WO2016159726A1

WO2016159726A1 - Device for automatically sensing lesion location from medical image and method therefor

Info

Publication number: WO2016159726A1
Application number: PCT/KR2016/003432
Authority: WO
Inventors: 김황남; 손수연; 이석규
Original assignee: 고려대학교 산학협력단
Priority date: 2015-04-01
Filing date: 2016-04-01
Publication date: 2016-10-06
Also published as: KR20160118037A

Abstract

Disclosed is an automatic lesion location sensing device for automatically sensing a lesion location from a medical image. The automatic lesion location sensing device of the present invention comprises: an input unit for receiving an original image collected through a medical instrument; a threshold value setting unit for converting the original image into a gray tone image, and then, setting at least one threshold value on the basis of a brightness value of pixels for forming the gray tone image; an image conversion unit for binarizing the gray tone image into a black and white image on the basis of the at least one threshold value; and a contour processing unit for extracting contours by searching for, from the binarized image, components which are suspected lesions, and then displaying the contours on the original image.

Description

Apparatus and method for automatically detecting the location of a lesion from a medical image

The present invention relates to an apparatus and method for automatically detecting the position of a lesion from a medical image, and more particularly, to an apparatus and a method for automatically detecting a lesion position using a brightness difference in an image obtained through an endoscope.

In general, images obtained through various medical devices are used to examine lesions inside the body. Examples of such images include endoscope images, magnetic resonance imaging (MRI), and computed tomography (CT) images.

'Endoscope' is a medical device that can directly observe the internal organs or the body cavity, and is designed to insert a machine to observe the organs that cannot directly identify the lesion without incision. Depending on the organ, there are esophagus, gastroscopy, duodenum, etc. Depending on the method, there are fiber scope, lens system type, gastric camera, etc.

Magnetic Resonance Imaging (MRI) is a large magnetic cylinder that generates a magnetic field, and then enters the human body to generate high-frequency waves to resonate hydrogen nuclei in the body, thereby measuring the difference in the signal from each tissue. It is a technique to image by reconstructing.

Computed tomography (CT) is a technology that takes a human body into a large circular machine with an X-ray generator and then photographs a cross-section of the human body. Computed tomography (CT) has the advantage that structures are less overlapped than simple X-rays, so structures and lesions can be seen more clearly.In most organs and diseases, the lesions are suspected and need to be examined. When necessary, it is basically a test method. On the other hand, computed tomography (CT) is common to magnetic resonance imaging (MRI) in that cross-sectional images are obtained, but computed tomography (CT) uses X-rays, and magnetic resonance imaging (MRI) uses high frequency in a magnetic field. The difference is that images are acquired by transferring.

As described above, among the methods of examining lesions inside the body by using an image, magnetic resonance imaging (MRI) or computed tomography (CT) images detect the lesion and notify the examination doctor to the site where the lesion is suspected. There is a system that provides guidelines. However, these systems do not exist at the endoscopy.

On the other hand, a technique for automatically detecting pulmonary nodules in computed tomography (CT) has been developed, but it is only accepted as a reference opinion because the accuracy is not high enough.

Accordingly, an object of the present invention is to provide an apparatus and method for automatically detecting a lesion location by detecting a tissue suspected to be a lesion by using brightness differences in a medical image, so as to accurately detect even minute changes that are difficult to visually identify.

In addition, the present invention is to provide an apparatus and method for automatically detecting the position of the lesion to increase the cure rate by initially diagnosing the lesion.

In addition, the present invention provides a criterion for determining whether a lesion can be diagnosed as abnormal tissue, thereby providing an apparatus and method for automatically detecting a lesion location, which can be used as a tool for learning to apprentices who are not familiar with lesion diagnosis. I would like to.

According to an aspect of the present invention, there is provided an apparatus for automatically detecting a lesion location, including: an input unit configured to receive an original image collected through a medical device; A threshold setting unit configured to set at least one threshold based on brightness values of pixels constituting the grayscale image after converting the original image into a grayscale image; An image conversion unit for binarizing the grayscale image into a black and white image based on the at least one threshold value; And an contour processor for searching for components suspected to be lesions from the binarized image, extracting contours, and displaying the contours on the original image.

Preferably, the input unit may receive an original image from the endoscope.

Preferably, the threshold setting unit sets an average value of brightness values for all the pixels constituting the grayscale image as a first threshold value, and then adds at least one additional threshold value based on the first threshold value. Can be set.

