JP2001351090A - Method and system for detecting abnormal shadow candidate - Google Patents

Abstract

PROBLEM TO BE SOLVED: To obtain an adequate detection level for an abnormal shadow candidate according to previous information, etc., such as medical interview information. SOLUTION: The abnormal shadow candidate detection system equipped with an abnormal shadow candidate detecting means 1 which detects an abnormal shadow candidate through mammography P is equipped with a previous information receiving means 2 which inputs previous information on a subject and a detection level varying means 3 which varies the detection level for the abnormal shadow candidate of the abnormal shadow candidate detecting means 1 according to the contents of the previous information inputted to the previous information receiving means 2.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an abnormal shadow candidate detecting method and an abnormal shadow candidate detecting system, and more particularly, to a detecting method and a detecting system with improved diagnostic performance.

[0002]

2. Description of the Related Art Conventionally, in a medical field, a radiation image of a patient is photographed, and a film on which the obtained radiation image is recorded is read on a shakasten for interpretation.
They are used to diagnose injuries and other conditions. In recent years, instead of directly exposing a radiation image to a film,
The radiation image is stored in a stimulable phosphor sheet (radiation image conversion panel) for storing and storing the radiation image, and the excitation light is applied to the stimulable phosphor sheet to emit the stimulated emission light in a light amount corresponding to the stored and stored radiation image. To obtain a digital image signal, and thereafter, the digital image signal is reproduced as a visible image by printing it on a recording medium such as a film or displaying it on an image display device such as a CRT. (Radiation image recording and reading system (computed radiography);
No. 29, No. 56-11395, No. 56-11397).

According to such a radiation image recording and reading system, by performing various signal processing on the digital image signal, the gradation characteristics and frequency characteristics of the output visible image are more suitable for image reading than before. The diagnostic performance based on images can be improved.

[0004] The obtained digital image signal is analyzed using a computer, and abnormal shadows such as tumor shadows and microcalcification shadows appearing in the image are automatically detected. A system that secures a certain detection level even when the degree is low and is useful for diagnosis has been developed (abnormal shadow candidate detection system; Japanese Patent Application Laid-Open No. 8-294479, etc.).

This system automatically detects a candidate for a tumor shadow in an image by evaluating the degree of concentration of a density (signal value) gradient vector of a digital image signal (mammography) of a breast obtained mainly in a breast cancer screening. Or morphological operations (dilation processing,
By performing an erosion process, an opening process, a closing process, etc.) to automatically detect candidates for microcalcification shadows, and to detect abnormal shadow candidates such as tumor shadows and microcalcifications. An abnormal shadow candidate detected by this system is displayed on a CRT or a liquid crystal display device by, for example, marking it in a rectangular ROI (region of interest) frame on mammography,
By printing on a diagnostic film, it can be used for diagnosis.

[0006]

As described above, the abnormal shadow candidate detection system automatically detects abnormal shadow candidates on the basis of the obtained image, thereby improving the image reading ability of the image interpreter. Despite this, it is possible to detect abnormal shadow candidates at a certain detection level, so it is a very effective system to suppress overlooking of abnormal shadow candidates, but it detects abnormal shadow candidates based only on images. In view of the contents of the prior information about the subject obtained before the detection processing of the abnormal shadow candidate, such as information other than the image, that is, an inquiry result, a palpation result, medical history information, etc., the subject is abnormal. Even if the radiogram interpreter determines that shadow is highly likely to have occurred, it has not reached the detection level preset in the abnormal shadow candidate detection system. Have abnormal shadow is, there may not be detected. In such a case, the interpreter comprehensively judges the above-described prior information, the abnormal shadow candidate detection result, and the image interpretation result, and if the suspected existence of the abnormal shadow candidate cannot be wiped out, the instruction of the interpreter is issued. The detection processing of the abnormal shadow candidate is performed again, for example, by changing the detection level according to the above, and the presence or absence of the abnormal shadow candidate is determined based on the re-detected result.

However, as described above, when there is a suspicion that an abnormal shadow candidate exists based on the prior information obtained in advance, a detection process is first performed at a normal detection level, whereby an abnormal shadow candidate cannot be obtained. When
It is a waste of time to change the detection level and perform the detection process again, and there is a possibility that the result of missing the abnormal shadow candidate without performing the detection process again because the first detection process is completely trusted. I can't deny it.

When the above-mentioned image of the subject is obtained by radiography, the imaging conditions (tube voltage of the radiation source, radiation dose (irradiation time), use of grid, etc.) are individually determined according to individual differences of the subject. In some cases, the difference in the photographing conditions set in this way may cause the obtained image to have a different detected level of the abnormal shadow candidate.

The present invention has been made in view of the above circumstances, and the reliability of abnormal shadow candidate detection has been improved as compared with the related art in consideration of individual circumstances according to prior information about a subject and shooting conditions. It is an object of the present invention to provide a method and system for detecting an enhanced abnormal shadow candidate.

[0010]

An abnormal shadow candidate detecting method and an abnormal shadow candidate detecting system according to the present invention change the detection level of an abnormal shadow candidate according to the contents of prior information about a subject and shooting conditions. In addition, abnormal shadow candidate detection processing is also performed in consideration of individual circumstances such as prior information about a subject and shooting conditions, and the reliability of detection is increased compared to the related art.

That is, a first abnormal shadow candidate detecting method according to the present invention is the abnormal shadow candidate detecting method for detecting an abnormal shadow candidate in an image based on the image of the object. The detection level of the abnormal shadow candidate is changed accordingly.

