is carried out. In Formula (64), T1 and T2 represent the threshold values which have been set experimentally. Besides the discrimination with Formulas (63) and (64), the discrimination between the calcified pattern and the non-calcified pattern may be carried out with the combination of the opening processing and the closing processing with the multi-scale. Specifically, the value of Dmor may be set with Formulas (65), (66), and (67) shown below. D' = Dmor ⊖ λ 1 B ⊕ λ 1 B D c (x, y) = D' ⊕λ 2 B ⊖λ 2 B if D c (x,y) ≧ T then Dmor(x,y) = Dmor else Dmor(x,y) = 0 In Formula (67), T represents a threshold value having been set experimentally. In the third embodiment described above, the local area limited image emphasizing means 270 carries out the image emphasis processing with Formula (2) and the conversion table having the function shown in Figure 13. Alternatively, the image emphasis processing may be carried out with Formula (5) and a conversion table having the function shown in Figure 15A or Figure 15B. As another alternative, the image emphasis processing may be carried out with Formula (6) and a conversion table having the function shown in Figure 16. As a further alternative, in lieu of the conversion table having the function shown in Figure 13, the local area limited image emphasizing means 270 may be provided with a low pass filter for converting the variation Dmor of the density value into an unsharp mask signal corresponding to super-low frequency of the variation Dmor. The conversion is carried out with Formula (4). f(Dmor) = (∑ ∑Dmor)/N 2 wherein ∑ ∑ Dmor represents the sum of the morphology signal values with respect to the picture elements located within a picture element matrix, which has a size of N columns × N rows (where N is equal to, for example, 3) and has its center at the picture element, that is associated with the morphology signal Dmor, in a two-dimensional array of picture elements. In such cases, with the local area limited image emphasizing means 270, as represented by Formula (2), the signal value f(Dmor) obtained with Formula (4) may be multiplied by the predetermined emphasis coefficient αm, and the resulting product may be added to the original density value Dorg of the local area in the vicinity of the small calcified pattern. In this manner, of the local area limited image P 2 , only the small calcified pattern P 1 may be selectively emphasized. In cases where the local area limited image emphasizing means 270 is thus constituted, as illustrated in Figure 20A, the morphology signal Dmor, which has sharp pulsed characteristics shown in, for example, Figure 19, is converted to a signal representing the arithmetic mean value of the morphology signal values with respect to the picture elements surrounding the picture element, that is associated with the morphology signal Dmor. Therefore, the pulsed characteristics of the morphology signal Dmor can be rendered unsharp. As a result, a processed image signal Dproc in accordance with the function f(Dmor) can be obtained such that it may represent an emphasized image, in which the occurrence of an artifact at the boundary between the calcified pattern and the other image portions with each other, has been reduced and which gives a natural feeling. Besides the closing operation described above, the morphology filter 240 may also carry out the dilation processing with Formula (8'). Also, in cases where the image signal is of the high luminance-high signal level type, the opening processing with Formula (7) or the erosion processing with Formula (7') may be carried out. A second embodiment of the image processing method in accordance with the present invention will be described hereinbelow. Figure 21 is a block diagram showing an apparatus for computer aided diagnosis of medical images, in which the second embodiment of the image processing method in accordance with the present invention is employed. In Figure 21, similar elements are numbered with the same reference numerals with respect to Figure 1. The term "abnormal pattern" as used in the fourth embodiment means a calcified pattern. Also, a morphology filter, which carries out a morphology operation, is employed as an abnormal pattern detecting means 340. The morphology filter 340 feeds out the information representing the value, which has been calculated with Formula (8) with respect to the picture element corresponding to the calcified pattern, as a morphology signal Dmor representing whether the picture element is or is not the one constituting the calcified pattern. Also, the term "local area" as used in the fourth embodiment means the region, which is located in the vicinity of the calcified pattern taken as the abnormal pattern and contains the calcified pattern. In the second embodiment, the image signal value representing each of the picture element constituting the image is represented by a density signal value Dorg. Also, the image signal, which represents the region constituted of the set of the picture elements, is represented by the image signal S. The density value Dorg is the high density-high signal level type of signal value. As described above, the processing with the morphology filter 340 is carried out with the detection processing algorithm for detecting a specific image portion. However, in this embodiment, the morphology filter 340 does not indicate the algorithm itself and indicates the means for carrying out the processing for detecting the calcified pattern with the algorithm, i.e., the processing carried out with