Preferably, the threshold setting unit may set an average value of brightness values for pixels having a brightness value greater than the first threshold value among pixels constituting the grayscale image as a second threshold value.

Preferably, the threshold setting unit may set an average value of brightness values for pixels of which the brightness value of the pixels constituting the grayscale image is smaller than the first threshold value to a third threshold value.

Preferably, the image conversion unit binarizes the grayscale image into a black and white image for each of the at least one threshold, and as a result, may generate the same number of black and white images as the number of the thresholds.

Preferably, the contour processing unit extracts the contour for each of the black and white images, and then displays the contours in one original image.

Preferably, the contour processor may store in advance a size range of the contour to be displayed on the original image, and display only the contour within the size range.

On the other hand, the lesion position automatic detection method according to an embodiment of the present invention for achieving the above object comprises the steps of converting the original image collected through the medical device to a grayscale image; Setting at least one threshold value based on brightness values of pixels constituting the grayscale image; Binarizing the grayscale image to a black and white image based on the at least one threshold; And an outline processing step of extracting an outline by searching for components suspected to be lesions from the binarized image and displaying the outline on the original image.

Preferably, the setting of the threshold value may include: setting an average value of brightness values for all pixels constituting the grayscale image as a first threshold value; And setting at least one additional threshold value based on the first threshold value.

Preferably, the setting of the additional threshold value may set an average value of brightness values for pixels whose brightness value is greater than the first threshold value among pixels constituting the grayscale image as a second threshold value.

Preferably, the setting of the additional threshold further includes setting an average value of brightness values for pixels of which the brightness value of the pixels constituting the grayscale image is smaller than the first threshold value as a third threshold value. It may include.

Advantageously, said image converting step may binarize said grayscale image into a black and white image for each of said at least one threshold, resulting in the same number of black and white images as said number of thresholds.

Advantageously, said contour processing step comprises extracting said contour for each of said monochrome images; And displaying the extracted contour lines in the one original image.

Preferably, the contour processing step may display only the contour line within the size range of the preset contour line.

The present invention has an advantage of detecting the boundary lines for the tissues suspected to be lesions by using the brightness difference in the image during the endoscopy, thereby accurately detecting minute changes that are difficult to be examined by the naked eye. In particular, the present invention has the advantage of automatically detecting the position of the bottle, by using a multiple threshold value to reduce the binarization error to obtain accurate results.

For this reason, the present invention can increase the cure rate of the disease by initially diagnosing the lesion, and providing a criterion for whether the lesion can be diagnosed as an abnormal tissue, thereby providing a tool for learning to apprentices who are not familiar with the diagnosis of the lesion. It can be used as. In addition, the present invention is also applied to the capsule endoscope adjusted in vitro during telemedicine, to guide the examination doctor to the tissue that is considered to be a problem or to move to the suspected lesion site, automation of the movement of the capsule endoscope is also possible There is an advantage to this.

1 is a schematic block diagram of an automatic lesion position detection apparatus according to an embodiment of the present invention.

2 is a flowchart illustrating a method for automatically detecting a lesion position according to an embodiment of the present invention.

3 is a schematic process flowchart of the multi-threshold setting process of FIG. 2.

4 is a schematic process flowchart of the image conversion process of FIG. 2.

5 is a diagram illustrating images generated for each processing step according to an embodiment of the present invention.

6 and 7 are diagrams for explaining and explaining the effects of the present invention.

As the invention allows for various changes and numerous embodiments, particular embodiments will be illustrated in the drawings and described in detail in the written description. However, this is not intended to limit the present invention to specific embodiments, it should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention. In describing the drawings, similar reference numerals are used for similar elements.

Terms such as first, second, A, and B may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be referred to as the second component, and similarly, the second component may also be referred to as the first component. The term and / or includes a combination of a plurality of related items or any item of a plurality of related items.

When a component is referred to as being "connected" or "connected" to another component, it may be directly connected to or connected to that other component, but it may be understood that other components may be present in between. Should be. On the other hand, when a component is said to be "directly connected" or "directly connected" to another component, it should be understood that there is no other component in between.

The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting of the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, the terms "comprise" or "have" are intended to indicate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, and one or more other features. It is to be understood that the present invention does not exclude the possibility of the presence or the addition of numbers, steps, operations, components, components, or a combination thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. Terms such as those defined in the commonly used dictionaries should be construed as having meanings consistent with the meanings in the context of the related art and shall not be construed in ideal or excessively formal meanings unless expressly defined in this application. Do not.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. Throughout the specification and claims, when a part includes a certain component, it means that it may further include other components, except to exclude other components unless specifically stated otherwise.