Here, changing the detection level in accordance with the contents of the prior information means that at least when the contents of the prior information increase the possibility of the presence of the abnormal shadow, the detection rate of the abnormal shadow candidate is increased. Means to change the detection level.

For example, as the preliminary information, it is possible to apply medical inquiry information obtained by medical inquiry, past medical history information, palpation information obtained by palpation examination, and a combination of two or more of these. Interview information includes the patient's age, gender, weight, lifestyle (dietary habits, sleeping hours, etc.)
The information includes tastes (drinking amount, smoking status, etc.), details of the task (radiation, asbestos, other chemicals, etc.), subjective symptoms (pain status, etc.), and family medical history. For example, elderly people have cancer (abnormal shadow)
Because the onset rate tends to be high, the detection level is changed so as to increase the detection rate of abnormal shadow candidates as the age increases, and the weight of people with heavy weight tends to have low contrast of abnormal shadow images. The detection level may be changed so as to increase the detection rate as the weight becomes heavier, and the detection level may be changed according to the gender for the type of cancer in which the onset probabilities for each site vary greatly according to gender. In addition, when eating habits that tend to develop illness (unbalanced eating, etc.) are remarkable, when drinking is large, when smoking is used, when radiation, asbestos, chemicals are handled, and when subjective symptoms are present, The detection level may be changed so as to increase the detection rate.

Further, when a family member or relative has a disease caused by an abnormal shadow such as a cancer, the subject patient to be subjected to the abnormal shadow candidate detection processing at this time also has
Because there is a high risk of developing such abnormal shadows epidemiologically, the detection level was changed to increase the detection rate of abnormal shadow candidates, and for past medical history information, for example, the history of treating abnormal shadows in the past In some cases, since there is a high possibility of recurrence, the detection level is changed so as to increase the detection rate of abnormal shadow candidates, and for palpation information, for example, in the palpation examination of a doctor or the like, if there is clotting, Since the coagulation supports the existence of the abnormal shadow, the detection level may be changed so as to increase the detection rate of the abnormal shadow candidate.

If the content of the prior information is to increase the possibility of the presence of an abnormal shadow, and if the detection level is changed so as to increase the detection rate of the abnormal shadow candidate, on the contrary, When the content of the advance information is to reduce the possibility of the presence of the abnormal shadow, a mode of changing the detection level so as to lower the detection rate of the abnormal shadow candidate may be added.

In the case of special circumstances such as the above-mentioned palpation information in which the location of the coagulation can be specified to some extent, the detection level may be changed for only a part of the image, or the detection level may be changed for each different part. You may change the level.

As a subject image in the abnormal shadow candidate detection method of the present invention, it is highly practical to apply mammography or the like, which is a radiographic image of the breast obtained by taking a breast cancer screening. However, the present invention is not necessarily limited to mammography, and may be applied to, for example, detection of abnormal shadow candidates in the chest and the like.

As a method for detecting an abnormal shadow candidate, for example, it is preferable to apply the detection method using the above-described abnormal shadow candidate detection system (Japanese Patent Laid-Open No. 8-294479). This abnormal shadow candidate detection method automatically selects a tumor shadow candidate (an aspect of an abnormal shadow candidate) in an image by an iris filter process that evaluates the degree of concentration of a density (signal value) gradient vector of a digital image signal representing an image. And morphological operations on digital image signals (dilation, erosion, opening, closing, etc.)
, The algorithm automatically detects a microcalcification shadow candidate (an aspect of an abnormal shadow candidate), and performs a threshold (e.g., an output of an iris filter process to be described later) at the time of evaluating the degree of concentration of the density gradient vector. The detection level can be changed by changing the threshold value T1 for comparing and contrasting the value I, and the size of the structural element in the morphological operation is changed, or the threshold value at the time of the malignancy evaluation after the morphological operation process ( The detection level can be changed by changing threshold values T2, T3, and the like in Expression (17) described later. Of course, various detection methods other than the abnormal shadow candidate detection method may be applied.

In the iris filter processing, Obata et al. Of Tokyo University of Agriculture and Technology reported “Detection of tumor shadow in DR images (iris filter)” (Transactions of the Institute of Electronics, Information and Communication Engineers, D-II Vol. J75-D-).
II No.3 P663-670 March 1992), which has been studied as an effective method to detect candidates for tumor shadows, which is one of the characteristic features of breast cancer. The target image is not limited to such a tumor shadow in mammography, but may be applied to any image portion where the gradient of the image signal (density etc.) representing the image is concentrated. This is a possible detection process.

The outline of the process of detecting a tumor shadow candidate using the iris filter will be described below.

For example, in a radiographic image (an image represented by a high-density high-signal level image signal) on an X-ray film, it is generally known that the density of a tumor shadow is slightly lower than that of a surrounding image portion. The density value distribution has a density value gradient such that the density value decreases from the substantially circular periphery toward the center. Therefore, in the tumor shadow, a local gradient of the density value is recognized, and the gradient line is concentrated toward the center of the tumor.

The iris filter calculates the gradient of the image signal represented by the density value as a gradient vector, and outputs the degree of concentration of the gradient vector. Based on this, a candidate for a tumor shadow is detected.

That is, for example, in the mammography P as shown in FIG. 5A, the gradient vector at an arbitrary pixel in the tumor shadow PJ is directed to the vicinity of the center of the tumor shadow as shown in FIG. In the case of an elongated shadow PK such as a mammary gland, the gradient vector does not concentrate on a specific point as shown in FIG. If the extracted region is extracted, it becomes a candidate considered as a tumor shadow. It should be noted that, as shown in FIG. 4D, with regard to a shadow PL in which elongated shadows such as a mammary gland intersect, the gradient vector tends to concentrate on a specific point, and may be erroneously detected as a candidate for an abnormal shadow. The above is the basic concept of the iris filter processing. The specific algorithm steps are shown below.

