Texture homogeneity based in-vivo image identifying device, method, and computer-readable recording device

An image identifying device includes a mechanism that sets an evaluation area whose category is to be identified in an in-vivo image; a mechanism that acquires texture components from the evaluation area in the in-vivo image; a mechanism that calculates an evaluation value indicating homogeneity of the texture components; and a mechanism that identifies the category of the evaluation area on the basis of the evaluation value.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority from Japanese Patent Application No. 2010-265795, filed on Nov. 29, 2010, the entire contents of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an image processing device, an image processing method, and a computer-readable recording device for processing in-vivo images captured inside a body of a subject.

2. Description of the Related Art

Conventionally, endoscopes are widely used as a medical observation device that is introduced into the body of a subject, such as a patient, to observe the inside of a lumen. In recent years, swallowable endoscopes (capsule endoscopes) have been developed that are provided with an imaging device, a communication device, and the like in a capsule-shaped casing and that wirelessly transmit image data captured by the imaging device to the outside of the body. The number of in-vivo images (intra-gastrointestinal images) sequentially captured by such a medical observation device is huge (more than several tens of thousands), and considerable experience is required to make observation and diagnosis of each intra-gastrointestinal image. Therefore, there is a demand for a medical diagnosis support function for supporting doctors in the diagnosis. As one of image recognition techniques that realize such a function, a technique has been proposed in which an abnormal area, such as a lesion, is automatically detected from an intra-gastrointestinal image and an image that needs to be focused on in the diagnosis is presented.

Meanwhile, intra-gastrointestinal images sometimes contain contents, such as residue, that need not be observed, in addition to a mucosal area to be observed in the diagnosis. As a technique for distinguishing between the mucosal area and the contents area (i.e., categories of areas), for example, Japanese Laid-open Patent Publication No. 2010-115413 discloses an image processing method, in which a plurality of images are selected from a series of intra-gastrointestinal images, color feature data is calculated for each pixel or for each small area of the selected images, and a mucosal area in each of the images constituting the series of the intra-gastrointestinal images is identified on the basis of the color feature data.

Furthermore, as a technique for distinguishing between different areas that appear in an image, a method based on texture feature data using a co-occurrence matrix is known (see, for example, Okutomi Masatoshi et al., “Digital Image Processing”, CG-ARTS Society, pp. 194 to 195). The co-occurrence matrix is a matrix whose elements are the probabilities Pδ(Li, Lj) that pairs of pixel values (Li, Lj) occur on the assumption that Li and Lj represent respective pixel values of a pixel i and a pixel j that is deviated from the pixel i by predetermined displacement δ (d, θ) (d is a distance and θ is an angle). With use of such a matrix, it is possible to obtain the feature data indicating the properties, such as homogeneity, directionality, or contrast, of the pixel values.

SUMMARY OF THE INVENTION

An image processing device according to an aspect of the present invention includes: an evaluation area setting unit that sets an evaluation area whose category is to be identified in an in-vivo image; a texture component acquiring unit that acquires texture components from the evaluation area in the in-vivo image; an evaluation value calculating unit that calculates an evaluation value indicating homogeneity of the texture components; and an identifying unit that identifies the category of the evaluation area on the basis of the evaluation value.

An image processing method according to another aspect of the present invention includes: setting an evaluation area whose category is to be identified in an in-vivo image; acquiring texture components from the evaluation area in the in-vivo image; calculating an evaluation value indicating homogeneity of the texture components; and identifying the category of the evaluation area on the basis of the evaluation value.

A computer-readable recording device according to still another aspect of the present invention is stored thereon an executable program, wherein the program instructs a processor to perform: setting an evaluation area whose category is to be identified in an in-vivo image; acquiring texture components from the evaluation area in the in-vivo image; calculating an evaluation value indicating homogeneity of the texture components; and identifying the category of the evaluation area on the basis of the evaluation value.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Exemplary embodiments of the present invention will be explained in detail below with reference to the accompanying drawings. The present invention is not limited by the embodiments. In the drawings, the same components are denoted by the same reference numerals.

Image processing devices according to the embodiments described below are apparatuses that process in-vivo images (intra-gastrointestinal images) of a subject captured by a medical observation device, such as an endoscope or a capsule endoscope. Specifically, the image processing devices perform a process for identifying categories of areas that appear in the intra-gastrointestinal images (i.e., identification of a mucosal area that is observed in diagnosis and a residue area that need not be observed in the diagnosis). In the following embodiments, the intra-gastrointestinal images captured by the medical observation device are, for example, color images, in which each pixel has pixel levels (pixel values) corresponding to respective color components of R (red), G (green), and B (blue).