Formula (8). Also, a local area limited image emphasizing means 370 comprises a high frequency component calculating means 371. The high frequency component calculating means 371 carries out an operation on each picture element value (the density value Dorg), which constitutes the local area limited image signal S 2 , in order to calculate an unsharp mask signal Dus with respect to an unsharp mask constituted of a picture element matrix, which has a size of N columns × N rows (wherein N represents an odd number, for example, 5) and has its center at the picture element, the unsharp mask signal Dus being calculated with Formula (37) Dus= (∑Dorg)/N 2 wherein ∑Dorg represents the sum of the image signal values representing the picture elements located within the unsharp mask. The high frequency component calculating means 371 then calculates high frequency components (Dorg-Dus) by subtracting the unsharp mask signal value Dus from the density value Dorg. The local area limited image emphasizing means 370 also comprises a conversion table 372 for converting the morphology signal Dmor, which has been obtained with respect to the picture element representing the calcified pattern extracted by the morphology filter 340, into an emphasis coefficient β(Dmor), which increases monotonously and is shown in Figure 22A. The local area limited image emphasizing means 370 further comprises a calcified pattern emphasizing means 373 for multiplying the high frequency components (Dorg-Dus) by the emphasis coefficient β(Dmor) and carrying out a frequency emphasis processing to a high extent on the calcified pattern, which has a density value lower than the density value of the surrounding area. How the apparatus for computer aided diagnosis of medical images, in which the fourth embodiment of the image processing method in accordance with the present invention is employed, operates will be described hereinbelow. The entire area image signal S, which represents the entire area image P of the mamma having the calcified portion therein and serving as the object, is fed from an external storage medium, such as a magneto-optical disk, an image read-out apparatus, or the like, into the entire area image memory 10. Also, the entire area image signal S is fed directly from the exterior into the entire area image displaying means 30 (along a line A shown in Figure 21). Alternatively, the entire area image signal S having been stored in the entire area image memory 10 may be fed from the entire area image memory 10 into the entire area image displaying means 30 (along a line B shown in Figure 21). The entire area image displaying means 30 displays the entire area image P in accordance with the entire area image signal S. The entire area image signal S having been stored in the entire area image memory 10 is also fed into the morphology filter 340. With Formula (8), the morphology filter 340 detects the image signal (hereinbelow referred to as the calcified pattern image signal) S 1 , which represents a calcified pattern P 1 . As described above with reference to Figures 11A and 11B, the morphology filter 340 specifies the picture element (and its position), which corresponds to the image signal S 1 representing the small calcified pattern P 1 . The judgment means 50 judges that the calcified pattern image signal S 1 representing the small calcified pattern P 1 has been detected by the morphology filter 340. Also, the judgment means 50 feeds a position signal (hereinbelow referred to as the calcified picture element position signal) D 1 , which specifies the position of the picture element represented by the calcified pattern image signal S 1 , and the morphology signal Dmor, which represents the variation of the density value of the small calcified pattern P 1 having been extracted by the morphology filter 340, into the local area extracting means 60. In cases where it has been judged that the calcified pattern image signal S 1 representing the small calcified pattern P 1 has not been detected by the morphology filter 340, the calcified picture element position signal D 1 , which specifies the position of the picture element represented by the calcified pattern image signal S 1 , is not fed out, and the processing is finished. In cases where it has been judged that the calcified pattern image signal S 1 has been detected, the entire area image signal S having been stored in the entire area image memory 10 is also fed into the local area extracting means 60. In accordance with the received entire area image signal S and the received calcified picture element position signal D 1 , the local area extracting means 60 specifies the picture elements (i.e., the local area constituted of the set of these picture elements), which include the picture elements corresponding to the calcified pattern image signal S 1 and are located in the vicinity of them, according to a predetermined processing procedure. The local area extracting means 60 thus extracts the local area limited image signal S 2 , which represents the local area limited image P 2 , from the entire area image signal S. The extracted local area limited image signal S 2 and the morphology signal Dmor are fed into the local area limited image emphasizing means 370. With respect to each picture element (the density value Dorg) constituting the local area limited image signal S 2 having been fed into the local area limited image emphasizing means 370, the high frequency component calculating means 371 for calculating the high frequency components