Method and apparatus for detecting prospective abnormal patterns

A value of the degree of centralization of gradient vectors, which has been calculated from an image signal, is compared with a threshold value, and a region of a prospective abnormal pattern is thereby determined. A picture element corresponding to a position, at which the center of gravity on the region of the prospective abnormal pattern is located, is taken as the picture element of interest, and a picture element corresponding to an end point that is associated with a mean value of index values for each radial direction line in iris filter processing, which mean value takes the maximum value, is thereby specified as a marginal point of the region of the prospective abnormal pattern in the direction along which the radial direction line extends. The thus set marginal points are connected by predetermined lines, and the region surrounded by the connecting lines is extracted as the prospective abnormal pattern. The contour shape of a prospective abnormal pattern having a shape with a special image density distribution is detected accurately.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
This invention relates to a method and apparatus for detecting a 
prospective abnormal pattern, typically a tumor pattern, which is embedded 
in a radiation image. 
2. 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. 
In such image processing, the processing is often carried out on the entire 
area of the image. Alternatively, in cases where the purpose of 
examination or diagnosis is clear to a certain extent, the emphasis 
processing is often carried out selectively on a desired image portion, 
which is adapted to the purpose of examination or diagnosis. 
Ordinarily, when an image portion to be processed is to be selected, the 
person, who views the radiation image, views the original image before 
being processed and manually selects the image portion to be processed. 
However, there is the risk that the selected image portion or the 
specified image range will vary, depending upon the level of the 
experience or the image understanding capability of the person, who views 
the radiation image, and the selection cannot be carried out objectively. 
For example, in cases where a radiation image has been recorded for the 
examination of breast cancer, it is necessary to find a tumor pattern, 
which is one of features of a cancerous portion, from the radiation image. 
However, the range of the tumor pattern cannot always be specified 
accurately. Therefore, there is a strong demand for techniques for 
accurately detecting an abnormal pattern, such as a tumor pattern, without 
depending upon the skill of the person, who views the radiation image. 
In order to satisfy the demand described above, extensive research has been 
carried out to make computer aided diagnosis of medical images (CADM). 
With the CADM techniques, prospective abnormal patterns are detected 
automatically by utilizing computer processing. As one of the CADM 
techniques, iris filter processing has heretofore been proposed. 
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 portion having the characteristics such that the 
gradients of the image signal (the image density, or the like) 
representing the image are centralized. 
How the processing for detecting a prospective abnormal pattern 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 X-ray 
film (i.e., an image yielding an image signal of a high signal level for a 
high image density), the image density values of a tumor pattern are 
slightly smaller than the image density values of the surrounding image 
areas. The image density values of the tumor pattern are distributed such 
that the image density value becomes smaller from the periphery of an 
approximately circular tumor pattern toward the center point of the tumor 
pattern. Thus the distribution of the image density values of the tumor 
pattern has gradients of the image density values. Therefore, in the tumor 
pattern, the gradients of the image 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. 
The iris filter calculates the gradients of image signal values, which are 
represented by the image density values, as gradient vectors and feeds out 
the information representing the degree of centralization of the gradient 
vectors. With the iris filter processing. a tumor pattern is detected in 
accordance with the degree of centralization of the gradient vectors. 
Specifically, by way of example, as illustrated in FIG. 5A, a tumor pattern 
P.sub.1 may be embedded in a mammogram P. As illustrated in FIG. 5B, the 
gradient vector at an arbitrary picture element in the tumor pattern 
P.sub.1 is directed to the vicinity of the center point of the tumor 
pattern P.sub.1. On the other hand, as illustrated in FIG. 5C, in an 
elongated pattern P.sub.2, such as a blood vessel pattern or a mammary 
gland 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. As illustrated in FIG. 5D, in a pattern P.sub.3, in 
which elongated patterns, such as mammary gland patterns, intersect each 
other, gradient vectors are liable to centralize upon a specific point. 
Therefore, the pattern P.sub.3, may be detected as a false positive. 
The processing with the iris filter is based on the fundamental concept 
described above. 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 .theta. of the gradient vector of the image 
signal representing the image is calculated with Formula (1) shown below. 
##EQU1## 
As illustrated in FIG. 6, f.sub.1 through f.sub.16 in Formula (1) represent 
the picture element 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, for example, five picture elements (located along the 
column direction of the picture element array) x 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 (2) shown below. 
##EQU2## 
As illustrated in FIG. 7, in Formula (2), 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 .theta.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 (1). 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 (2) 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. FIG. 8 shows an 
example of the filter. The filter is different from the filter shown in 
FIG. 7. With the filter of FIG. 8, the degree of centralization is rated 
only with the picture elements, which are located along radial direction 
lines extending radially from a picture element of interest in M kinds of 
directions adjacent at 2.pi./M degree intervals. (In FIG. 8, 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 direction line and is the n'th picture element as counted 
from the picture element of interest, are given by Formulas (3) and (4) 
shown below. 
EQU x=k+n cos {2.pi.(i-1)/M} (3) 
EQU y=l+n sin {2.pi.(i-1)/M} (4) 
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 direction lines, the output value obtained for 
the picture elements ranging from a certain picture element to a picture 
element, which is located along the radial direction line and at which the 
maximum degree of centralization is obtained, is taken as the degree of 
centralization Cimax with respect to the direction of the radial direction 
line. The mean value of the degrees of centralization Cimax, which have 
been obtained for all of the radial direction lines, is then calculated. 
The mean value of the degrees of centralization Cimax 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 direction line, is 
calculated with Formula (5) shown below. 
##EQU3## 
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. 
Specifically, with Formula (5), the degree of centralization Ci(n) is 
calculated with respect to all of the picture elements, which are located 
along each of the radial direction lines and fall within the range from a 
starting point to an end point, the starting point being set at the 
picture element of interest, the end point being set at one of picture 
elements that are located between a position at the length of distance 
corresponding to the minimum value Rmin having been set for the radius of 
the tumor pattern, which is to be detected, and a position at the length 
of distance corresponding to the maximum value Rmax. 
Thereafter, the degree of centralization C of the gradient vector group is 
calculated with Formulas (6) and (7) shown below. 
##EQU4## 
The value of Cimax of Formula (6) represents the maximum value of the 
degree of centralization Ci(n) obtained for each of the radial direction 
lines with Formula (5). 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 along the direction of 
the radial direction line. 
The calculation with Formula (6) is made for all of the radial direction 
lines, and the contours (marginal points) of the regions of the 
prospective tumor pattern on all of the radial direction lines are thereby 
detected. The adjacent marginal points of the regions of the prospective 
tumor pattern on the radial direction lines are then connected by a 
straight line or a non-linear curve. In this manner, it is possible to 
specify the contour of the region, which may be regarded as the 
prospective tumor pattern. 
Thereafter, with Formula (7), the mean value of the maximum values Cimax of 
the degrees of centralization within the aforesaid regions, which maximum 
values Cimax have been given by Formula (6) for all directions of the 
radial direction lines, is calculated. In Formula (7), by way of example, 
the radial direction lines are set along 32 directions. The calculated 
mean value serves as an output value I of the iris filter processing. The 
output value I is compared with a predetermined constant threshold value 
T, which is appropriate for making a judgment as to whether the detected 
pattern is or is not a prospective tumor pattern. In cases where 
I.gtoreq.T, it is judged that the region having its center at the picture 
element of interest is a prospective abnormal pattern (a prospective tumor 
pattern). In cases where I&lt;T, it is judged that the region having its 
center at the picture element of interest is not a prospective tumor 
pattern. 
FIG. 9A shows a radiation image (a negative image recorded on photographic 
film) P, in which a pattern P.sub.0 of the mamma serving as an object is 
embedded. By way of example, the iris filter processing may be carried out 
on the radiation image P. In such cases, as illustrated in FIGS. 9B and 
9C, an output value I.sub.1 is obtained for a tumor pattern P.sub.1. By 
the comparison of the output value I.sub.1 and the threshold value T with 
each other, a cross-sectional shape A, which is obtained by cutting out 
the distribution pattern of the output value I.sub.1 by the threshold 
value T, is detected as being the region of the prospective tumor pattern. 