First Embodiment

FIG. 1is a block diagram of a configuration of an image processing device according to a first embodiment of the present invention. As illustrated inFIG. 1, an image processing device1includes an image acquiring unit11, an input unit12, a display unit13, a recording unit14, a calculating unit15, and a control unit10that controls the whole operation of the image processing device1.

The image acquiring unit11acquires image data of intra-gastrointestinal images captured by a medical observation device. The image acquiring unit11is appropriately configured depending on the mode of a system that includes the medical observation device. For example, when the medical observation device is a capsule endoscope and a portable recording medium is used to transmit and receive the image data to and from the medical observation device, the image acquiring unit11is realized by a reader device that reads the image data of the intra-gastrointestinal images stored in the recording medium that is detachably attached thereto. When a server for storing the image data of the intra-gastrointestinal images captured by the medical observation device is provided, the image acquiring unit11is realized by a communication device or the like that is connected to the server and that acquires the image data of the intra-gastrointestinal images through data communication with the server. Alternatively, the image acquiring unit11may be configured as an interface device or the like that receives image signals from the medical observation device, such as an endoscope, via a cable.

The input unit12is realized by an input device, such as a keyboard, a mouse, a touch panel, or various switches, and outputs input signals input through user operation of the input device to the control unit10.

The display unit13is realized by a display device, such as an LCD or an EL display, and displays various screens including intra-gastrointestinal images under the control of the control unit10.

The recording unit14is realized by various IC memories, such as a ROM configured by a rewritable flash memory or the like and a RAM, a hard disk that is built in or is connected through a data communication terminal, an information recording medium such as a CD-ROM, a reading device thereof, or the like. The recording unit14stores therein the image data of the intra-gastrointestinal images acquired by the image acquiring unit11, a program for operating the image processing device1or for causing the image processing device1to implement various functions, data used during execution of the program, and the like. Specifically, the recording unit14stores therein an image processing program141for distinguishing between a mucosal area and a residue area contained in an intra-gastrointestinal image.

The calculating unit15is realized by hardware, such as a CPU, and the image processing program141loaded on the hardware. The calculating unit15performs various types of calculation processing for processing the image data of the intra-gastrointestinal images and distinguishing the mucosal area and the residue area. The calculating unit15includes an evaluation area setting unit16, a texture component acquiring unit17, a biased-evaluation-value calculating unit18as an evaluation value calculating means that calculates an evaluation value indicating the homogeneity of texture components in the evaluation area, and an identifying unit19.

The evaluation area setting unit16sets an evaluation area, in which a mucosal area and a residue area are distinguished, from an intra-gastrointestinal image.

The texture component acquiring unit17acquires texture components by eliminating structural components from the evaluation area of the intra-gastrointestinal image. As a method of acquiring the texture components, for example, a morphological opening process is used (see KOBATAKE Hidefumi, “Morphology”, Corona Publishing CO., LTD. pp. 82 to 85).

FIGS. 2A to 2Care diagrams explaining the method of acquiring the texture components through the morphological opening process. In the morphological opening process, a reference graphic GEcalled a structuring element is moved so as to be circumscribed around an image G (FIG. 2A) from the smallest pixel value in a three-dimensional space based on the assumption that the pixel value of each pixel on the xy plane constituting a two-dimensional image serves as the height (z axis), so that a locus SC of the maximum value of the outer circumference of the reference graphic GEis obtained (FIG. 2B). The locus SC corresponds to the structural components contained in the original image G. Then, the locus (structural components) SC is subtracted from the image G, so that texture components TC are obtained (FIG. 2C).

FIGS. 3A to 3Care diagrams illustrating a simulation result of acquisition of the texture components from a mucosal area. As illustrated inFIGS. 3A to 3C, structural components SCM(FIG. 3B) obtained through the opening process are subtracted from an image GM(FIG. 3A) of the mucosal area, so that texture components TCMof the mucosal area (FIG. 3C) are obtained. As illustrated inFIG. 3C, the texture components TCMof the mucosal area are relatively homogeneous.

By contrast,FIGS. 4A to 4Care diagrams illustrating a simulation result of acquisition of the texture components from a residue area. As illustrated inFIGS. 4A to 4C, structural components SCR(FIG. 4B) obtained through the opening process are subtracted from an image GR(FIG. 4A) of the residue area, so that texture components TCRof the residue area (FIG. 4C) are obtained. As illustrated inFIG. 4C, the texture components TCRof the residue area are not homogeneous but have a lot of irregularities.