1 is a schematic block diagram of an automatic lesion position detection apparatus according to an embodiment of the present invention. Referring to FIG. 1, the apparatus 100 for automatically detecting a lesion position according to an exemplary embodiment of the present invention may include an input unit 110, a threshold setting unit 120, an image switching unit 130, and an outline processing unit 140. ).

The input unit 110 inputs an original image to the automatic lesion position detecting apparatus 100. In particular, the original image collected through a medical device (eg, endoscope) is received and transmitted to the threshold setting unit 120.

The threshold setting unit 120 sets at least one threshold by analyzing the original image transmitted through the input unit 110. To this end, the threshold setting unit 120 converts the original image, which is a color tone image, to a grayscale image, and then derives brightness values of all pixels constituting the grayscale image. At least one threshold is set based on the brightness values of all the pixels.

First, the threshold setting unit 120 derives an average value of brightness values for all the pixels constituting the grayscale image based on Equation 1, and sets the value as a first threshold.

[Equation 1]

Threshold = ΣValue _gray _(i) / Number of Pixels

In this case, ΣValue _{gray (i)} is the sum of brightness values for all the pixels constituting the grayscale image, Number of Pixels is the number of all pixels, and the first threshold obtained by Equation 1 is the corresponding frame. It is the standard for normal tissue in Esau.

In addition, the threshold setting unit 120 may further set additional thresholds based on the first threshold. This is to reduce an error that may occur when the corresponding image is binarized with only one first threshold. For example, if the illuminated area within a frame is concentrated in one place, an error may occur when the difference between abnormal tissue and normal tissue is determined by one reference value.

Therefore, in order to reduce such an error, the threshold value setting unit 120 includes an average value (aka second threshold value) for a portion of the illumination that is illuminated in addition to the first threshold value, and an average value (aka 3 thresholds). The second threshold may be derived from Equation 2, and the third threshold may be derived from Equation 3.

[Equation 2]

Threshold _bright ₌ ΣValue _bright _(i) / Number of bright Pixels

In this case, Threshold _bright is an average value for the illuminated area, ΣValue _{bright (i)} is the sum of brightness values of pixels having a brightness value greater than the first threshold value, and Number of brightPixels is the first threshold value. This is the number of pixels with a brightness value greater than the value.

[Equation 3]

Threshold _dark = ΣValue _dark _(i) / Number of _dark Pixels

In this case, Threshold _dark is an average value for a non-illuminated area, ΣValue _{dark (i)} is a sum of brightness values of pixels having a brightness value smaller than a first threshold value, and Number of darkPixels is a first value. The number of pixels having a brightness value less than a threshold.

The image converting unit 130 binarizes the grayscale image into a black and white image based on at least one threshold set by the threshold setting unit 120.

For example, when binarization is performed based on the first threshold value generated by the threshold setting unit 120, the image conversion unit 130 may adjust brightness values of all pixels constituting the grayscale image and the first threshold value. Comparing the values, the brightness value of a pixel having a brightness value greater than the first threshold value is set to 10, and the brightness value of a pixel having a brightness value less than the first threshold value is set to 0, thereby making the grayscale image monochrome. Convert to an image.

The image conversion unit 130 generates the black and white image by performing the binarization process on each of the one or more threshold values generated by the threshold setting unit 120. Therefore, the number of monochrome images also varies according to the number of thresholds. When the threshold value setting unit 120 sets the first to third threshold values as in the above example, the image conversion unit 130 generates a black and white image for each of the first to third threshold values, Black and white images will be generated.

The contour processor 140 searches for components suspected of lesions from the black-and-white image (that is, the binarized image) generated by the image conversion unit 130, extracts the contour, and displays the contour on the original image.

Here, the component refers to a white interior region at the points where black and white abut on the binarized image, and the contour processor 140 extracts a contour corresponding to the edge of the white interior region.

When one or more black and white images are generated in the image conversion unit 130 as in the above example, the contour processing unit 140 extracts the contours for each of the one or more black and white images and then integrates the contours into one original image. It is preferable to display.

In this case, the method of extracting the contour scans the brightness values of the pixels constituting the image sequentially, and if the brightness values differ from the surrounding pixels, the corresponding pixels are displayed and they are connected to express the boundary line. Can be used.