The size and the shape of the region, in which the degree of centralization 
C of the gradient vector group with Formula (7) is rated, change 
adaptively in accordance with the distribution of the gradient vectors. 
Such an adaptive change is similar to the manner, in which the iris of the 
human s eye expands or contracts in accordance with the brightness of the 
external field. Therefore, the aforesaid technique for detecting the 
region of the prospective tumor pattern by utilizing the degrees of 
centralization of the gradient vectors is referred to as the iris filter 
processing. 
The calculation of the degree of centralization Ci(n) may be carried out by 
using Formula (5') shown below in lieu of Formula (5). 
##EQU5## 
Specifically, with Formula (5'), the degree of centralization Ci(n) is 
calculated with respect to all of the picture elements, which are located 
along each of the radial direction lines and fall within the range from a 
starting point to an end point, the starting point being set at a picture 
element that is located at the length of distance corresponding to the 
minimum value Rmin having been set for the radius of the tumor pattern to 
be detected, which length of distance is taken from the picture element of 
interest, the end point being set at one of picture elements that are 
located between the position at the length of distance corresponding to 
the minimum value Rmin and the position at the length of distance 
corresponding to the maximum value Rmax, which length of distance is taken 
from the picture element of interest. 
By carrying out the steps described above, the iris filter can efficiently 
detect only the tumor pattern, which has a desired size, from a radiation 
image. Research has heretofore been carried out on the iris filter 
particularly for the purpose of detecting a cancerous portion from a 
mammogram. 
The output value I of the iris filter processing does not necessarily have 
the mountain-shaped distribution having a single peak as illustrated in 
FIG. 9B. 
Specifically, it often occurs that, as illustrated in FIG. 3A, an abnormal 
pattern P.sub.1 having an image density distribution with two minimum 
image density portions may be embedded in a radiation image P. In such 
cases, as illustrated in FIG. 3B, an output value obtained from the iris 
filter processing carried out on the radiation image P has a 
mountain-shaped distribution I.sub.1, which has two peaks (two maximum 
portions). In such cases, as illustrated in FIG. 3C, if a judgment is made 
with a threshold value T1, which has been set to be a level such that it 
may cut out the base portion of the distribution pattern of the output 
value of the iris filter processing, the shape of the cut surface can be 
extracted as a single region A. However, as illustrated in FIG. 3D, if a 
judgment is made with a threshold value T2, which has been set to be a 
level such that it may cut out the portion in the vicinity of the peaks of 
the distribution pattern of the output value, the shapes of the cut 
surfaces will be extracted as two regions A1 and A2. 
Also, if emphasis processing is carried out on the prospective abnormal 
patterns, which have been extracted as the two regions illustrated in FIG. 
3D, an image will be formed which gives a feeling markedly different from 
the proper shape (the proper contour) of the region illustrated in FIG. 
3C. In such cases, the problems occur in that an accurate diagnosis cannot 
be made easily. 
In order for the aforesaid problems to be eliminated, the threshold value T 
may be set to be a level such that it may cut out the base portion of the 
distribution pattern of the output value of the iris filter processing. 
However, as illustrated in FIG. 3C, if the threshold value T is set to be 
small, besides the region A of the tumor pattern, a region B of a false 
positive, which is actually not the tumor pattern, is detected as the 
prospective abnormal pattern. In such cases, considerable time and labor 
will be required for a person who views the radiation image, such as a 
medical doctor, to make a judgment as to whether the detected pattern is a 
tumor pattern or a false positive. Further, in practice, it is impossible 
to previously set an appropriate level of the threshold value in 
accordance with the pattern having a special shape as described above. 
SUMMARY OF THE INVENTION 
The primary object of the present invention is to provide a method of 
detecting a prospective abnormal pattern, wherein a contour shape of a 
prospective abnormal pattern having a shape with a special image density 
distribution is detected accurately. 
Another object of the present invention is to provide an apparatus for 
carrying out the method of detecting a prospective abnormal pattern. 
The objects are accomplished by methods and apparatuses for detecting a 
prospective abnormal pattern in accordance with the present invention, 
wherein a region of a prospective abnormal pattern is obtained by carrying 
out threshold value processing on an output value I of iris filter 
processing, and the center of gravity on the region is calculated. A 
picture element corresponding to the position, at which the center of 
gravity is located, is taken as a picture element of interest. At this 
time, a picture element associated with a mean value of the degrees of 
centralization of image density gradient vectors upon the picture element 
of interest, as calculated with respect to each of radial direction lines 
in the iris filter processing, which mean value takes the maximum value, 
is specified. The thus specified picture element is set as a marginal 
point of the region of the prospective abnormal pattern along the radial 
direction line. The marginal points, which have thus been set on the 
radial direction lines, are then connected by straight lines or non-linear 
curves, and the margin (the contour) of the region of the prospective 
abnormal pattern is thereby determined accurately. 
Specifically, the present invention provides a first method of detecting a 
prospective abnormal pattern, in which an image signal representing a 
radiation image of an object is obtained, the image signal being made up 
of a series of image signal components representing picture elements in 
the radiation image, and a prospective abnormal pattern is detected from 
the radiation image in accordance with the image signal, the method 
comprising the steps of: 
(1) for each picture element among all of the picture elements in the 
radiation image, calculating a gradient vector of the image signal, 
(2) setting an arbitrary picture element, which is among all of the picture 
elements in the radiation image, as a picture element of interest, 
(3) setting a plurality of (i number of) radial direction lines on the 
radiation image, the radial direction lines extending radially from the 
picture element of interest and being adjacent to one another at 
predetermined angle intervals, 
(4) calculating an index value cos .theta.il for each picture element among 
the picture elements, which are located along each of the radial direction 
lines and fall within the range from the picture element of interest to a 
picture element that is located at a length of distance (Rmax) 
corresponding to the maximum size of the prospective abnormal pattern to 
be detected, the index value cos .theta.il being calculated from an angle 
Oil that is made between the gradient vector, which has been calculated 
for each picture element, and the direction along which the radial 
direction line extends, 
(5) calculating a mean value of the index values cos .theta.il having been 
calculated for the picture elements, which are located along each of the 
radial direction lines and fall within the range from a starting point to 
an end point, the starting point being set at the picture element of 
interest, the end point being set at one of the picture elements that are 
located between a position at a length of distance (Rmin) corresponding to 
the minimum size of the prospective abnormal pattern to be detected and 
the position at the length of distance corresponding to the maximum size 
of the prospective abnormal pattern to be detected, a plurality of the 
mean values being obtained for each of the radial direction lines by 
successively setting the end point at the picture elements, 
(6) calculating the maximum value (Cimax of Formula (6)) of the mean values 
of the index values cos .theta.il, which mean values have been obtained 
for each of the radial direction lines by successively setting the end 
point at the picture elements, 
(7) calculating a total sum of the maximum values, which have been obtained 
for all of the plurality of the radial direction lines, a value of the 
degree of centralization of the gradient vector group with respect to the 
picture element of interest being thereby calculated, 
(8) comparing the value of the degree of centralization of the gradient 
vector group, which value has been calculated by the operation of step (7) 
defined above, and a predetermined threshold value with each other, 
(9) judging that the picture element of interest is located within the 
region of the prospective abnormal pattern in cases where the value of the 
degree of centralization of the gradient vector group is not smaller than 
the predetermined threshold value, and judging that the picture element of 
interest is not located within the region of the prospective abnormal 
pattern in cases where the value of the degree of centralization of the 
gradient vector group is less than the predetermined threshold value, 
(10) successively setting the picture element of interest at all of the 
picture elements in the radiation image, repeating the operations of steps 
(3) to (9) defined above, and making judgments as to whether the 
respective picture elements are or are not located within the region of 
the prospective abnormal pattern, 
(11) calculating the center of gravity on a region constituted of the 
picture elements, which have been judged as being located within the 
region of the prospective abnormal pattern, 
(12) taking a picture element corresponding to the position, at which the 
center of gravity is located, as the picture element of interest, and 
thereby specifying a picture element corresponding to the end point that 
is associated with the mean value of the index values cos .theta.il having 
been calculated for each of the radial direction lines, which mean value 
takes the maximum value in the operation of step (6) defined above, 
(13) setting the specified picture element, which corresponds to the end 
point on each of the radial direction lines, as a marginal point of the 
region of the prospective abnormal pattern in the direction along which 
the radial direction line extends, a plurality of the marginal points 
being thereby set on the plurality of the radial direction lines, and 
(14) connecting the adjacent marginal points, which have been set on the 
plurality of the radial direction lines, by predetermined lines, the 
region surrounded by the connecting lines being extracted as the 
prospective abnormal pattern. 