As the method of acquiring the texture components, various known methods may be used other than the method described above. For example, the Fourier transform is performed on an intra-gastrointestinal image and then high-pass filter processing is performed to cut low-frequency components. Thereafter, the inverse Fourier transform is performed on the image thus obtained to obtain the texture components.

The biased-evaluation-value calculating unit18calculates an evaluation value that indicates the homogeneity of the texture components in the coordinate space. Specifically, the biased-evaluation-value calculating unit18includes a coordinate-centroid-distance calculating unit181. The coordinate-centroid-distance calculating unit181calculates, as the evaluation value that indicates the homogeneity, a distance between a centroid of the coordinate and a weighted centroid of the coordinate that is weighted by pixel values (luminance) of the texture components.

The identifying unit19identifies a category of an evaluation area on the basis of the evaluation value calculated by the biased-evaluation-value calculating unit18. Specifically, when the evaluation value indicates that the texture components are homogeneous, the identifying unit19identifies the evaluation area as the mucosal area (seeFIG. 3C). On the other hand, when the evaluation value does not indicate that the texture components are homogeneous, the identifying unit19identifies the evaluation area as the residue area (seeFIG. 4C). Whether the evaluation value indicates that the texture components are homogeneous or not is determined depending on whether the evaluation value is in a predetermined range.

The control unit10is realized by hardware, such as a CPU. The control unit10reads various programs stored in the recording unit14and transfers instructions or data to each unit included in the image processing device1on the basis of the image data input by the image acquiring unit11or an operational signal or the like input by the input unit12, thereby controlling the overall operation of the image processing device1.

The operation performed by the image processing device1will be explained below with reference toFIG. 5.FIG. 5is a flowchart of an image processing operation for distinguishing between the mucosal area and the residue area, which is performed by the image processing device1. In the following, an example will be explained in which image processing is performed on an intra-gastrointestinal image illustrated inFIG. 6. As illustrated inFIG. 6, a series of intra-gastrointestinal images including an intra-gastrointestinal image100mostly contains a mucosal area101and sometimes contains a residue area102, a lesion area103, or the like.

When image data of the series of the intra-gastrointestinal images acquired by the image processing device1are stored in the recording unit14, the calculating unit15reads the intra-gastrointestinal image100as a processing object from the recording unit14(Step S1).

At Step S2, the evaluation area setting unit16sets an evaluation area in the intra-gastrointestinal image100. Specifically, the evaluation area setting unit16divides the intra-gastrointestinal image100into a plurality of rectangular areas as illustrated inFIG. 7, and sequentially sets the areas as an evaluation area105. InFIG. 7, the intra-gastrointestinal image100is divided into sixteen areas; however, the number of the divided areas and the size and the shape of each area are not limited to this example and can be set as desired. For example, it is possible to set an area corresponding to one or a few pixels as one evaluation area, or it is possible to set the whole intra-gastrointestinal image100(i.e., without division) as one evaluation area.

At Step S3, the texture component acquiring unit17receives the intra-gastrointestinal image100and information (e.g., coordinate information) indicating the evaluation area105, and removes structural components106from the intra-gastrointestinal image in the evaluation area105, thereby acquiring texture components.

At Step S4, the coordinate-centroid-distance calculating unit181calculates, as the evaluation value that indicates the homogeneity of the texture components in the evaluation area105(hereinafter, also simply described as an “evaluation value”), a coordinate centroid distance that is a distance between a centroid of the coordinate in the evaluation area105and a weighted centroid of the coordinate that is weighted by pixel values of the texture components in the evaluation area105.

FIG. 8is a flowchart of a process for calculating the evaluation value that indicates the homogeneity of the texture components. At Step S11, the coordinate-centroid-distance calculating unit181acquires information (e.g., coordinate information) indicating the evaluation area105from the evaluation area setting unit16and acquires texture components of the evaluation area105from the texture component acquiring unit17.

At Step S12, the coordinate-centroid-distance calculating unit181calculates a coordinate centroid (gx, gy) of pixels contained in the evaluation area105by using the following Equation (1).

In Equation (1), Xi is the x coordinate of a pixel i (x, y) at the i-th position in the evaluation area105, and Yi is the y coordinate of the pixel i (x, y) at the i-th position in the evaluation area105. N is the total number of pixels contained in the evaluation area105.