(Step 1) Calculation of Gradient Vector For all the pixels constituting the target image, the direction θ of the gradient vector of the image data is calculated for each pixel j based on the following formula (1). Ask.

[0025]

(Equation 1) Here, as shown in FIG. 6, f _{1 to} f ₁₆ are pixel values (image data) corresponding to pixels on the outer periphery of the mask of 5 × 5 pixels centering on the pixel j.

(Step 2) Calculation of Concentration of Gradient Vector Next, for all the pixels constituting the target image,
For each pixel, the degree of concentration C of the gradient vector having that pixel as the pixel of interest is calculated according to the following equation (2).

[0027]

(Equation 2) Here, N is the number of pixels existing in a circle having a radius R around the pixel of interest, θj is a straight line connecting the pixel of interest and each pixel j in the circle, and the above equation (1) for each pixel j (See FIG. 7). Therefore, the degree of concentration C represented by the above equation (2) becomes a large value when the direction of the gradient vector of each pixel j is concentrated on the target pixel.

By the way, the gradient vector of each pixel j in the vicinity of the tumor shadow is determined regardless of the contrast of the tumor shadow.
Since the pixel of interest is directed substantially toward the center of the tumor shadow, the pixel of interest having a large value of the degree of concentration C can be said to be the pixel at the center of the tumor shadow. On the other hand, in the shadow of a linear pattern such as a blood vessel, the value of the degree of concentration C is small because the direction of the gradient vector is biased in a certain direction. Therefore, the value of the degree of concentration C with respect to the pixel of interest is calculated for each of all the pixels constituting the image, and whether or not the value of the degree of concentration C exceeds a predetermined threshold value is evaluated. Can be detected. In other words, this filter is less susceptible to blood vessels and mammary glands than a normal differential filter,
It has the feature that tumor shadows can be detected efficiently.

Further, in the actual processing, in order to achieve a detection power independent of the size and shape of the tumor, a device for adaptively changing the size and shape of the filter is used. FIG. 8 shows the filter. This filter differs from the filter shown in FIG.
The evaluation of the degree of concentration is performed only by pixels on a radial line in M kinds of directions at every / M degrees (in FIG. 8, 32 directions at every 11.25 degrees are exemplified).

Here, the coordinates ([x], [y]) of the n-th pixel on the i-th line and from the pixel of interest are as follows if the coordinates of the pixel of interest are (k, l): Equation (3),
Given by (4).

[0031]

(Equation 3) Here, [x] and [y] are the maximum integers not exceeding x and y.

Further, the output value up to the pixel where the maximum concentration is obtained for each line on the radial line is defined as the concentration Cimax in that direction, and the concentration Cimax is averaged in all directions to obtain the attention. The degree of concentration C of the gradient vector group for the pixel is set.

Specifically, first, the degree of concentration Ci (n) obtained from the target pixel to the n-th pixel on the i-th radial line is obtained by the following equation (5).

[0034]

(Equation 4) That is, equation (5) is based on the target pixel as the starting point and the end point as R
The concentration Ci (n) is calculated within the range from min to Rmax. Here, Rmin and Rmax are the minimum and maximum values of the radius of the tumor shadow to be extracted.

Next, the concentration C of the gradient vector group is calculated by the following equations (6) and (7).

[0036]

(Equation 5) Here, since Cimax in Expression (6) is the maximum value of the concentration Ci (n) for each radial line obtained in Expression (5), the concentration Ci (n) is the maximum value from the pixel of interest. The region up to the pixel becomes a candidate region of the tumor shadow in the direction of the line.

Equation (6) is calculated for all the radial lines, the area of the tumor shadow on each line is obtained, and the area of the tumor shadow on each line is connected between adjacent lines by a straight line or a nonlinear curve. The shape of the outer peripheral edge of a region that can be a candidate for a tumor shadow can be specified.

In the equation (7), the maximum value Cimax of the degree of concentration given by the equation (6) in this area is calculated for all directions of the radial line (the equation (7) exemplifies the case of 32 directions). Find the average value. The obtained value is the output value I of the iris filter processing, and this output value I is compared with a predetermined fixed threshold value T1 suitable for determining whether or not the shadow is a tumor, and I ≧ T1 If (or I> T1), the area around the target pixel is an abnormal shadow candidate (tumor shadow candidate), and I <T1 (or I ≦ T1).
If 1), it is determined that it is not a tumor shadow candidate.

The manner in which the size and shape of the region for evaluating the degree of concentration C of the gradient vector group in equation (7) adaptively changes in accordance with the distribution of the gradient vector is enlarged in accordance with the brightness of the outside world. Since the iris of the human eye that shrinks is similar to the appearance, the above-described method of detecting a candidate region of a tumor shadow using the degree of concentration of the gradient vector is referred to as iris filter processing. I have.

The above-mentioned calculation of the degree of concentration Ci (n) may use the following equation (5 ') instead of equation (5).

[0041]

(Equation 6) That is, equation (5 ') calculates the degree of concentration Ci (n) from the pixel corresponding to the minimum value Rmin of the radius of the tumor shadow to be extracted to the end point within the range from Rmin to Rmax. .

According to the above-described steps, the iris filter can effectively detect only a tumor shadow of a desired size from a radiographic image.

By the way, in general, the shadow of a malignant mass has the following morphological characteristics: 1) its edge is irregular; 2) it has a shape close to a circle; and 3) the inside has an uneven density distribution. I have.