calculates the unsharp mask signal Dus and then calculates the high frequency components (Dorg-Dus). Thereafter, with the conversion table 372, the morphology signal Dmor having been received from the morphology filter 340 is converted into the emphasis coefficient β(Dmor). As illustrated in Figure 22A, the conversion table 372 is constituted of the monotonously increasing function. Specifically, a large value of the morphology signal Dmor represents that the picture element is the one corresponding to the calcified pattern. Therefore, when the picture element is the one corresponding to the calcified pattern, a large value of the emphasis coefficient β(Dmor) s fed out from the conversion table 372. With the conversion table 372, the emphasis coefficient β(Dmor) is restricted to a small value with respect to a region C1, in which the value of the morphology signal Dmor is small. Therefore, as for the region C1, high frequency radiation noise can be reduced. Specifically, actually, as illustrated in Figure 11C, radiation noise has been superposed upon the distribution curve of the density value shown in Figure 11B. However, as indicated by the broken line in Figure 11C, the radiation noise is smoothed by the closing operation of the second term in Formula (8). Therefore, the value of Dmor is varied very finely. However, the amount of the variation is smaller than the variation Dmor due to the small calcified pattern P 1 . Accordingly, by appropriate setting of the value of the boundary between the regions C1 and C2, it is possible to prevent the high frequency noise from being emphasized. In the region C3, the change in the emphasis coefficient β(Dmor) is restricted with respect to the change in the value of Dmor. The restriction is done in order to prevent the image portion, which already has a certain level of contrast, from being emphasized excessively. If the excessive emphasis is carried out, the contrast of the image areas other than the image portion will become comparatively low, and therefore the image quality of the image and its capability of serving as an effective tool in the efficient and accurately diagnosis of an illness will become low. The calcified pattern emphasizing means 373 calculates the product β(Dmor)×(Dorg-Dus) of the emphasis coefficient β(Dmor), which has been obtained from the conversion table 372, and the high frequency components (Dorg-Dus), which have been obtained from the high frequency component calculating means 371. The calcified pattern emphasizing means 373 then adds the density value Dorg of the original image to the product and thus carries out the frequency emphasis processing with Formula (9). [In this embodiment, f(Dorg-Dus) in Formula (9) is set such that f(Dorg-Dus)=Dorg-Dus.] With the frequency emphasis processing, the comparatively high frequency components (Dorg-Dus) are emphasized with the emphasis coefficient β(Dmor) obtained in accordance with the morphology signal Dmor, which is obtained from the morphology filter 340 and indicates whether the picture element is or is not the one constituting the calcified pattern. Therefore, even if unnecessary components, such as quantum noise, is contained in the high frequency components (Dorg-Dus), in cases where the picture element is not the one constituting the image portion, such as the calcified pattern, (for example, in cases where the picture element is the one constituting the blood vessel pattern, or the like), the value of β(Dmor) with respect to the picture element will be small, and the degree of emphasis with respect to the picture element will be kept low. In cases where the picture element is the one constituting the image portion, such as the calcified pattern, the value of β(Dmor) with respect to the picture element is large, and therefore the degree of emphasis with respect to the picture element is kept high. Therefore, regardless of whether radiation noise is or is not contained in the high frequency components (Dorg-Dus) of the image, the specific image portion, such as the calcified pattern, can be selectively emphasized with the function β(Dmor), which has a value in accordance with whether the image area is or is not the specific image portion. The local area limited image displaying means 90 displays an image, in which the small calcified pattern P 1 in the local area limited image P 2 has been emphasized by the local area limited image emphasizing means 370. In this manner, of the entire area image P, only the local area limited image P 2 containing the small calcified pattern P 1 is independently displayed on the local area limited image displaying means 90. Therefore, the person, who views the radiation image, can concentrate his attention on the local area limited image P 2 , which is displayed on the local area limited image displaying means 90. As a result, the efficiency and the accuracy of the diagnosis, or the like, can be kept high. In this embodiment, the entire area image displaying means 30 may also serve as the local area limited image displaying means 90. In such cases, of the entire area image P displayed, only the small calcified pattern P 1 is emphasized selectively. Therefore, overshooting and undershooting can be restricted, an artifact due to them can be reduced, and a reproduced image can be obtained, which has good image quality and can serve as an effective tool in the efficient and accurate diagnosis of an illness. In this embodiment, the morphology operation is carried out with Formula (8). Alternatively, the emphasis