At Step S13, the coordinate-centroid-distance calculating unit181calculates a coordinate centroid (Gx, Gy) by weighting the pixels contained in the evaluation area105by pixel values of texture components, by using the following Equation (2).

In Equation (2), Ti (x, y) is the pixel value of a texture component of the pixel at the i-th position in the evaluation area105.

At Step S14, the coordinate centroid distance DG-gby using the calculation results obtained at Steps S12and S13and the following Equation (3).
DG-g=√{square root over ((Gx−gx)2+(Gy−gy)2)}{square root over ((Gx−gx)2+(Gy−gy)2)}  (3)

Thereafter, the operation returns to the main routine.

When the texture components are homogeneous, the weighted coordinate centroid (Gx, Gy) approximately matches with the coordinate centroid (gx, gy), so that the value of the coordinate centroid distance DG-g-becomes small. On the other hand, when the texture components are not homogeneous, the weighted coordinate centroid (Gx, Gy) is deviated from the coordinate centroid (gx, gy), so that the value of the coordinate centroid distance DG-gbecomes large.

The coordinate-centroid-distance calculating unit181outputs the coordinate centroid distance DG-gcalculated through the processes at Steps S11to S14described above to the identifying unit19as the evaluation value that indicates the homogeneity of the texture components.

At Step S5, the identifying unit19identifies the category of the evaluation area105by using the coordinate centroid distance DG-gcalculated by the coordinate-centroid-distance calculating unit181as the evaluation value. Specifically, the identifying unit19compares the evaluation value with a predetermined threshold that is set in advance. When the evaluation value is smaller than the predetermined threshold, the identifying unit19determines that the texture components are homogeneous and identifies the evaluation area105as a mucosal area. On the other hand, when the evaluation value is equal to or greater than the predetermined threshold, the identifying unit19determines that the texture components are inhomogeneous and identifies the evaluation area105as a residue area. The identifying unit19outputs and displays the identification result of the evaluation area105on the display unit13, and stores the identification result in the recording unit14(Step S6).

When there is the evaluation area105of which category is not identified (NO at Step S7), the operation returns to Step S2. On the other hand, when the identification process is completed on all of the evaluation areas105(YES at Step S7), the image processing operation on the intra-gastrointestinal image100ends.

As described above, according to the first embodiment, whether the evaluation area is identified as the mucosal area or the residue area is determined by focusing on the homogeneity of the texture components in the evaluation area. Therefore, it is possible to appropriately identify a category of an identification target area (evaluation area) whose size and shape may vary, by using an algorithm that operates at a high processing speed and without being influenced by imaging conditions. Furthermore, according to the first embodiment, the coordinate centroid distance is calculated as the evaluation value that indicates the homogeneity of the texture components. Therefore, it is possible to determine the homogeneity of the texture components by simple calculation processing.

First Modification of the First Embodiment

The evaluation area may be set (Step S2) by various methods other than the method described above. For example, it is possible to divide the intra-gastrointestinal image by color feature data (see Japanese Laid-open Patent Publication No. 2010-115413) and set each divided area as the evaluation area. Alternatively, it is possible to set an internal area of a closed curve that is extracted by an active contour method (snakes method), as one evaluation area. The active contour method is a method in which the shape of a closed curve that is given as an initial value is continuously changed to extract a closed curve that has the most stable energy level based on the continuity and the smoothness of the closed curve and based on the edge intensity on the closed curve (see CG-ARTS society, Digital Image Processing, pp. 197 to 199).

Second Modification of the First Embodiment

When the coordinate centroid weighted by the pixel values of the texture components is calculated (Step S13), it is possible to perform the weighting on only pixels that satisfy a predetermined condition instead of performing the weighting on all of the pixels by the pixel values. Specifically, the weighting is performed on a pixel whose pixel value is a predetermined percent or greater (e.g., 50% or greater) of the maximum pixel value in the evaluation area. Alternatively, it is possible to perform the weighting on only a pixel having a peak pixel value in the continuous distribution of the texture components (i.e., a pixel whose first derivative is zero and whose second derivative is negative). Consequently, when the texture components are inhomogeneous, distortion between the normal coordinate centroid (gx, gy) and the weighted coordinate centroid (Gx, Gy) is further increased, so that it becomes possible to more easily distinguish between the mucosal area and the residue area.

Second Embodiment

An image processing device according to a second embodiment of the present invention will be explained below.