In the operation of step (7) defined above, in lieu of the total sum of the 
maximum values being calculated, the mean value of the maximum values may 
be calculated. Also, in the operation of step (8) defined above, the thus 
calculated mean value of the maximum values and the predetermined 
threshold value may be compared with each other. 
Further, in the operation of step (14) defined above, the marginal points 
may be connected by straight lines or non-linear curves. Alternatively, 
dynamic contour extracting techniques may be employed, wherein a dynamic 
curve having an initial shape repeats deformation in accordance with a 
predetermined deformation tendency and converges, and discretely set 
marginal points are thereby connected smoothly by the dynamic curve. 
In the dynamic contour extracting techniques, an imaginary curve 
(hereinbelow referred to as the dynamic curve), which repeats deformation 
in accordance with the predetermined deformation tendency, is set as a 
model of the contour to be extracted. The tendency of deformation is 
determined such that the contour model may become close to a target 
contour, i.e. such that the dynamic curve may repeat deformation and may 
ultimately converge to the target contour. In this manner, the target 
contour is extracted. 
As one of the dynamic contour extracting techniques, a snakes model has 
heretofore been known. In the snakes model, the tendency of deformation is 
determined by defining energy of the dynamic curve and quantitatively 
rating the state of the dynamic curve. The energy of the dynamic curve is 
defined such that the level of energy may become minimum when the dynamic 
curve coincides with the target contour. The target contour can be 
extracted by finding the stable state, in which the level of energy of the 
dynamic curve becomes minimum. The speed and the accuracy, with which the 
contour extracting processing is carried out, depend upon how the tendency 
of deformation is determined. (The snakes model is described in, for 
example, "SNAKES: ACTIVE CONTOUR MODELS" by M. Kass, A. Witkin, D. 
Terzopoulos, International Journal of Computer Vision, Vol. 1, No. 4, pp. 
321-331, 1988.) 
In cases where the snakes model, which is one of the dynamic contour 
extracting techniques, is employed as a technique for connecting the 
marginal points, the marginal points on the radial direction lines can be 
connected smoothly, and the actual contour of the prospective abnormal 
pattern can be extracted accurately. 
In the aforesaid first method of detecting a prospective abnormal pattern 
in accordance with the present invention, the starting point is set at the 
picture element of interest. A second method of detecting a prospective 
abnormal pattern in accordance with the present invention, which is 
described below, is the same as the first method of detecting a 
prospective abnormal pattern in accordance with the present invention, 
except that the starting point is set at a picture element located at a 
length of distance corresponding to the minimum size of the prospective 
abnormal pattern to be detected, which length of distance is taken from 
the picture element of interest. 
Specifically, the present invention also provides a second method of 
detecting a prospective abnormal pattern, in which an image signal 
representing a radiation image of an object is obtained, the image signal 
being made up of a series of image signal components representing picture 
elements in the radiation image, and a prospective abnormal pattern is 
detected from the radiation image in accordance with the image signal, the 
method comprising the steps of: 
(1) for each picture element among all of the picture elements in the 
radiation image, calculating a gradient vector of the image signal, 
(2) setting an arbitrary picture element, which is among all of the picture 
elements in the radiation image, as a picture element of interest, 
(3) setting a plurality of radial direction lines on the radiation image, 
the radial direction lines extending radially from the picture element of 
interest and being adjacent to one another at predetermined angle 
intervals, 
(4) calculating an index value cos .theta.il for each picture element among 
the picture elements, which are located along each of the radial direction 
lines and fall within the range from a picture element that is located at 
a length of distance corresponding to the minimum size of the prospective 
abnormal pattern to be detected, the length of distance being taken from 
the picture element of interest, to a picture element that is located at a 
length of distance corresponding to the maximum size of the prospective 
abnormal pattern to be detected, the length of distance being taken from 
the picture element of interest, the index value cos .theta.il being 
calculated from an angle .theta.il that is made between the gradient 
vector, which has been calculated for each picture element, and the 
direction along which the radial direction line extends, 
(5) calculating a mean value of the index values cos .theta.il having been 
calculated for the picture elements, which are located along each of the 
radial direction lines and fall within the range from a starting point to 
an end point, the starting point being set at the picture element that is 
located at the length of distance corresponding to the minimum size of the 
prospective abnormal pattern to be detected, the end point being set at 
one of the picture elements that are located between the position at the 
length of distance corresponding to the minimum size of the prospective 
abnormal pattern to be detected and the position at the length of distance 
corresponding to the maximum size of the prospective abnormal pattern to 
be detected, a plurality of the mean values being obtained for each of the 
radial direction lines by successively setting the end point at the 
picture elements, 
(6) calculating the maximum value of the mean values of the index values 
cos .theta.il, which mean values have been obtained for each of the radial 
direction lines by successively setting the end point at the picture 
elements, 
(7) calculating a total sum (or a mean value) of the maximum values, which 
have been obtained for all of the plurality of the radial direction lines, 
a value of the degree of centralization of the gradient vector group with 
respect to the picture element of interest being thereby calculated, 
(8) comparing the value of the degree of centralization of the gradient 
vector group, which value has been calculated by the operation of step (7) 
defined above, and a predetermined threshold value with each other, 
(9) judging that the picture element of interest is located within the 
region of the prospective abnormal pattern in cases where the value of the 
degree of centralization of the gradient vector group is not smaller than 
the predetermined threshold value, and judging that the picture element of 
interest is not located within the region of the prospective abnormal 
pattern in cases where the value of the degree of centralization of the 
gradient vector group is less than the predetermined threshold value, 
(10) successively setting the picture element of interest at all of the 
picture elements in the radiation image, repeating the operations of steps 
(3) to (9) defined above, and making judgments as to whether the 
respective picture elements are or are not located within the region of 
the prospective abnormal pattern, 
(11) calculating the center of gravity on a region constituted of the 
picture elements, which have been judged as being located within the 
region of the prospective abnormal pattern, 
(12) taking a picture element corresponding to the position, at which the 
center of gravity is located, as the picture element of interest, and 
thereby specifying a picture element corresponding to the end point that 
is associated with the mean value of the index values cos .theta.il having 
been calculated for each of the radial direction lines, which mean value 
takes the maximum value in the operation of step (6) defined above, 
(13) setting the specified picture element, which corresponds to the end 
point on each of the radial direction lines, as a marginal point of the 
region of the prospective abnormal pattern in the direction along which 
the radial direction line extends, a plurality of the marginal points 
being thereby set on the plurality of the radial direction lines, and 
(14) connecting the adjacent marginal points, which have been set on the 
plurality of the radial direction lines, by predetermined lines, the 
region surrounded by the connecting lines being extracted as the 
prospective abnormal pattern. 