Therefore, for a more definitive diagnosis, the image signal of the abnormal shadow candidate obtained by the comparison process between the iris filter output value I and the threshold value T1 described above is formed into a shape in consideration of these characteristics. The determination may be further performed. The feature amounts used here are the spread degree (Spreadness), the elongation (Elongation), the roughness of the edge (Roughness), the circularity (Circularity), and the inner roughness (Entropy). The final determination as to whether or not the candidate is a tumor shadow may be made by comparing with another predetermined threshold T2 set in advance.
The additional detection processing such as the detection processing based on the morphological features is not the iris filter processing itself, but is applied to the abnormal shadow candidate detection processing by the iris filter processing to detect the abnormal shadow candidate. Since this is a process, these additional detection processes are also included in the iris filter process and are referred to as “process based on the iris filter process”.

On the other hand, the morphological operation processing is a method of detecting a candidate for a microcalcification image, which is a characteristic form of breast cancer, together with a tumor shadow, and uses a multi-scale λ and a structural element (mask) B, and (1) It is effective in extracting the calcified image itself, (2) is not easily affected by complicated background information, and (3) the extracted calcified image is not distorted. In other words, this method is different from general differential processing in that the size, shape,
It is possible to perform detection while better maintaining geometric information such as a density distribution. The outline is described below.

(Basic Operation of Morphology) Morphological operation processing is generally developed as a set theory in an N-dimensional space. However, for intuitive understanding, a description will be given of a two-dimensional grayscale image.

A point at coordinates (x, y) is represented by a density value f
It is regarded as a space having a height corresponding to (x, y). Here, the density value f (x, y) is a high-luminance high-signal level signal which becomes a larger image signal as the density is lower (the brightness is higher when displayed on a CRT).

First, for simplicity, consider a one-dimensional function f (x) corresponding to the cross section. The structuring element g used in the morphological operation processing is a symmetric function symmetric about the origin as shown in the following equation (8).

(Equation 7) And the value is 0 in the domain, and the domain is represented by the following equation (9).

[0049]

(Equation 8) At this time, the basic form of the morphological operation is expressed by equations (10) to (1).
As shown in 3), this is a very simple operation.

[0050]

(Equation 9) That is, the dilation process is a process of searching for a maximum value within a range of ± m (a value determined according to the structural element B) around the target pixel (FIG. 9).
On the other hand, the erosion process is a process of searching for a minimum value within a width of ± m around the pixel of interest (see FIG. 9B). The opening processing corresponds to searching for the maximum value after searching for the minimum value, and the closing processing corresponds to searching for the minimum value after searching for the maximum value.
The opening process is equivalent to smoothing the density curve f (x) from the low brightness side and removing a convex density variation portion (a portion higher in brightness than the surrounding portion) that fluctuates in a space narrower than the mask size 2 m. (See FIG. 9C).
On the other hand, in the closing processing, the density curve f
This corresponds to smoothing (x) and removing a concave density variation portion (a portion having a lower luminance than the surrounding portion) which fluctuates in a space narrower than the mask size of 2 m (FIG. 9D).
reference).

Here, in the case of a high-density high-signal level signal in which the higher the density, the higher the density, the density value f
Since the magnitude relationship is reversed with respect to the case where the image signal value of (x) is at the high luminance and high signal level, the dilation processing on the signal at the high density and high signal level is performed by the erosion processing at the high luminance and high signal level (see FIG. B)), the erosion processing for the signal with the high density and high signal level is performed by the dilation processing at the high luminance and high signal level (FIG. 9).
(A)), the opening process for a signal with a high density and high signal level matches the closing process for a signal with a high luminance and high signal level (FIG. 9D), , And the opening process at the high luminance and high signal level (FIG. 9C). In this section, a case of an image signal (luminance value) at a high luminance and high signal level will be described.

(Application to Calculated Shading Detection) For the detection of calcified shadows, a difference method of subtracting a smoothed image from an original image can be considered. It is difficult to distinguish between calcified shadows and elongated non-calcified shadows (e.g., mammary glands, blood vessels, and mammary gland supporting tissues) using a simple smoothing method. Obata et al. We propose a morphological operation represented by equation (14) ("Extraction of microcalcification image by morphological filter using multiple structural elements" IEICE Transactions D-II Vol.J75-D-II No.
7 P1170-1176, July 1992, "Basics of Morphology and Its Application to Mammogram Processing" MEDICALIMAGING TEC
HNOLOGY Vol.12 No.1 January 1994).

[0053]

(Equation 10) Here, Bi (i = 1, 2, 3, 4) are four linear structural elements B shown in FIG. If the structural element B is set to be larger than the calcified shadow to be detected, the calcification is a convex signal change portion (an image portion that fluctuates in a narrow spatial range) finer than the structural element B in the opening process. The image is removed. On the other hand, the elongated non-calcified shadow has a longer length than the structural element B, and if its inclination (extending direction) matches any of the four structural elements Bi, the opening process (the equation (14)) Even if the two-term operation is performed, it remains as it is. Therefore, by subtracting the smoothed image (image from which the calcified shadow has been removed) obtained by the opening process from the original image f, an image including only small calcified image candidates is obtained. This is the idea of equation (14).

As described above, in the case of a signal having a high density and a high signal level, the calcified shadow has a lower density value than the surrounding image portion, and the calcified shadow has a concave signal change with respect to the surrounding portion. Therefore, the closing process is applied instead of the opening process, and the expression (15) is applied instead of the expression (14).