FIG. 9is a block diagram of a configuration of the image processing device according to the second embodiment. An image processing device2illustrated inFIG. 9includes a calculating unit20. The calculating unit20includes a biased-evaluation-value calculating unit21as the evaluation value calculating means, and an identifying unit22that identifies a category of an evaluation value on the basis of a calculation result obtained by the biased-evaluation-value calculating unit21. The calculating unit20of the second embodiment converts the continuous distribution of the texture components in the evaluation area into a discrete distribution, and calculates the evaluation value indicating the homogeneity of the texture components on the basis of the discrete distribution. The other configurations are the same as those illustrated inFIG. 1.

The biased-evaluation-value calculating unit21includes a discrete distribution calculating unit211and a homogeneity-evaluation-value calculating unit212. The discrete distribution calculating unit211generates discrete distribution data composed of a plurality of discrete points on the basis of the texture components represented as the continuous distribution. The homogeneity-evaluation-value calculating unit212calculates the evaluation value indicating the homogeneity of the texture components on the basis of the discrete distribution data. The identifying unit22determines the homogeneity of the texture components on the basis of the evaluation value and identifies the evaluation area as the mucosal area or the residue area on the basis of the determination result.

The operation performed by the image processing device2will be explained below with reference toFIGS. 10 to 12.FIG. 10is a flowchart of the operation performed by the image processing device2. The operations at Steps S1to S3, S6, and S7are the same as those explained in the first embodiment.

At Step S21, the discrete distribution calculating unit211generates discrete distribution data from the texture components represented as the continuous distribution.FIG. 11is a diagram explaining a process for generating discrete distribution data of the texture components contained in a mucosal area.FIG. 12is a diagram explaining a process for generating discrete distribution data of the texture components contained in a residue area. Specifically, the discrete distribution calculating unit211receives information (coordinate information) indicating the evaluation area from the evaluation area setting unit16, receives the texture components represented as the continuous distribution from the texture component acquiring unit17, and acquires the maximum value of the pixel values in the evaluation area (FIG. 11(a) andFIG. 12(a)).

Subsequently, the discrete distribution calculating unit211acquires the value of a predetermined percent of the maximum value of the pixel values (hereinafter, described as a sampling value). InFIG. 11(a) andFIG. 12(a), the sampling value is set to, for example, 50% of the maximum value. Furthermore, the discrete distribution calculating unit211extracts pixels (coordinates) having the same pixel values as the sampling value, and generates discrete distribution data by setting these pixels as points (discrete points) constituting the discrete distribution (FIG. 11(b) andFIG. 12(b)).

At Step S22, the homogeneity-evaluation-value calculating unit212calculates the evaluation value indicating the homogeneity of the texture components, on the basis of the discrete distribution data generated by the discrete distribution calculating unit211. At this time, the homogeneity-evaluation-value calculating unit212uses what is called a spatial analysis method. The spatial analysis method is a method for classifying the discrete distribution data into a “distribution type” or a “concentrated type”. In the second embodiment, as an example of the spatial analysis method, the nearest neighbor distance method is explained, in which the distribution of the discrete points P is analyzed on the basis of distances between the discrete points P.

As illustrated inFIG. 13, a distance dibetween each of the discrete points Pi(i=1 to n) constituting the discrete distribution data and the nearest neighbor discrete point. Then, an average nearest neighbor distance W that is an average of the distances diis calculated by using the following Equation (4).

In Equation (4), n is the total number of the discrete points Picontained in the discrete distribution data. The average nearest neighbor distance W calculated as above is used as the evaluation value that indicates the homogeneity of the texture components.

FIGS. 14A to 14Care diagrams illustrating a relationship between dispersion (distribution and concentration) of the discrete points P and the average nearest neighbor distance W in an evaluation area A1. When the discrete points P are concentrated in approximately one area in the evaluation area A1(FIG. 14A), the average nearest neighbor distance W takes a small value (W=W1). When areas where the discrete points P are concentrated are increased (FIG. 14B), the average nearest neighbor distance W is slightly increased (W=W2>W1). For example, inFIG. 14B, the discrete points P are concentrated in two areas in the evaluation area A1. The values W1and W2of the average nearest neighbor distance of this case indicate that the texture components are inhomogeneous in the distribution as illustrated in, for example,FIG. 12(b). When the discrete points P are dispersed over the whole evaluation area A1(FIG. 14C), the average nearest neighbor distance W becomes large (W=W3>W2). The value W3of the average nearest neighbor distance of this case indicates that the texture components are homogeneous as illustrated in, for example,FIG. 11(b).