In the second method of detecting a prospective abnormal pattern in 
accordance with the present invention, in the operation of step (14) 
defined above, the marginal points may be connected by straight lines or 
non-linear curves. Alternatively, the dynamic contour extracting 
techniques, such as the snakes model, may be employed, wherein a dynamic 
curve having an initial shape repeats deformation in accordance with a 
predetermined deformation tendency and converges, and discretely set 
marginal points are thereby connected smoothly by the dynamic curve. 
The present invention further provides a first apparatus for detecting a 
prospective abnormal pattern, in which an image signal representing a 
radiation image of an object is obtained, the image signal being made up 
of a series of image signal components representing picture elements in 
the radiation image, and a prospective abnormal pattern is detected from 
the radiation image in accordance with the image signal, the apparatus 
comprising: 
(i) a gradient vector calculating means for calculating a gradient vector 
of the image signal, the calculation being made for each picture element 
among all of the picture elements in the radiation image, 
(ii) a picture-element-of-interest setting means for setting an arbitrary 
picture element, which is among all of the picture elements in the 
radiation image, as a picture element of interest, the picture elements 
being successively set as the picture element of interest, 
(iii) a detection size setting means for setting the minimum size and the 
maximum size of the prospective abnormal pattern to be detected, 
(iv) a direction line setting means for setting a plurality of radial 
direction lines on the radiation image, the radial direction lines 
extending radially from the picture element of interest and being adjacent 
to one another at predetermined angle intervals, 
(v) an index value calculating means for calculating an index value cos 
.theta.il for each picture element among the picture elements, which are 
located along each of the radial direction lines and fall within the range 
from the picture element of interest to a picture element that is located 
at a length of distance corresponding to the maximum size of the 
prospective abnormal pattern to be detected, the index value cos .theta.il 
being calculated from an angle .theta.il that is made between the gradient 
vector, which has been calculated for each picture element, and the 
direction along which the radial direction line extends, 
(vi) a maximum value calculating means for calculating a mean value of the 
index values cos .theta.il having been calculated for the picture 
elements, which are located along each of the radial direction lines and 
fall within the range from a starting point to an endpoint, the starting 
point being set at the picture element of interest, the end point being 
set at one of the picture elements that are located between a position at 
a length of distance corresponding to the minimum size of the prospective 
abnormal pattern to be detected and the position at the length of distance 
corresponding to the maximum size of the prospective abnormal pattern to 
be detected, a plurality of the mean values being obtained for each of the 
radial direction lines by successively setting the end point at the 
picture elements, 
the maximum value calculating means extracting the maximum value of the 
mean values of the index values cos .theta.il, which mean values have been 
obtained for each of the radial direction lines by successively setting 
the end point at the picture elements, 
(vii) a centralization degree calculating means for calculating a total sum 
of the maximum values, which have been obtained for all of the plurality 
of the radial direction lines, and thereby calculating a value of the 
degree of centralization of the gradient vector group with respect to the 
picture element of interest, 
(viii) a comparison and judgment means for comparing the value of the 
degree of centralization of the gradient vector group, which value has 
been calculated by the centralization degree calculating means, and a 
predetermined threshold value with each other, 
the comparison and judgment means judging that the picture element of 
interest is located within the region of the prospective abnormal pattern 
in cases where the value of the degree of centralization of the gradient 
vector group is not smaller than the predetermined threshold value, and 
judging that the picture element of interest is not located within the 
region of the prospective abnormal pattern in cases where the value of the 
degree of centralization of the gradient vector group is less than the 
predetermined threshold value, 
(ix) a center-of-gravity calculating means for calculating the center of 
gravity on a region constituted of the picture elements, which have been 
judged as being located within the region of the prospective abnormal 
pattern, 
(x) a marginal point setting means for taking a picture element 
corresponding to the position, at which the center of gravity is located, 
as the picture element of interest, and thereby specifying a picture 
element corresponding to the end point that is associated with the maximum 
value having been extracted by the maximum value calculating means, the 
picture element corresponding to the end point being specified with 
respect to each of the radial direction lines, 
the marginal point setting means setting the specified picture element, 
which corresponds to the end point on each of the radial direction lines, 
as a marginal point of the region of the prospective abnormal pattern in 
the direction along which the radial direction line extends, a plurality 
of the marginal points being thereby set on the plurality of the radial 
direction lines, and 
(xi) a contour extracting means for connecting the adjacent marginal 
points, which have been set on the plurality of the radial direction 
lines, by predetermined lines, and extracting the region, which is 
surrounded by the connecting lines, as the prospective abnormal pattern. 
In the first apparatus for detecting a prospective abnormal pattern in 
accordance with the present invention (and in a second apparatus for 
detecting a prospective abnormal pattern in accordance with the present 
invention, which is described below), the contour extracting means may 
connect the marginal points by straight lines or non-linear curves. 
Alternatively, the contour extracting means may employ the dynamic contour 
extracting techniques, such as the snakes model, in which a dynamic curve 
having an initial shape repeats deformation in accordance with a 
predetermined deformation tendency and converges, and in which discretely 
set marginal points are thereby connected smoothly by the dynamic curve. 
A second apparatus for detecting a prospective abnormal pattern in 
accordance with the present invention is the same as the first apparatus 
for detecting a prospective abnormal pattern in accordance with the 
present invention, except for the index value calculating means and the 
maximum value calculating means. 
Specifically, the present invention still further provides a second 
apparatus for detecting a prospective abnormal pattern, in which an image 
signal representing a radiation image of an object is obtained, the image 
signal being made up of a series of image signal components representing 
picture elements in the radiation image, and a prospective abnormal 
pattern is detected from the radiation image in accordance with the image 
signal, the apparatus comprising: 
(i) a gradient vector calculating means for calculating a gradient vector 
of the image signal, the calculation being made for each picture element 
among all of the picture elements in the radiation image, 
(ii) a picture-element-of-interest setting means for setting an arbitrary 
picture element, which is among all of the picture elements in the 
radiation image, as a picture element of interest, the picture elements 
being successively set as the picture element of interest, 
(iii) a detection size setting means for setting the minimum size and the 
maximum size of the prospective abnormal pattern to be detected, 
(iv) a direction line setting means for setting a plurality of radial 
direction lines on the radiation image, the radial direction lines 
extending radially from the picture element of interest and being adjacent 
to one another at predetermined angle intervals, 
(v) an index value calculating means for calculating an index value cos 
.theta.il for each picture element among the picture elements, which are 
located along each of the radial direction lines and fall within the range 
from a picture element that is located at a length of distance 
corresponding to the minimum size of the prospective abnormal pattern to 
be detected, the length of distance being taken from the picture element 
of interest, to a picture element that is located at a length of distance 
corresponding to the maximum size of the prospective abnormal pattern to 
be detected, the length of distance being taken from the picture element 
of interest, the index value cos .theta.il being calculated from an angle 
.theta.il that is made between the gradient vector, which has been 
calculated for each picture element, and the direction along which the 
radial direction line extends, 
(vi) a maximum value calculating means for calculating a mean value of the 
index values cos .theta.il having been calculated for the picture 
elements, which are located along each of the radial direction lines and 
fall within the range from a starting point to an endpoint, the starting 
point being set at the picture element that is located at the length of 
distance corresponding to the minimum size of the prospective abnormal 
pattern to be detected, the end point being set at one of the picture 
elements that are located between the position at the length of distance 
corresponding to the minimum size of the prospective abnormal pattern to 
be detected and the position at the length of distance corresponding to 
the maximum size of the prospective abnormal pattern to be detected, a 
plurality of the mean values being obtained for each of the radial 
direction lines by successively setting the end point at the picture 
elements, 
the maximum value calculating means extracting the maximum value of the 
mean values of the index values cos .theta.il, which mean values have been 
obtained for each of the radial direction lines by successively setting 
the end point at the picture elements, 
(vii) a centralization degree calculating means for calculating a total sum 
of the maximum values, which have been obtained for all of the plurality 
of the radial direction lines, and thereby calculating a value of the 
degree of centralization of the gradient vector group with respect to the 
picture element of interest, 
(viii) a comparison and judgment means for comparing the value of the 
degree of centralization of the gradient vector group, which value has 
been calculated by the centralization degree calculating means, and a 
predetermined threshold value with each other, 
the comparison and judgment means judging that the picture element of 
interest is located within the region of the prospective abnormal pattern 
in cases where the value of the degree of centralization of the gradient 
vector group is not smaller than the predetermined threshold value, and 
judging that the picture element of interest is not located within the 
region of the prospective abnormal pattern in cases where the value of the 
degree of centralization of the gradient vector group is less than the 
predetermined threshold value, 
(ix) a center-of-gravity calculating means for calculating the center of 
gravity on a region constituted of the picture elements, which have been 
judged as being located within the region of the prospective abnormal 
pattern, 
(x) a marginal point setting means for taking a picture element 
corresponding to the position, at which the center of gravity is located, 
as the picture element of interest, and thereby specifying a picture 
element corresponding to the end point that is associated with the maximum 
value having been extracted by the maximum value calculating means, the 
picture element corresponding to the end point being specified with 
respect to each of the radial direction lines, 
the marginal point setting means setting the specified picture element, 
which corresponds to the end point on each of the radial direction lines, 
as a marginal point of the region of the prospective abnormal pattern in 
the direction along which the radial direction line extends, a plurality 
of the marginal points being thereby set on the plurality of the radial 
direction lines, and 
(xi) a contour extracting means for connecting the adjacent marginal 
points, which have been set on the plurality of the radial direction 
lines, by predetermined lines, and extracting the region, which is 
surrounded by the connecting lines, as the prospective abnormal pattern. 