[0055]

[Equation 11] However, even in this case, some non-calcified shadows having the same size as the calcified shadow may remain. In such a case, the differential information based on the morphological operation of the following equation (16) is used. The non-calcified image included in P in equation (14) is further removed.

[0056]

(Equation 12) Here, as the value of Mgrad is larger, the possibility of calcification shadow is larger, and the calcification candidate image Cs can be obtained by the following equation (17).

[0057]

(Equation 13) Here, T2 and T3 are preset thresholds determined experimentally.

However, for non-calcified shadows different from the size of the calcified shadow, P in equation (14) and a predetermined threshold T2
In the case where a non-calcified shadow having the same size as the calcified shadow does not remain since it can be removed only by comparing with the condition (P (i, j) .Gtoreq.T2).

As described above, the morphological operation processing is described for the case of image data having a high luminance and a high signal level. ), The opening operation and the closing operation have the opposite relationship.

In the morphology calculation process, an additional detection process similar to the additional detection process such as the detection process based on the morphological features in the iris filter process may be further applied. Such additional detection processing is also included in the morphology calculation processing, and is referred to as “processing based on morphology calculation processing”.

The second abnormal shadow candidate detecting method of the present invention comprises:
In the abnormal shadow candidate detection method for detecting an abnormal shadow candidate in an image based on an image of a subject, a detection level of the abnormal shadow candidate is changed according to imaging conditions at the time of acquiring the image by radiation imaging. It is characterized by the following.

The method of detecting the image of the subject, the abnormal shadow candidate, and the partial change of the detection level applied here are the same as the above-described first abnormal shadow candidate detecting method of the present invention.

It is well known that changing the detection level in accordance with the photographing conditions means that the image quality of the image obtained by the photographing changes in accordance with the photographing conditions. Images are more difficult to detect high spatial frequency structures (eg, microcalcifications, etc.) than images obtained at higher doses, depending on individual imaging conditions. Since the detection level of the abnormal shadow candidate changes relatively, when the imaging condition is such that the detection of the abnormal shadow candidate is relatively reduced, the detection rate of the abnormal shadow candidate is increased. It means to change the detection level. In this case as well, if the photographing condition changes the detection level so as to increase the detection rate of the abnormal shadow candidate when the photographing condition is to lower the detection rate of the abnormal shadow, on the contrary, the photographing condition becomes abnormal. When the detection rate of the shadow is to be increased, a mode may be added in which the detection level is changed so as to lower the detection rate of the abnormal shadow candidate.

The imaging conditions include, for example, tube voltage and tube current of a radiation source for irradiating a subject, radiation irradiation time,
The product of the tube current and the irradiation time (mAs value), the degree of compression at the time of compression shooting of the subject (particularly, the compression pressure when the subject is a breast, the thickness at the time of compression, etc.), the prevention of scattered radiation For use, the type of grid to be used, the enlargement ratio in the case of enlarged photographing, or a combination of these.

A first abnormal shadow candidate detecting system according to the present invention is an apparatus for implementing the first abnormal shadow candidate detecting method according to the present invention, wherein an abnormal shadow candidate in an image based on a subject image is provided. In an abnormal shadow candidate detection system including an abnormal shadow candidate detection unit that detects a candidate, a prior information receiving unit that receives an input of prior information about the subject, and an a priori information input to the prior information receiving unit. A detection level changing unit for changing a detection level of the abnormal shadow candidate by the abnormal shadow candidate detecting unit according to the content, wherein the abnormal shadow candidate detecting unit changes the detection level changed by the detection level changing unit. , The abnormal shadow candidate is detected.

A second abnormal shadow candidate detecting system according to the present invention is an apparatus for implementing the second abnormal shadow candidate detecting method according to the present invention. In an abnormal shadow candidate detection system including an abnormal shadow candidate detection unit that detects a shadow candidate, an imaging condition receiving unit that receives an input of imaging conditions when the image is acquired by radiation imaging, and an input that is input to the imaging condition receiving unit. A detection level changing unit that changes a detection level of the abnormal shadow candidate by the abnormal shadow candidate detecting unit according to the imaging condition, wherein the abnormal shadow candidate detecting unit is changed by the detection level changing unit. The abnormal shadow candidate is detected based on the detected detection level.

In each of the abnormal shadow candidate detection systems according to the present invention, a system for changing the detection level for each part of the image is applied as the detection level changing means. It is preferable to apply a method for detecting an abnormal shadow candidate based on the detection level changed for each case.

[0068]

According to the abnormal shadow candidate detecting method and the abnormal shadow candidate detecting system of the present invention, according to the individual circumstances such as the contents of the prior information about the subject and the photographing conditions.
By changing the detection level of the abnormal shadow candidate, it is possible to perform the processing of detecting the abnormal shadow candidate by using the individual information such as the advance information and the photographing conditions of the subject as a determination material, and to improve the reliability of the detection as compared with the related art. Can be enhanced.

In other words, the individual circumstances described above are such that when a reader such as a physician reads an image detected or not detected by the abnormal shadow candidate detection method / system, the reader himself or herself By interpreting the image taking into account individual circumstances, it was determined whether there was an abnormal shadow candidate, whether the abnormal shadow candidate was benign or malignant, or whether follow-up was necessary. By making the candidate detection process appropriate depending on the individual circumstances, the image reader can perform image interpretation without considering the individual circumstances.

[0070]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, specific embodiments of the abnormal shadow candidate detecting method and abnormal shadow candidate detecting system of the present invention will be described with reference to the drawings.