At Step S23, the identifying unit22determines the homogeneity of the texture components and identifies the category of the evaluation area, on the basis of the evaluation value (the average nearest neighbor distance W) calculated by the homogeneity-evaluation-value calculating unit212. Specifically, the identifying unit22determines the homogeneity of the evaluation value by using an expectation value E[W] calculated by the following Equation (5) as a threshold. The expectation value E[W] indicated by Equation (5) is an expectation value based on the assumption that the discrete points P are randomly distributed in the discrete distribution data.

In Equation (5), k is a coefficient (constant) obtained by experimental measurement, and S is an area of the evaluation area A1.

Accordingly, the homogeneity of the texture components is determined as follows.

Furthermore, when the texture components are homogeneous, the identifying unit22identifies the evaluation area as the mucosal area. On the other hand, when the texture components are not homogeneous, the identifying unit22identifies the evaluation area as the residue area.

As described above, according to the second embodiment, the homogeneity of the texture components and the category of the evaluation area are determined on the basis of the discrete distribution data that represents the texture components. Therefore, it is possible to reduce the total amount of calculations, enabling to obtain a determination result in shorter time.

The evaluation value based on the discrete distribution data may be calculated (Step S22) by various methods other than the method described above. In the following, examples of the other methods for calculating the evaluation value based on the discrete distribution data will be explained in the first to third modifications of the second embodiment.

First Modification of the Second Embodiment

Calculation of the Evaluation Value by K-Function Method

The K-function method is a method developed to identify a distribution that can hardly be discriminated by the nearest neighboring method. An evaluation value K(h) by the K-function method is calculated by the following Equation (6).

In Equation (6), ki(h) indicates the number of other discrete points that are present within a range of a distance h form each of the discrete points Piconstituting the discrete distribution data. For example, in the case of a certain discrete point Piindicated in an evaluation area A2inFIG. 15, ki(h)=4. λ is a density (λ=n/S) of the discrete points P in the evaluation area A2.

When the evaluation value K(h) as above is used, the homogeneity of the texture distribution and the category of the evaluation area are determined in the following manner.

First, an expectation value E [K(h)] of the K-function is obtained by the following Equation (7).
E[K(h)]=πh2(7)

Here, π is a circular constant.

The homogeneity of the texture distribution is determined as follows by using the expectation value E [K(h)] as a threshold.

Therefore, when the texture components are homogeneous, the evaluation area is identified as the mucosal area. On the other hand, when the texture components are not homogeneous, the evaluation area is identified as the residue area.

Second Modification of the Second Embodiment

Calculation of the Evaluation Value by x2Test

When an evaluation area A3illustrated inFIGS. 16A and 16Bis evaluated, it is assumed that the evaluation A3is divided into a plurality of areas (e.g., rectangular areas) B each having the same shape and the same size. When it is assumed that the discrete points P are evenly distributed in the evaluation area A3, the occurrence probability of the discrete points P contained in each area B becomes constant. For example, inFIG. 16A, one discrete point P is present in each area B at the same probability. On the other hand, as illustrated inFIG. 16B, when the distribution of the discrete points P is biased, the occurrence probability of the discrete points P in each area B varies. Therefore, if the degree of deviation between the occurrence probability of the discrete points P that are assumed as being evenly distributed and the occurrence probability of the actual discrete points P is obtained, the degree of deviation can be used as the evaluation value that indicates the homogeneity of the texture components.

The x2test is a method for testing, on the assumption that the probability that a discrete point P is contained in the j-th area Bjis PRj, whether a theoretical value based on the probability matches with the actual distribution of n discrete points P. To determine the homogeneity of the texture components by using the x2test, x2is calculated by the following Equation (8) on the assumption that the occurrence probability PR of all of the areas B becomes such that PR=1/M (M is the number of rectangular areas C).

In Equation (8), Cjis the number of discrete points P contained in the j-th area Bj. n is the total number of the discrete points P contained in the evaluation area A3.

x2calculated by the above Equation (8) is used as the evaluation value that indicates the homogeneity of the texture distribution That is, when x2is smaller than a predetermined value (i.e., when the degree of deviation described above is small), it is determined that the texture distribution is homogeneous. In this case, the evaluation area is identified as the mucosal area. On the other hand, when x2is equal to or greater than the predetermined value (i.e., when the degree of deviation described above is large), it is determined that the texture distribution is not homogeneous. In this case, the evaluation area is identified as the residue area.