With the methods and apparatuses for detecting a prospective abnormal 
pattern in accordance with the present invention, the region of the 
prospective abnormal pattern is obtained by carrying out threshold value 
processing on the output value of the iris filter processing, and the 
center of gravity on the region is calculated. The picture element 
corresponding to the position, at which the center of gravity is located, 
is taken as the picture element of interest. At this time, a picture 
element associated with the mean value of the degrees of centralization of 
image density gradient vectors upon the picture element of interest, as 
calculated with respect to each of radial direction lines in the iris 
filter processing, which mean value takes the maximum value, is specified. 
The thus specified picture element is set as a marginal point of the 
region of the prospective abnormal pattern along the radial direction 
line. The marginal points, which have thus been set on the radial 
direction lines, are then connected by straight lines or non-linear 
curves. In this manner, the margin (the contour) of the region of the 
prospective abnormal pattern can be determined accurately. 
Specifically, the methods and apparatuses for detecting a prospective 
abnormal pattern in accordance with the present invention utilize the 
characteristics such that, as for the picture elements located within the 
region of the prospective abnormal pattern, regardless of which picture 
element located within the region of the prospective abnormal pattern is 
taken as the picture element of interest, the picture element associated 
with the mean value of the degrees of centralization of image density 
gradient vectors upon the picture element of interest, as calculated with 
respect to each of radial direction lines in the iris filter processing, 
which mean value takes the maximum value, is located at the margin of the 
region of the prospective abnormal pattern. As illustrated in FIG. 3D, it 
may occur that the region of the prospective abnormal pattern, which 
region is obtained by carrying out the threshold value processing on the 
output value of the iris filter processing, is divided into two regions. 
In such cases, the picture element corresponding to the position, at which 
the center of gravity on either one of the two divided regions is located, 
may be taken as the picture element of interest. Also, with respect to the 
picture element of interest, the picture element associated with the mean 
value of the degrees of centralization of image density gradient vectors 
upon the picture element of interest, as calculated with respect to each 
of radial direction lines in the iris filter processing, which mean value 
takes the maximum value, may be specified. The thus specified picture 
element corresponds to the proper margin of the region of the prospective 
abnormal pattern. 
Therefore, the contour of the prospective abnormal pattern can be 
determined by connecting the marginal points, which have thus been set on 
the radial direction lines, by utilizing, for example, the aforesaid 
dynamic contour extracting techniques. In this manner, the region of the 
prospective abnormal pattern can be detected accurately.

DESCRIPTION OF THE PREFERRED EMBODIMENTS 
The present invention will hereinbelow be described in further detail with 
reference to the accompanying drawings. 
FIG. 1 is a schematic block diagram showing an embodiment of the first 
apparatus for detecting a prospective abnormal pattern in accordance with 
the present invention. FIG. 2 is a block diagram showing an example of a 
computer aided medical image diagnosing apparatus, in which the 
prospective abnormal pattern detecting apparatus of FIG. 1 is employed. 
With reference to FIG. 2, a computer aided medical image diagnosing 
apparatus 100 comprises a storage means 30 for storing a received image 
signal (hereinbelow referred to as the entire area image signal) S, and an 
entire area image processing means 40 for reading out the entire area 
image signal S from the storage means 30 and carrying out image 
processing, such as gradation processing or frequency processing, on the 
entire area image signal S. The computer aided medical image diagnosing 
apparatus 100 also comprises a prospective abnormal pattern detecting 
apparatus 10 for reading out the entire area image signal S from the 
storage means 30 and extracting an image signal (hereinbelow referred to 
as the local area limited image signal), which represents a prospective 
abnormal pattern (a prospective tumor pattern), from the entire area image 
signal S. The computer aided medical image diagnosing apparatus 100 
further comprises a local area limited image processing means 50 for 
carrying out emphasis processing on the extracted local area limited image 
signal in order to emphasize the extracted prospective tumor pattern. The 
computer aided medical image diagnosing apparatus 100 still further 
comprises a displaying means 60 for displaying the entire area image, 
which has been obtained from the image processing carried out by the 
entire area image processing means 40, and the prospective tumor pattern, 
which has been obtained from the image processing carried out by the local 
area limited image processing means 50, as a visible image. 
By way of example, as illustrated in FIG. 3A, an image P, which represents 
a mammogram of a patient, is stored on a stimulable phosphor sheet. The 
stimulable phosphor sheet, on which the image P representing the mammogram 
has been stored, is then exposed to stimulating rays, which cause the 
stimulable phosphor sheet to emit light in proportion to the amount of 
energy stored thereon during its exposure to radiation. The emitted light 
is photoelectrically detected, and the thus obtained image signal is 
converted into a digital image signal. The digital image signal (the image 
signal of a high image signal level for a high image density) is fed as 
the entire area image signal S into the computer aided medical image 
diagnosing apparatus 100. In the image P, a tumor pattern P.sub.1 having 
an image density distribution with two minimum image density portions, a 
pattern P.sub.2 of the mammary gland, and the like, are embedded. 
With the displaying means 60, the entire area image and the prospective 
tumor pattern may be independently displayed on the displaying screen. 
However, in this embodiment, the entire area image is displayed, and the 
image portion corresponding to the prospective tumor pattern in the entire 
area image is replaced by the prospective tumor pattern, which has been 
obtained from the image processing carried out by the local area limited 
image processing means 50. 