processing may be carried out with the morphology operation in accordance with one of Formula (7) and Formulas (10) through (14). With the processing for detecting the small calcified pattern alone, which is carried out by the morphology filter 340 in accordance with one of morphology operation Formulas (7), (8) and Formulas (10) through (14), it will often occur that a pattern resembling the small calcified pattern is also detected as a calcified pattern. Specifically, an image (hereinbelow referred to as the non-calcified pattern) will often be detected, which is other than the calcified pattern and has approximately the same size as the calcified pattern and for which the value of Dmor calculated with Formulas (7), (8) and Formulas (10) through (14) does not become equal to zero. If the emphasis processing is carried out on such a non-calcified pattern, an accurate diagnosis cannot be made. Therefore, such that the non-calcified pattern may be prevented from being detected together with the calcified pattern and only the calcified pattern can be detected accurately, the morphology filter 340 may be provided with the discriminating function described below. Specifically, the differential operation based upon the morphology operation is carried out with Formula (63) shown above. A larger value of Mgrad represents a higher possibility that the picture element will be the one constituting the calcified pattern. Therefore, in lieu of Formulas (7), (8) and Formulas (10) through (14), the logical operation is then carried out with Formula (64). In cases where the value of Dmor obtained with Formula (64) is equal to zero, the picture element is the one constituting the non-calcified pattern. Therefore, in such cases, the emphasis processing with Formula (9) is not carried out. In cases where the value of Dmor obtained with Formula (64) is not equal to zero, the picture element is the one constituting the calcified pattern. Therefore, in such cases, the emphasis processing with Formula (9) is carried out. Besides the discrimination with Formulas (63) and (64), the discrimination between the calcified pattern and the non-calcified pattern may be carried out with the combination of the opening processing and the closing processing with the multi-scale. Specifically, the value of Dmor may be set with Formulas (65), (66), and (67) shown above.

Claims: An image processing method comprising the steps of: i) carrying out a morphology operation on an original image signal Dorg, which represents an image, by using a multiply structure element Bi and a scale factor λ, a morphology signal Dmor being thereby obtained, said morphology signal Dmor representing whether each of picture elements constituting said image is or is not the one corresponding to an image portion, at which said original image signal Dorg fluctuates in a spatially narrower range than said multiply structure element Bi, said method being characterized by ii) carrying out image emphasis processing on said original image signal Dorg and in accordance with said morphology signal Dmor. A method as defined in Claim 1 wherein the image emphasis processing in accordance with said morphology signal Dmor is carried out with Formula (2) Dproc=Dorg+α • f(Dmor) wherein a represents the emphasis coefficient, and f(Dmor) represents the function of Dmor. A method as defined in Claim 2 wherein the function f(Dmor) in accordance with said morphology signal Dmor is said morphology signal Dmor as represented by Formula (3) f(Dmor) =Dmor A method as defined in Claim 2 wherein the function f(Dmor) in accordance with said morphology signal Dmor is an unsharp mask signal corresponding to super-low frequency of said morphology signal Dmor as represented by Formula (4) f(Dmor) = (∑ ∑ Dmor)/N 2 wherein ∑ ∑ Dmor represents the sum of the morphology signal values with respect to the picture elements located within a picture element matrix, which has a size of N columns × N rows (where N represents an odd number) and has its center at the picture element, that is associated with the morphology signal Dmor, in a two-dimensional array of picture elements. A method as defined in Claim 1 wherein the image emphasis processing in accordance with said morphology signal Dmor is carried out with Formula (5) Dproc=Dorg+β (Dorg) × (Dorg-Dmor) wherein β(Dorg) represents the function of Dorg. A method as defined in Claim 1 wherein the image emphasis processing in accordance with said morphology signal Dmor is carried out with Formula (6) Dproc=Dorg + β (Dmor) × (Dorg-Dmor) wherein β(Dmor) represents the function of Dmor. A method as defined in any of Claims 1 to 6 wherein said morphology operation is carried out with Formula (7), and the value of the image signal corresponding to said image portion is larger than the image signal values corresponding to the image areas surrounding said image portion: wherein the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X, and the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X. A method as defined in any of claims 1 to 6 wherein said morphology operation is carried out with Formula (8), and the value of the image signal corresponding to said image portion is smaller than the image signal values corresponding to the image areas surrounding said image portion: wherein the expression X ⊕ λY represents that A times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X, and the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X. A method as defined in any of claims 1 to 6 wherein said morphology operation is