Third Modification of the Second Embodiment

Calculation of the Evaluation Value by a Diversity Index

As illustrated inFIG. 16A, when the discrete points P are evenly distributed in the evaluation area A3, the number of the areas B containing the discrete points P is large. On the other hand, as illustrated inFIG. 16B, when the distribution of the discrete points P is biased, the number of the areas B that does not contain the discrete point B is large. Therefore, if the degree of distribution of the discrete points P to a plurality of areas B is obtained, the degree can be used as the evaluation value that indicates the homogeneity of the texture components.

As a method of calculating the degree of distribution of the discrete points P to the areas B, for example, a diversity index is used. The diversity index is an index for evaluating the abundance of a type (M) in a group (N). Specifically, the number of the areas B is used as the type (M), and the Simpson diversity index D is calculated by using the following Equation (9).

In Equation (9), Cjis the number of discrete points P contained in the j-th area Bj. n is the total number of the discrete points P contained in the evaluation area A3.

When the diversity index D as above is used, the homogeneity of the texture distribution and the category of the evaluation area are determined in the following manner. That is, when the diversity index D is greater than a predetermined threshold (i.e., when the distribution of the discrete points P is diverse), it is determined that the texture distribution is homogeneous and the evaluation area is a mucosal area. On the other hand, when the diversity index D is equal to or smaller than the predetermined threshold (i.e., when the distribution of the discrete points P is biased), it is determined that the texture components are not homogeneous and the evaluation area is a residue area.

Alternatively, the Shannon index H′ by the following Equation (10) may be used as the degree of distribution of the discrete points P to a plurality of areas B.

In this case, when the Shannon index H′ is greater than a predetermined threshold, it is determined that the texture distribution is homogeneous and the evaluation area is a mucosal area. On the other hand, when the Shannon index H′ is smaller than the predetermined threshold, it is determined that the texture components are inhomogeneous in the distribution and the evaluation area is a residue area.

Third Embodiment

An image processing device according to a third embodiment of the present invention will be explained below.

FIG. 17is a block diagram of a configuration of the image processing device according to the third embodiment. An image processing device3illustrated inFIG. 17includes a calculating unit30. The calculating unit30includes a skewness-evaluation-value calculating unit31as the evaluation value calculating means, and an identifying unit32that identifies a category of an evaluation area on the basis of a calculation result obtained by the skewness-evaluation-value calculating unit31. The other configurations are the same as those illustrated inFIG. 1.

The skewness-evaluation-value calculating unit31obtains a frequency distribution of pixel values (intensities) of the texture components in the evaluation area, and calculates the evaluation value indicating the homogeneity of the texture components on the basis of skewness of the shape of the frequency distribution. The identifying unit32determines the homogeneity of the texture components on the basis of the evaluation value and identifies the evaluation area as a mucosal area or a residue area on the basis of the determination result.

The principle of the category identification in the third embodiment will be explained below with reference toFIGS. 18A,18B,19A and19B. InFIG. 18AandFIG. 19A, the horizontal axes represent the x coordinate of pixels contained in the evaluation area, and the vertical axes represent a difference I′ between a pixel value I and an average μ of the pixels values in the evaluation area. InFIG. 18BandFIG. 19B, the horizontal axes represent a difference I′ between the pixel value I and the average μ, and the vertical axes represent the frequency of the difference I′.

FIG. 18Aillustrates the property of the texture components in the mucosal area. In the mucosal area, the pixel values of the texture components are relatively uniform. When a histogram of such texture components is generated, the histogram has a symmetrical shape with less distortion, about the average value (I′=0) as a central axis (FIG. 18B). On the other hand,FIG. 19Aillustrates the property of the texture components in the residue area. In the residue area, the pixel values of the texture components largely vary. When a histogram of such texture components is generated, the histogram is distorted (FIG. 19B). Therefore, the homogeneity of the texture components can be determined by obtaining the symmetry (distortion) of the histogram of the pixel values of the texture components. Consequently, it is possible to determine whether the evaluation area is the mucosal area or the residue area on the basis of the homogeneity.

The operation performed by the image processing device3will be explained below.FIG. 20is a flowchart of the operation performed by the image processing device3. The operations at Steps S1to S3, S6, and S7are the same as those explained in the first embodiment.

At Step S31, the skewness-evaluation-value calculating unit31acquires texture components from the texture component acquiring unit17and calculates the frequency distribution of the pixel values.

At Step S32, the skewness-evaluation-value calculating unit31calculates skewness Sk by using the following Equation (11), as an evaluation value that indicates the symmetry of the frequency distribution obtained at Step S31.