As illustrated in FIG. 1 in detail, the prospective abnormal pattern 
detecting apparatus 10 comprises a gradient vector calculating means 11 
for calculating an image density gradient vector of the received image 
signal, the calculation being made for each picture element among all of 
the picture elements in the radiation image represented by the received 
image signal. The prospective abnormal pattern detecting apparatus 10 also 
comprises a picture-element-of-interest setting means 14 for setting an 
arbitrary picture element, which is among all of the picture elements in 
the radiation image, as a picture element of interest, the picture 
elements being successively set as the picture element of interest, and a 
detection size setting means 12 for setting the minimum size (a radius 
Rmin) and the maximum size (a radius Rmax) of the prospective tumor 
pattern to be detected. The prospective abnormal pattern detecting 
apparatus 10 further comprises a direction line setting means 13 for 
setting a plurality of (e.g., 32) radial direction lines (as illustrated 
in FIG. 8) on the radiation image, the radial direction lines extending 
radially from the picture element of interest, which has been set by the 
picture-element-of-interest setting means 14, and being adjacent to one 
another at predetermined angle intervals (e.g., at intervals of 11.25 
degrees). The prospective abnormal pattern detecting apparatus 10 still 
further comprises an index value calculating means 15 for calculating an 
index value cos .theta.il for each picture element among the picture 
elements, which are located along each of the radial direction lines and 
fall within the range from the picture element of interest to a picture 
element that is located at a length of distance corresponding to the 
maximum size Rmax of the prospective tumor pattern to be detected. The 
index value cos .theta.il is calculated from an angle .theta.il that is 
made between the gradient vector, which has been calculated for each 
picture element, and the direction along which the radial direction line 
extends. (The angle .theta.il represents the angle that is made between 
the gradient vector, which has been calculated for an l'th picture 
element, as counted from the picture element of interest, on an i'th 
radial direction line among the 32 radial direction lines, and the 
direction along which the i'th radial direction line extends.) The 
prospective abnormal pattern detecting apparatus 10 also comprises a 
maximum value calculating means 16 for calculating a mean value Ci(n) of 
the index values cos .theta.il having been calculated for the picture 
elements, which are located along each of the radial direction lines and 
fall within the range from a starting point to an end point, the starting 
point being set at the picture element of interest, the end point being 
set at one of the picture elements that are located between a position at 
a length of distance corresponding to the minimum size Rmin of the 
prospective tumor pattern to be detected and the position at the length of 
distance corresponding to the maximum size Rmax of the prospective tumor 
pattern to be detected. The mean value Ci(n) is calculated with Formula 
(5) shown below, and a plurality of the mean values Ci(n) are obtained for 
each of the radial direction lines by successively setting the end point 
at the picture elements. Also, the maximum value calculating means 16 
extracts the maximum value Cimax of the mean values Ci(n) of the index 
values cos .theta.il, which mean values have been obtained for each of the 
radial direction lines by successively setting the end point at the 
picture elements. The maximum value Cimax is extracted with Formula (6) 
shown below. The prospective abnormal pattern detecting apparatus 10 
further comprises a centralization degree calculating means 17 for 
calculating an arithmetic mean value, (.SIGMA.Cimax)/32, of the maximum 
values Cimax, which have been obtained for all of the 32 radial direction 
lines, and thereby calculating a value C of the degree of centralization 
of the gradient vector group with respect to the picture element of 
interest. The value C is calculated with Formula (7) shown below. 
##EQU6## 
The prospective abnormal pattern detecting apparatus 10 still further 
comprises a comparison and judgment means 18 for comparing the value C of 
the degree of centralization of the gradient vector group, which value has 
been calculated by the centralization degree calculating means 17, and a 
predetermined threshold value T with each other. In cases where the value 
C of the degree of centralization of the gradient vector group is not 
smaller than the predetermined threshold value T, the comparison and 
judgment means 18 judges that the picture element of interest is located 
within the region of the prospective tumor pattern. In cases where the 
value C of the degree of centralization of the gradient vector group is 
less than the predetermined threshold value T, the comparison and judgment 
means 18 judges that the picture element of interest is not located within 
the region of the prospective tumor pattern. The prospective abnormal 
pattern detecting apparatus 10 also comprises a center-of-gravity 
calculating means 19 for calculating the center of gravity on a region 
constituted of the picture elements, which have been judged as being 
located within the region of the prospective tumor pattern as a result of 
the operations for successively setting the picture element of interest at 
all of the picture elements in the radiation image by the 
picture-element-of-interest setting means 14 and for making judgments with 
respect to all of the picture elements in the radiation image by the 
comparison and judgment means 18. The prospective abnormal pattern 
detecting apparatus 10 further comprises a marginal point setting means 20 
for taking a picture element corresponding to the position, at which the 
center of gravity is located, as the picture element of interest, and 
thereby specifying a picture element corresponding to the end point that 
is associated with the maximum value having been extracted by the maximum 
value calculating means 16, the picture element corresponding to the end 
point being specified with respect to each of the radial direction lines. 
Also, the marginal point setting means 20 sets the specified picture 
element, which corresponds to the end point on each of the radial 
direction lines, as a marginal point of the region of the prospective 
tumor pattern in the direction along which the radial direction line 
extends. A plurality of the marginal points are thereby set on the 
plurality of the radial direction lines. The prospective abnormal pattern 
detecting apparatus 10 still further comprises a contour extracting means 
21 for connecting the adjacent marginal points, which have been set on the 
plurality of the radial direction lines, by predetermined non-linear 
curves in accordance with the dynamic contour extracting technique, and 
extracting the region, which is surrounded by the connecting curves, as 
the prospective tumor pattern. 
Specifically, as illustrated in FIG. 6, the gradient vector calculating 
means 11 sets a mask, which has a size of, for example, five picture 
elements (located along the column direction of the picture element 
array).times.five picture elements (located along the row direction of the 
picture element array) and which has its center at the picture element j. 
Also, for each picture element j among all of the picture elements 
constituting the image represented by the received image signal, the 
gradient vector calculating means 11 calculates the direction .theta. of 
the image density gradient vector of the image signal with Formula (1) 
shown below by using the image signal values (i.e., the picture element 
values) f.sub.1 through f.sub.16 corresponding to the picture elements 
located at the peripheral areas of the mask. 
##EQU7## 
The mask size is not limited to five picture elements (located along the 
column direction of the picture element array).times.five picture elements 
(located along the row direction of the picture element array), and may be 
selected from various different sizes. 
The detection size setting means 12 sets the minimum size (the radius Rmin) 
and the maximum size (the radius Rmax) of the prospective tumor pattern to 
be detected. For this purpose, information representing the minimum size 
and the maximum size may be inputted by the operator from an input means 
(not shown), such as a keyboard, which is provided in the detection size 
setting means 12. Alternatively, information representing various sizes 
may be stored previously in the detection size setting means 12, and one 
of the sizes may be selected automatically in accordance with the kind of 
the image to be processed. 
The number of the radial direction lines, which is set by the direction 
line setting means 13, is not limited to 32. However, if the number of the 
radial direction lines is very large, the amount of the calculation 
processing will become very large. If the number of the radial direction 
lines is very small, the contour shape of the prospective tumor pattern 
cannot be detected accurately. Therefore, the number of the radial 
direction lines should preferably be approximately 32. From the viewpoint 
of the calculation processing, or the like, the radial direction lines 
should preferably be set at equal angle intervals. 
In the maximum value calculating means 16, in lieu of the starting point 
being set at the picture element of interest, the starting point may be 
set at a picture element located at a length of distance corresponding to 
the minimum size Rmin of the prospective tumor pattern to be detected, 
which length of distance is taken from the picture element of interest. 
In such cases, in lieu of Formula (5), the mean value Ci(n) of the index 
values cos .theta.il having been calculated for the picture elements, 
which are located along each of the radial direction lines and fall within 
the range from the starting point to the end point, is represented by 
Formula (5') shown below. The constitution for such processing constitutes 
an embodiment of the second apparatus for detecting a prospective abnormal 
pattern in accordance with the present invention. 
##EQU8## 
In the comparison and judgment means 18, the value C of the degree of 
centralization of the gradient vector group and the predetermined 
threshold value T are compared with each other. The term "predetermined 
threshold value" as used herein means the threshold value determined 
before an ultimate comparison is made. In this embodiment, several 
threshold values of different levels are prepared. The threshold value of 
each level is employed by way of trial, and a threshold value of a level 
is ultimately employed such that the number of detected prospective tumor 
patterns may fall within the range of seven to ten. The ultimately 
employed threshold value is also referred to as the predetermined 
threshold value. 