FIG. 1 is a block diagram showing an embodiment of a first abnormal shadow candidate detection system of the present invention for implementing the first abnormal shadow candidate detection method of the present invention, and FIG. FIG. 1 is a block diagram showing a computer-aided image diagnostic apparatus 20 incorporating a shadow candidate detection system 10. The illustrated computer-aided image diagnostic apparatus 60 receives an input image signal (hereinafter, referred to as an image signal).
A storage means 20 for storing an entire image signal (hereinafter referred to as an entire image signal) S, and an entire image obtained by reading out the entire image signal S stored in the storage means 20 and performing image processing such as gradation processing and frequency processing on the entire signal S The processing unit 30 and the whole image signal S stored in the storage unit 20 are read, and image signals (hereinafter, referred to as local image signals) Sp and Sq representing the abnormal shadow candidates Pp and Pq are detected from the whole image signal S. An abnormal shadow candidate detection system 10, a local image processing unit 40 that emphasizes the abnormal shadow candidates Pp and Pq with respect to the detected local image signals Sp and Sq, and an image processed by the entire image processing unit 30. And an abnormal shadow candidate Pp 'represented by local image (abnormal shadow candidate) signals Sp' and Sq 'after image processing by the local image processing means 40. And pq 'is a configuration that includes a display unit 50 for displaying a visible image.

Here, the whole image signal S input to the computer-aided image diagnostic apparatus 60 is stored in a stimulable phosphor sheet on which a mammography P representing a breast radiation image of a patient as shown in FIG. This is an image signal (a signal of a high density and a high signal level) obtained by photoelectrically reading the stimulated emission light generated by irradiating the excitation light, and thereafter performing digital conversion.

The display means 50 may display the entire image P and the abnormal shadow candidates Pp 'and Pq' on the display surface separately. In the present embodiment, the display means 50 displays the entire image P while displaying the entire image P. Only the image portions of the abnormal shadow candidates Pp and Pq in the whole image P are replaced with the abnormal shadow candidate images Pp ′ and Pq ′ subjected to the image processing by the local image processing means 40 and displayed.

As shown in detail in FIG. 1, the abnormal shadow candidate detection system 10 performs predetermined arithmetic processing for detecting the abnormal shadow candidates Pp and Pq on the entire image signal S.
By comparing and comparing the obtained output value of the arithmetic processing with a preset threshold value T (collectively, T1, T2, and T3 described below), abnormal shadow candidates Pp,
Abnormal shadow candidate detecting means 1 for detecting Pq, prior information receiving means 2 for receiving prior information S1 about the subject, and prior information S1 inputted to prior information receiving means 2
And a detection level changing means 3 for changing the detection level of the abnormal shadow candidate by the abnormal shadow candidate detecting means 1 according to the contents of the above.

The object to be diagnosed by the computer-aided image diagnostic apparatus 60 shown in FIG. 2 is a mammography (whole image) P obtained by a breast cancer screening, and the abnormal shadow is a tumor which is a mode of breast cancer. Shading Pp and microcalcification shading P
q (see FIG. 3).

The detection of the abnormal shadow candidates Pp and Pq by the abnormal shadow candidate detecting means 1 is performed by the iris filter processing described above and the subsequent iris filter output value I and the threshold value T1.
And a microcalcification shadow candidate detection consisting of the morphology calculation processing described above and the threshold processing with thresholds T2 and T3 in equation (17) described above. If the iris filter output value I of the predetermined area is larger than the threshold value T1 (I> T1), the area is detected as a tumor shadow candidate Pp, and if the iris filter output value I is equal to or smaller than the threshold value T1 (I ≦ T1), the area is designated as a tumor shadow candidate Pp
, And the result of the morphological operation P (i,
j) and Mgrad (see equations (15) and (16)) are equal to or greater than thresholds T2 and T3, respectively (P (i, j) ≧ T2 and Mgrad ≧ T3), and the corresponding area is designated as a microcalcification shadow candidate Pq. If the result P (i, j) or Mgrad of the morphological operation is smaller than the threshold values T2 and T3, (P (i,
j) <T2 or Mgrad <T3), a process of not detecting the region as a microcalcification shadow candidate Pq. Therefore, the detection level of these tumor shadow candidates is equal to the threshold T1.
, And the detection level of the microcalcification shadow candidate can be changed by changing the thresholds T2 and T3.

The prior information input to the prior information receiving means 2 includes inquiry information obtained by interviewing the patient, past medical history information, palpation information obtained by palpation examination, and the like. Therefore, the detection level changing unit 3 determines whether the subject to be subjected to the current abnormal shadow candidate detection process should be used when the family or relatives have a disease caused by an abnormal shadow such as cancer. Even in patients, epidemiologically, there is a high risk of developing such abnormal shadows. Therefore, the threshold values T1, T2, and T3 that define the detection levels are increased so as to increase the detection rate of abnormal shadow candidates, and past medical history information is set. For example, if there is a history of treating abnormal shadows in the past, there is a high possibility of recurrence. Therefore, the thresholds T1, T2, and T3 are reduced so as to increase the detection rate of abnormal shadow candidates. For example, in a palpation examination by a doctor or the like, if there is coagulation, the coagulation supports the existence of an abnormal shadow. Therefore, the threshold T1 is set to increase the detection rate of abnormal shadow candidates. Forming a process to reduce the T2, T3.

Next, the operation of the computer-aided image diagnostic apparatus 60 including the abnormal shadow candidate detecting system 10 of the present embodiment will be described.