carried out with Formula (7'), and the value of the image signal corresponding to said image portion is larger than the image signal values corresponding to the image areas surrounding said image portion: wherein the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X. A method as defined 7 in any of claims 1 to 6 wherein said morphology operation is carried out with Formula (8'), and the value of the image signal corresponding to said image portion is smaller than the image signal values corresponding to the image areas surrounding said image portion: wherein the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X. A method as defined in Claim 1 wherein said image is a radiation image. A method as defined in Claim 11 wherein said radiation image is a medical image. An image processing method comprising the steps of: i) carrying out a morphology operation on an original image signal Dorg, which represents an image, by using a multiply structure element Bi and a scale factor λ, a morphology signal Dmor being thereby obtained, said morphology signal Dmor representing whether each of picture elements constituting said image is or is not the one corresponding to an image portion, at which said original image signal Dorg fluctuates in a spatially narrower range than said multiply structure element Bi, said method being characterized by ; ii) calculating an unsharp mask signal Dus, which corresponds to super-low frequency, from said original image signal Dorg, and iii) carrying out an operation with Formula (9) Dproc=Dorg+ β (Dmor) × f(Dorg-Dus) on said original image signal Dorg by using a function β (Dmor), which is in accordance with said morphology signal Dmor, and a function f(Dorg-Dus), which is in accordance with a difference signal (Dorg-Dus) between said original image signal Dorg and said unsharp mask signal Dus, whereby image emphasis processing is selectively carried out on said image portion. A method as defined in Claim 13 wherein said morphology operation is carried out with Formula (7), and the value of said original image signal Dorg corresponding to said image portion is larger than the image signal values corresponding to the image areas surrounding said image portion: wherein the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X, and the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X. A method as defined in Claim 13 wherein said morphology operation is carried out with Formula (8), and the value of said original image signal Dorg corresponding to said image portion is smaller than the image signal values corresponding to the image areas surrounding said image portion: wherein the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X, and the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X. A method as defined in Claim 13 wherein said morphology operation is carried out with Formula (10), and the value of said original image signal Dorg corresponding to said image portion is larger than the image signal values corresponding to the image areas surrounding said image portion: wherein the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X. A method as defined in Claim 13 wherein said morphology operation is carried out with Formula (11), and the value of said original image signal Dorg corresponding to said image portion is smaller than the image signal values corresponding to the image areas surrounding said image portion: wherein the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X. A method as defined in Claim 13 wherein said morphology operation is carried out with Formula (12), and the value of said original image signal Dorg corresponding to said image portion is larger than the image signal values corresponding to the image areas surrounding said image portion: wherein the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X, the expression (X ⊖ λY) Y represents that the opening operation with the structure element Y is carried out on the image signal (X ⊖ λY), and represents the sum of sets of λ=0, 1, ..., N. A method as defined in Claim 13 wherein said morphology operation is carried out with Formula (13), and the value of said original image signal Dorg corresponding to said image portion is smaller than the image signal values corresponding to the image areas surrounding said image portion: wherein the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X, the expression (X ⊕ λY) Y represents that the closing operation with the structure element Y is carried out on the image signal (X ⊕ λY), and represents the sum of sets of λ =0, 1, ... , N. A method as defined in Claim 13 wherein said morphology operation is carried out with Formula (14) wherein the expression X ⊕ λY represents that λ times of calculations for finding the Minkowski sum are carried out with the structure element Y on the image signal X, and the expression X ⊖ λY represents that λ times of calculations for finding the Minkowski difference are carried out with the structure element Y on the image signal X. A method as defined in claim 13 wherein said image is a radiation image. A method as defined in Claim 21 wherein said radiation image is a medical image.

Inventors: NAKAJIMA NOBUYOSHI, TAKEO HIDEYA, YAMADA MASAHIKO, NAKAJIMA, NOBUYOSHI, TAKEO, HIDEYA, YAMADA, MASAHIKO, NAKAJIMA, NOBUYOSHI, C/O FUJI PHOTO FILM CO., LTD., Takeo, Hideya, c/o Fuji Photo Film Co., Ltd., Yamada, Masahiko, c/o Fuji Photo Film Co., Ltd.
Applicants: FUJI PHOTO FILM CO LTD, FUJI PHOTO FILM CO., LTD.
Application year: 1996
Main classification: G06T5
IPC codes: G06T5, G06T7