As the dynamic contour extracting technique in the contour extracting means 
21, the snakes model described above may be employed. 
In the snakes model, the tendency of deformation is determined by defining 
energy of the dynamic curve and quantitatively rating the state of the 
dynamic curve. The energy of the dynamic curve is defined such that the 
level of energy may become minimum when the dynamic curve coincides with 
the target contour. The target contour can be extracted by finding the 
stable state, in which the level of energy of the dynamic curve becomes 
minimum. The level of energy is defined as the total sum of a plurality of 
levels of energy, which are defined in accordance with the states of the 
dynamic curve. The levels of energy, which are defined in accordance with 
the states of the dynamic curve, include a level of energy, which is 
defined in accordance with the characteristics of the dynamic curve, a 
level of energy, which is defined in accordance with limitations imposed 
upon the dynamic curve from the exterior, and the like. 
In general, a point on the dynamic curve is represented by the formula 
shown below 
EQU v(s)=(x(s), y(s)) 
by using a parameter s corresponding to the distance from a predetermined 
point on the dynamic curve, the distance being taken along the dynamic 
curve. Also, energy E.sub.snakes which the dynamic curve has is 
represented by the formula shown below. 
##EQU9## 
wherein Eint represents the internal energy, Eimage represents the image 
energy, and Eext represents the external energy. 
The internal energy Eint is the value for rating the characteristics of the 
dynamic curve. The characteristics have heretofore been rated as the 
"smoothness," and the internal energy is also referred to as the spline 
energy. The internal energy is defined such that it may take a small value 
for a smooth dynamic curve. In cases where deformation is carried out such 
that the internal energy may become small, the dynamic curve becomes 
smooth. The internal energy is represented by the formula shown below. 
EQU Eint={w.sub.sp1 .times..vertline.v.sub.s (s).vertline..sup.2 +w.sub.sp2 
.times..vertline.v.sub.ss (s).vertline..sup.2 }/2 
wherein v.sub.s (s)=dv(s)/ds, v.sub.ss (s)=d.sup.2 v(s)/ds.sup.2, and each 
of w.sub.sp1 and w.sub.sp2 represents the parameter representing the 
weight of each term. 
The image energy Eimage is the value for rating the effects of the image 
upon the dynamic curve. As the effects, the "image density gradient" has 
heretofore been utilized. Specifically, the characteristics such that the 
image density gradient at an image portion in the vicinity of the contour 
is sharper than the image density gradients at the other image portions. 
The image energy is defined such that it may take a small value for an 
image portion at which the image density gradient is sharp. As a result, 
the dynamic curve is brought to the contour as the deformation proceeds. 
The image energy is represented by the formula shown below. 
EQU Eimage=w.sub.grad .times.{-grad.sup.2 I(x, y)} 
wherein I (x, y) represents the image density at the point (x(s), y(s)), 
and w.sub.grad represents the parameter representing the weight. 
The external energy Eext is the value for rating the limitations imposed 
intentionally by the operator. In general, as the limitations, a potential 
field specialized for each image, or the like, is employed. As in the two 
kinds of energy described above, the external energy is defined such that 
the dynamic curve may become close to the contour when the dynamic curve 
is deformed such that the external energy may become small. However, the 
external energy can be defined arbitrarily as a design item and lacks 
general-purpose properties. Therefore, In this embodiment, the external 
energy is ignored (Eext=0). 
Specifically, with respect to the marginal points having been set, an 
initial dynamic curve (initial snakes) is set. The initial dynamic curve 
has n number of nodes on the circumference of a circle having a radius 
Rmax and having its center at the position, at which the calculated center 
of gravity is located. A contraction repeating process is carried out 
until the dynamic curve converges. When the dynamic curve has converged, 
the nodes are connected with one another, and the picture elements falling 
within the region surrounded by the connecting curves are extracted. 
How the prospective abnormal pattern detecting apparatus 10 operates will 
be described hereinbelow. 
The entire area image signal S, which has been inputted from the storage 
means 30 into the prospective abnormal pattern detecting apparatus 10, is 
fed into the gradient vector calculating means 11, the 
picture-element-of-interest setting means 14, and the contour extracting 
means 21. As described above, the gradient vector calculating means 11 
sets the mask, which has a size of five picture elements (located along 
the column direction of the picture element array).times.five picture 
elements (located along the row direction of the picture element array). 
Also, for each picture element among all of the picture elements 
constituting the image represented by the received image signal, the 
gradient vector calculating means 11 calculates the direction .theta. of 
the image density gradient vector of the image signal by using the image 
signal values (i.e., the picture element values) corresponding to the 
picture elements located at the peripheral areas of the mask. Information 
representing the calculated direction .theta. of the image density 
gradient vector is fed into the index value calculating means 15. 
The picture-element-of-interest setting means 14 sets an arbitrary picture 
element, which is among all of the picture elements in the radiation image 
represented by the received image signal, as the picture element of 
interest. The picture elements are successively set as the picture element 
of interest. Information representing the thus set picture element of 
interest is fed into the direction line setting means 13. The direction 
line setting means 13 sets the 32 radial direction lines on the radiation 
image, the radial direction lines extending radially from the set picture 
element of interest and being adjacent to one another at equal angle 
intervals of, e.g., 11.25 degrees. Information representing the set radial 
direction lines is fed into the index value calculating means 15. 
Information representing the minimum size (the radius Rmin) and the maximum 
size (the radius Rmax) of the prospective tumor pattern to be detected by 
the prospective abnormal pattern detecting apparatus 10 is inputted by the 
operator into the detection size setting means 12. The information 
representing the minimum size Rmin and the maximum size Rmax is also fed 
into the index value calculating means 15. 
The index value calculating means 15 superposes the 32 radial direction 
lines, which have been set by the direction line setting means 13, upon 
the picture elements, which are arrayed in the two-dimensional array as in 
the image signal and for which the directions .theta. of the gradient 
vectors have been calculated by the gradient vector calculating means 11. 
Also, the index value calculating means 15 extracts the picture elements 
located on each of the 32 radial direction lines. 
Further, the index value calculating means 15 calculates the index value 
cos .theta.il for each picture element among the picture elements, which 
are located along each of the radial direction lines and fall within the 
range from the picture element of interest to a picture element that is 
located at the length of distance corresponding to the maximum size Rmax 
of the prospective tumor pattern to be detected. The index value cos 
.theta.il is calculated from the angle .theta.il that is made between the 
direction .theta. of the gradient vector, which has been calculated for 
each picture element, and the direction along which the radial direction 
line extends. (The angle .theta.il represents the angle that is made 
between the gradient vector, which has been calculated for an l'th picture 
element, as counted from the picture element of interest, on an i'th 
radial direction line among the 32 radial direction lines, and the 
direction along which the i'th radial direction line extends.) 
Information representing the index values cos .theta.il having been 
calculated for the picture elements, which are located along each of the 
radial direction lines, is fed into the maximum value calculating means 
16. The maximum value calculating means 16 calculates the mean value Ci(n) 
of the index values cos .theta.il having been calculated for the picture 
elements, which are located along each of the radial direction lines and 
fall within the range from a starting point to an endpoint, the starting 
point being set at the picture element of interest, the end point being 
set at one of the picture elements that are located between a position at 
the length of distance corresponding to the minimum size Rmin of the 
prospective tumor pattern to be detected and the position at the length of 
distance corresponding to the maximum size Rmax of the prospective tumor 
pattern to be detected. A plurality of the mean values Ci(n) are obtained 
for each of the radial direction lines by successively setting the end 
point at the picture elements. Also, the maximum value calculating means 
16 extracts the maximum value Cimax of the mean values Ci(n) of the index 
values cos .theta.il, which mean values have been obtained for each of the 
radial direction lines by successively setting the end point at the 
picture elements. 