The whole image signal S representing the mammography P is input to the computer-aided image diagnostic apparatus 60, and the whole image signal S is temporarily stored in the storage means 20. Here, the whole image processing means 30 reads out the whole image signal S stored in the storage means 20, and performs image processing such as gradation processing and frequency processing on the whole image signal S. On the other hand, the abnormal shadow candidate detection system 10 also reads out the entire image signal S stored in the storage unit 20, and the entire image signal S is input to the abnormal shadow candidate detection unit 1.

In the prior information receiving means 2 of the abnormal shadow candidate detection system 10, an inquiry obtained at a stage before the mammography P is photographed, for example, in a consultation room, about a patient having the mammography P taken information,
Prior information S1 such as past medical history information and palpation information obtained by palpation examination by a doctor is input. Note that these pieces of advance information S1 may be input from, for example, an electronic medical chart that digitizes information described in a paper chart.

The prior information S1 inputted to the prior information receiving means 2 is inputted to the detection level changing means 3, and the detection level changing means 3 receives the inputted prior information S1 as described above.
The thresholds T1, T2, and T3 stored in the abnormal shadow candidate detecting means 1 are changed in accordance with the contents of (1). As a result, the detection levels of the tumor shadow candidate Pp and the microcalcification shadow candidate Pq by the abnormal shadow candidate detection means 1 are changed.

The abnormal shadow candidate detecting means 1 comprises a storage means 20
The above-described iris filter processing and morphology calculation processing are separately performed on the entire image signal S read from the multiplexed image signal S, and the output value I and the output value I, which represent the signal Sp of the tumor shadow candidate and the signal Sq of the microcalcification shadow candidate, respectively, P (i,
j), Mgrad is calculated. The calculated output values I and P (i, j), Mgrad are respectively associated with the corresponding threshold values T1, T1 changed by the detection level changing means 3.
The iris filter output value I is compared with T2 and T3.
Is larger than the threshold T1 (I> T1), the area is detected as a signal Sp representing the tumor shadow candidate Pp. If the iris filter output value I is equal to or smaller than the threshold T1 (I ≦ T1), the area is It is not detected as the signal Sp representing the tumor shadow candidate Pp, and the output value P of the morphological operation is
If (i, j) and Mgrad (see equations (15) and (16)) are equal to or greater than thresholds T2 and T3, respectively, then (P (i, j) ≧ T
2 and Mgrad ≧ T3), the region is detected as a signal Sq representing the microcalcification shadow candidate Pq, and if the output value P (i, j) or Mgrad of the morphological operation is smaller than the threshold values T2 and T3, (P (i , J) <T2 or Mgrad <T
3) A signal S representing the microcalcification shadow candidate Pq
Perform processing not detected as q.

The signals Sp and Sq representing the abnormal shadow candidates detected by the abnormal shadow candidate detecting means 1 are respectively input to the local image processing means 40, and the local image processing means 40 processes the input local image signals Sp and Sq. Abnormal shadow candidate P
Image processing is performed so that p and Pq are emphasized, and the processed local image signals Sp ′ and Sq ′ are output.

The whole image signal S ′ obtained by processing by the whole image processing means 30 and the local image signals Sp ′ and Sq ′ obtained by processing by the local image processing means 40 are input to the image display means 50. The image display means 50 displays the whole image P ', and only the image part of the abnormal shadow candidate in the whole image P' is an image Pp ', Pq' of the abnormal shadow candidate which has been image-processed by the local image processing means 40. Replaced with and displayed.

As a result, the image display means 50 displays the image Pp 'and Pq' of the abnormal shadow candidate emphasized from the whole image P ', together with the whole image P', and diagnoses by a doctor or the like. To be served. Then, the displayed abnormal shadow candidate images Pp ′ and Pq ′ are detected by changing the detection level according to the contents of the prior information about the subject. The image can be interpreted without any consideration.

The abnormal shadow candidate detection system 10 of the present embodiment changes the detection level of the abnormal shadow candidate by changing the output value of the iris filter process and the output value of the morphological operation process and the threshold value for comparison. However, the method of changing the detection level is not limited to the method described in the present embodiment. For example, the detection level is changed by changing the size or shape of the structural element in the morphological operation processing. Is also good.

The abnormal shadow candidate detection system 10 of the present embodiment changes the detection level for the entire mammography P. The detection level changing means 3 changes the detection level for each part of the mammography P. The abnormal shadow candidate detecting means 1 may detect the abnormal shadow candidate according to the detection level changed for each part by the detection level changing means 3. In the case where such an aspect is adopted, for example, when there is a special situation such as the presence of the coagulation to some extent as palpation information, by changing the detection level only for a part thereof, a special It is possible to obtain a detection result with due consideration.

FIG. 4 is a block diagram showing an embodiment of the second abnormal shadow candidate detecting system of the present invention which implements the second abnormal shadow candidate detecting method of the present invention. The illustrated abnormal shadow candidate detection system 10 'is a prior information receiving unit 2 of the abnormal shadow candidate detection system 10 of the embodiment shown in FIG.
Of the mammography P at the time of imaging (tube voltage / tube current, radiation irradiation time, mAs value, degree of mammography compression, use / non-use of grid, type of grid used, enlargement ratio of magnified imaging, etc.) In addition to changing to the imaging condition receiving means 2 ', the detection level changing means 3 changes the detection level of the abnormal shadow candidate by the abnormal shadow candidate detecting means 1 according to the imaging condition input to the imaging condition receiving means 2'. The configuration is different from that of the detection level changing means 3 '.

According to the abnormal shadow candidate detection system 10 'constructed as described above, for example, since the mammography P is obtained by imaging with a small irradiation radiation dose, a structure having a high spatial frequency (for example, Even if it is difficult to detect calcified shadows, etc., the detection level of the abnormal shadow candidates such as microcalcified shadow candidates is increased by increasing the detection level of the abnormal shadow candidates relatively according to the individual imaging conditions. Therefore, the image reading can be performed by the image reader without considering the individual imaging conditions.