In Equation (11), Iiis a pixel value of the i-th pixel, μ is an average of the pixel values, σ is a standard deviation of the pixel values, and n is the total frequencies of the pixel values (i.e., the number of pixels).

The skewness Sk is a value that indicates the degree of asymmetry of the distribution of data (pixel value Ii) with respect to the average value (the average μ). As distortion increases, the skewness Sk is more deviated from zero. When the frequency distribution is skewed such that I′<0 (e.g., the case illustrated inFIG. 19A), Sk<0. On the other hand, when the frequency distribution is skewed such that I′>0, Sk>0.

At Step S33, the identifying unit32determines the homogeneity of the texture components and identifies the category of the evaluation area on the basis of the evaluation value (skewness) calculated by the skewness-evaluation-value calculating unit31. Specifically, the identifying unit32compares the skewness Sk calculated by Equation (11) with predetermined thresholds Thresh1and Thresh2that are acquired in advance, and determines the homogeneity of the texture components as follows.

When the texture components are homogeneous, the identifying unit32identifies the evaluation area as the mucosal area. On the other hand, when the texture components are not homogeneous, the identifying unit32identifies the evaluation area as the residue area.

As described above, according to the third embodiment, the homogeneity of the texture components and the category of the evaluation area are determined on the basis of the frequency distribution of the pixel values. Therefore, it is possible to obtain a determination result by using an algorithm that operates at a high processing speed.

The image processing devices1to3explained in the above first to the third embodiments may be implemented by causing a computer system, such as a personal computer or a workstation, to execute a program that is prepared in advance. In the following, a computer system that has the same functions as those of the image processing devices1to3and that executes the image processing program141stored in the recording unit14will be explained.

FIG. 21is a system configuration diagram of a computer system4.FIG. 22is a block diagram of a configuration of a main body unit41illustrated inFIG. 21. As illustrated inFIG. 21, the computer system4includes the main body unit41, a display42for displaying information, such as an image, on a display screen421in accordance with an instruction of the main body unit41, a keyboard43for inputting various types of information to the computer system4, and a mouse44for specifying an arbitrary position on the display screen421of the display42.

The main body unit41includes a CPU411, a RAM412, a ROM413, a hard disk drive (HDD)414, a CD-ROM drive415that receives a CD-ROM46, a USB port416detachably connected to a USB memory47, an I/O interface417for connecting the display42, the keyboard43and the mouse44, and a LAN interface418for connecting to a local area network or a wide area network (LAN/WAN) N1.

The computer system4is connected to a modem45for connecting to a public line N3, such as the Internet. The computer system4is also connected to other computers, such as a personal computer (PC)5, a server6, and a printer7, via the LAN interface418and the local area network or the wide area network N1.

The computer system4reads and executes an image processing program (e.g., the image processing program141illustrated inFIG. 1) recorded in a recording medium, thereby realizing the image processing devices1to3described in the first to the third embodiments. Herein, apart from the CD-ROM46or the USB memory47, the recording medium can be of any type in which the image processing program readable by the computer system4can be stored. For example, the recording medium can also be a “portable physical medium” such as an MO disk, a DVD, a flexible disk (FD), a magneto optical disk, or an IC card. Alternatively, the recording medium can also be a “fixed physical medium” such as the HDD414, the RAM412, or the ROM413that can be disposed inside or outside of the computer system4. Still alternatively, the recording medium can also be a “communication medium”, such as the public line N3connected via the modem45or can be the LAN or WAN N1to which the other computer systems such as the PC5and the server6are connected, that stores computer programs for a short period of time at the time of transmission. The image processing program is not limited to those implemented by the computer system4. The present invention can similarly be applied to the case that the other computer systems, such as the PC5 or the server6, executes the image processing program or the case that the other computer systems execute the image processing program in cooperation with each other.

The present invention is not limited to the first to third embodiments and modifications thereof, and various inventions can be made by appropriately combining plural components disclosed in respective embodiments and modifications. For example, an invention can be made by eliminating some components from all the components disclosed in respective embodiments and modifications, and an invention can be made by appropriately combining components disclosed in different embodiments and modifications.

According to one embodiment of the present invention, a category of an evaluation area in an in-vivo image is identified on the basis of the homogeneity of the texture components in the evaluation area of the in-vivo image. Therefore, it is possible to obtain an appropriate identification result without being influenced by the imaging conditions and by using an algorithm that operates at a high processing speed.