The mean value Ci(n) takes the maximum value Cimax in cases where the 
picture element at the end point is located at the margin of the tumor 
pattern P.sub.1, i.e. in cases where the picture element at the end point 
corresponds to a rising point G in the distribution of the output value 
I.sub.1 (=C) of the iris filter processing illustrated in FIG. 3B. 
Information representing the maximum value Cimax, which has been extracted 
for each of the radial direction lines, is fed into the centralization 
degree calculating means 17. The centralization degree calculating means 
17 calculates the arithmetic mean value of the maximum values Cimax, which 
have been obtained for all of the 32 radial direction lines, and thereby 
calculates the value C of the degree of centralization of the gradient 
vector group with respect to the picture element of interest. 
Information representing the value C of the degree of centralization of the 
gradient vector group with respect to the picture element of interest is 
fed into the comparison and judgment means 18. 
The same operations as those described above are carried out by 
successively setting the picture element of interest at different picture 
elements in the picture-element-of-interest setting means 14. Information 
representing the values C of the degrees of centralization, which have 
been calculated with respect to all of the picture elements in the 
radiation image, is fed into the comparison and judgment means 18. 
The comparison and judgment means 18 compares the value C of the degree of 
centralization of the gradient vector group and an initially set threshold 
value T with each other. In cases where C.gtoreq.T, the comparison and 
judgment means 18 judges that the picture element of interest is located 
within the region of the prospective tumor pattern. In cases where C&lt;T, 
the comparison and judgment means 18 judges that the picture element of 
interest is not located within the region of the prospective tumor 
pattern. 
The initially set threshold value T is not necessarily an appropriate 
value. Specifically, if the threshold value T is very small, even a 
picture element, which is not located within the region of the tumor 
pattern P.sub.1 and is located within the region of the mammary gland 
pattern P.sub.2 (corresponding to the output value I.sub.2 of the iris 
filter processing), will be judged as being located within the region of 
the tumor pattern P.sub.1. 
Therefore, the comparison and judgment means 18 adjusts the level of the 
threshold value such that the number of the regions, which are constituted 
of the picture elements having been judged as being located within the 
region of the prospective tumor pattern, may fall within the range of 
seven to ten. Information representing the region constituted of the 
picture elements, which have been judged as being located within the 
region of the prospective tumor pattern as a result of the processing with 
the adjusted threshold value T, is fed into the center-of-gravity 
calculating means 19. 
As illustrated in FIG. 3D, in cases where the threshold value, which has 
been set such that the number of the detected regions falls within the 
range of seven to ten, is equal to T2, the two peak portions of the 
distribution pattern of the value C (=I) of the degree of centralization 
in the tumor pattern P.sub.1 are cut out by the level of the threshold 
value T2. As a result, the proper region of the tumor pattern P.sub.1, 
which is indicated by the broken line in FIG. 3D, is extracted as two 
regions A1 and A2. 
The center-of-gravity calculating means 19 calculates the center of gravity 
on each of the seven to ten regions, which have been detected in the 
manner described above. For example, as for the region A1, the center of 
gravity a1 is calculated. As for the region A2, the center of gravity a2 
is calculated. Also, as for each of the other seven to eight regions of 
prospective tumor patterns (prospective abnormal patterns), the center of 
gravity is calculated. 
Information representing the center of gravity is fed into the marginal 
point setting means 20. From the maximum value calculating means 16, the 
marginal point setting means 20 receives the information representing a 
picture element corresponding to the end point that is associated with the 
maximum value Cimax of the mean values Ci(n) for each of the radial 
direction lines, the maximum value Cimax having been extracted by the 
maximum value calculating means 16, when a picture element corresponding 
to the position, at which the center of gravity is located, is taken as 
the picture element of interest. The picture element corresponding to the 
end point is thus specified with respect to each of the radial direction 
lines. 
As described above, the picture element corresponding to the end point, 
which is associated with the maximum value Cimax of the mean values Ci(n) 
for each of the radial direction lines, represents the margin of the 
prospective tumor pattern. Therefore, as illustrated in FIG. 3E, when the 
picture element corresponding to the position, at which the center of 
gravity a1 on the region A1 is located, is taken as the picture element of 
interest, the picture elements b1, b2, . . . , b32 corresponding to the 
end points, which are associated with the maximum values Cimax of the mean 
values Ci(n) for the radial direction lines, are located on the proper 
margin of the tumor pattern P.sub.1. Also, when the picture element 
corresponding to the position, at which the center of gravity a2 on the 
region A2 is located, is taken as the picture element of interest, the 
picture elements c1, c2, . . . , c32 corresponding to the end points, 
which are associated with the maximum values Cimax of the mean values 
Ci(n) for the radial direction lines, are located on the proper margin of 
the tumor pattern P.sub.1. 
As illustrated in FIG. 4A, with respect to the picture elements (the 
marginal points) located on the margin of each prospective tumor pattern, 
the contour extracting means 21 sets the initial dynamic curve (initial 
snakes). The initial dynamic curve has n number of nodes on the 
circumference of the circle having the radius Rmax and having its center 
at the position, at which the calculated center of gravity is located. 
Also, as illustrated in FIG. 4B, the contraction repeating process is 
carried out until the dynamic curve converges. As illustrated in FIG. 4C, 
when the dynamic curve has converged, the nodes are connected with one 
another, and the region surrounded by the connecting curves is extracted 
as the prospective tumor pattern. 
By the operations described above, even if the threshold value was not set 
to be an appropriate level, the contour shape of the prospective tumor 
pattern P.sub.1 can be detected accurately. 
The image signal representing the prospective tumor pattern, the contour of 
which has thus been extracted accurately by the prospective abnormal 
pattern detecting apparatus 10, is fed into the local area limited image 
processing means 50. In the local area limited image processing means 50, 
the received image signal is subjected to emphasis processing for 
emphasizing the prospective tumor pattern. The image signal having been 
obtained from the emphasis processing is fed into the displaying means 60. 
Also, in the entire area image processing means 40, image processing, such 
as gradation processing or frequency processing, for obtaining an entire 
area image having good image quality is carried out on the entire area 
image signal S. The entire area image signal S having been obtained from 
the image processing is fed from the entire area image processing means 40 
into the displaying means 60. On the displaying means 60, the entire area 
image, which is represented by the entire area image signal S, is 
displayed, such that the image portion corresponding to the prospective 
tumor pattern in the entire area image may be replaced by the prospective 
tumor pattern, which has been obtained from the image processing carried 
out by the local area limited image processing means 50. The displayed 
visible image is used by a person, who views the radiation image, such as 
a medical doctor, in making a diagnosis of the tumor pattern. 
In the embodiment described above, the maximum value calculating means 16 
sets the starting point at the picture element of interest. In lieu of the 
starting point being set at the picture element of interest, the starting 
point may be set at the picture element located at the length of distance 
corresponding to the minimum size Rmin of the prospective tumor pattern to 
be detected, which length of distance is taken from the picture element of 
interest. Specifically, in a prospective abnormal pattern detecting 
apparatus 10', a maximum value calculating means 16' for carrying out such 
an operation may be employed in lieu of the aforesaid maximum value 
calculating means 16. The prospective abnormal pattern detecting apparatus 
10' constitutes an embodiment of the second apparatus for detecting a 
prospective abnormal pattern in accordance with the present invention. The 
other constitution, the operations, and the effects of the embodiment of 
the second apparatus for detecting a prospective abnormal pattern in 
accordance with the present invention are the same as those in the 
aforesaid embodiment of the first apparatus for detecting a prospective 
abnormal pattern in accordance with the present invention. 
In the aforesaid embodiments of the first and second apparatuses for 
detecting a prospective abnormal pattern in accordance with the present 
invention, the mammogram is processed. However, the methods and 
apparatuses for detecting a prospective abnormal pattern in accordance 
with the present invention are also applicable when images other than the 
mammogram are processed.