Note that the abnormal shadow candidate detection system 10 'of the present embodiment uses the computer-aided image diagnostic apparatus 60 shown in FIG.
Can be replaced with the abnormal shadow candidate detection system 10.

[Brief description of the drawings]

FIG. 1 is a schematic block diagram showing an embodiment of a first abnormal shadow candidate detection system of the present invention.

FIG. 2 is a block diagram showing an example of a computer-aided image diagnostic apparatus using the abnormal shadow candidate detection system shown in FIG.

FIG. 3 is a diagram showing a mammography P represented by an image signal S input to the computer-aided image diagnostic apparatus shown in FIG. 3;

FIG. 4 is a schematic block diagram showing an embodiment of a second abnormal shadow candidate detection system of the present invention.

FIG. 5 is a conceptual diagram showing the degree of concentration of a density gradient in mammography.

FIG. 6 is a diagram showing a mask for calculating a gradient vector in iris filter processing.

FIG. 7 is a diagram showing the concept of the degree of concentration of a gradient vector for a target pixel.

FIG. 8 is a conceptual diagram showing an iris filter set so that a contour shape is adaptively changed.

FIG. 9 is a graph illustrating a basic operation of the morphological operation processing;

FIG. 10 is a diagram showing the concept of a structural element used in morphological operation processing.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Abnormal shadow candidate detecting means 2 Prior information receiving means 2 'Imaging condition receiving means 3, 3' Detection level changing means 10 Abnormal shadow candidate detecting system 20 Storage means 30 Overall image processing means 40 Local image processing means 50 Display means 60 Computer support Diagnostic imaging device

 ────────────────────────────────────────────────── ─── Continuing on the front page (72) Inventor Takeshi Okubo Takeshi 798, Miyadai, Kaisei-cho, Ashigara-gun, Kanagawa Prefecture Photo Film Co., Ltd. F-Term (reference) 4C093 AA26 CA18 CA21 CA31 DA06 FF09 FF17 FF18 5B057 AA08 BA30 CF01 CF02 DA02 DB02 DC22

Claims (12)

[Claims] 1. An abnormal shadow candidate detecting method for detecting an abnormal shadow candidate in an image based on an image of a subject, wherein a detection level of the abnormal shadow candidate is changed according to contents of prior information on the subject. A method for detecting an abnormal shadow candidate. 2. The apparatus according to claim 1, wherein the prior information about the subject is at least one of inquiry information obtained by an inquiry, past medical history information, and palpation information obtained by a palpation examination. Abnormal shadow candidate detection method. 3. An abnormal shadow candidate detecting method for detecting an abnormal shadow candidate in an image based on an image of a subject, wherein the abnormal shadow candidate is detected in accordance with an imaging condition at the time of acquiring the image by radiation imaging. An abnormal shadow candidate detection method, characterized by changing a detection level. 4. The imaging condition includes: a tube voltage or a tube current of a radiation source for irradiating the subject; an irradiation time of the radiation; a product of the tube current and the irradiation time; 4. The abnormal shadow candidate detecting method according to claim 3, wherein at least one of the degree of compression, the use / non-use of the grid, the type of the grid used, and the enlargement ratio in the case of the enlargement imaging is used. 5. The abnormal shadow candidate detection method according to claim 1, wherein the detection level is changed for each part of the image. 6. The abnormal shadow candidate detecting method according to claim 1, wherein the image of the subject is a mammography. 7. An abnormal shadow candidate detection system comprising an abnormal shadow candidate detecting means for detecting an abnormal shadow candidate in an image based on an image of the object, wherein prior information receiving receiving input of prior information on the object is performed. Means, and a detection level changing means for changing a detection level of the abnormal shadow candidate by the abnormal shadow candidate detecting means according to the contents of the prior information input to the prior information receiving means, An abnormal shadow candidate detecting system, wherein the abnormal shadow candidate detecting means detects the abnormal shadow candidate based on the detection level changed by the detection level changing means. 8. The apparatus according to claim 7, wherein the preliminary information on the subject is at least one of inquiry information obtained by an inquiry, past medical history information, and palpation information obtained by a palpation examination. Abnormal shadow candidate detection system. 9. An abnormal shadow candidate detection system comprising an abnormal shadow candidate detecting means for detecting an abnormal shadow candidate in an image based on an image of a subject, wherein: It further comprises: a photographing condition receiving unit that receives an input; and a detection level changing unit that changes a detection level of the abnormal shadow candidate by the abnormal shadow candidate detecting unit according to the photographing condition input to the photographing condition receiving unit. An abnormal shadow candidate detection system, wherein the abnormal shadow candidate detection means detects the abnormal shadow candidate based on the detection level changed by the detection level changing means. 10. The imaging condition includes: a tube voltage or a tube current of a radiation source for irradiating the subject; a radiation irradiation time; a product of the tube current and the irradiation time; The abnormal shadow candidate detection system according to claim 9, wherein the system is at least one of a compression degree, whether or not a grid is used, a type of a grid to be used, and an enlargement ratio in the case of enlarged imaging. 11. The detection level changing means changes the detection level for each part of the image,
The abnormal shadow candidate detection means, based on the detection level changed for each part by the detection level change means,
The abnormal shadow candidate detection system according to any one of claims 7 to 10, wherein the abnormal shadow candidate is detected. 12. The abnormal shadow candidate detection system according to claim 7, wherein the image of the subject is a mammography.