At this time, the method of extracting the contour by the above method is only one embodiment, the contour extraction method of the present invention is not limited to the above-described method. That is, a method of extracting the contours of the components may apply various known techniques.

On the other hand, the contour processing unit 140 preferably stores in advance the size range of the contour line to be displayed on the original image, and displays only the contour line within the size range.

At this time, the contour processing unit 140 may determine the length of the longest straight line that passes through the center of the contour of the closed curve and meets the closed curve as the size of the corresponding contour.

In addition, the outline processor 140 may display the outline on the original image only when the size of the outline is within the pre-stored size range. This is difficult to see as a lesion for components that are too large, and those components that are too small are areas where the mucosal surface reflects light, which is difficult to see as lesions. To this end, the size range of the contour is preferably set in advance by a person skilled in the art, the examination doctor may adjust the size of the lesion to be detected directly.

2 is a flowchart illustrating a method for automatically detecting a lesion position according to an embodiment of the present invention. 1 and 2 is a method for automatically detecting a lesion position according to an embodiment of the present invention.

First, in step S100, it is determined whether an original image (ie, a medical image) collected from a medical device is input to the input unit 110. For example, it is determined whether the image acquired through the endoscope is input to the input unit 110.

In operation S200, the threshold setting unit 120 converts the original image input in operation S100 into a grayscale image. Meanwhile, FIG. 5 is a diagram illustrating images generated by processing steps according to an embodiment of the present invention. FIG. 5A illustrates an original image (ie, a color tone image) input in step S100. (B) shows the image converted into a grayscale image in step S200. Regions indicated in white in FIG. 5B are components.

In operation S300, the threshold setting unit 120 sets at least one threshold value (ie, multiple threshold values) based on brightness values of pixels constituting the grayscale image. Since an example of a more specific process for this is illustrated in FIG. 3, the specific process will be described with reference to FIG. 3.

In operation S400, the image conversion unit 130 binarizes the grayscale image into a monochrome image based on at least one threshold value set in operation S300. In this case, the binarization process is repeated by the threshold number set in step S300, and the number of black and white images generated as the result is the same as the threshold number. 5 (c) and (d) illustrate the result of binarizing the grayscale image illustrated in FIG. 5 (b) with different threshold values. At this time, FIG. 5C shows a result of binarization by the first threshold, and FIG. 5D shows a result of binarization by the second or third threshold. Referring to FIGS. 5C and 5D, FIG. 5D shows that the binarization result is more accurate than that of FIG. 5C. Therefore, it can be seen that as the number of thresholds increases, an error occurring in the binarization process can be reduced. Meanwhile, since an example of a more specific process for this is illustrated in FIG. 4, the specific process will be described with reference to FIG. 4.

In operation S500, the contour processing unit 140 searches for components suspected of lesions from the binarized image and extracts contours. At this time, the contour extraction process is repeated by the number of monochrome images generated in step S400. That is, a contour extraction process is performed on each of the black and white images generated in step S400.

In step S600, the contour processing unit 140 displays the contour line extracted in the step S500 on the original image. When the outlines are extracted from the one or more black and white images in step S500, the respective outlines are displayed by integrating the respective outlines into one original image. Meanwhile, in step S600, not all the contour lines extracted in step S500 may be displayed, but only the contour lines satisfying a predetermined condition (eg, size) may be displayed. FIG. 5E shows an example of the contour extracted from the black and white image illustrated in FIG. 5D, and FIG. 5F shows the above image on the original image illustrated in FIG. 5A. The example which displayed the outline illustrated in (e) of 5 is shown.

3 is a schematic process flowchart of the multi-threshold setting process of FIG. 2. 1 and 3, the multi-threshold setting process S300 of FIG. 2 is as follows.

First, in step S310, the threshold value setting unit 120 extracts brightness values for all pixels constituting the grayscale image as illustrated in FIG. 5B.

In operation S320, the threshold setting unit 120 sets the average value of the brightness values for all the pixels as the first threshold value.

In operation S330, brightness values of pixels (eg, pixels whose brightness value is greater than the first threshold value) satisfying the first criterion set by the threshold value setting unit 120 based on the first threshold value. The average value of is set to a second threshold value.

In operation S340, brightness values of pixels (eg, pixels whose brightness value is smaller than the first threshold value) satisfying the second criterion set by the threshold value setting unit 120 based on the first threshold value. The average value of is set to a third threshold value.

To this end, the threshold setting unit 120 operates as described in the description with reference to FIG. 1 and preferably derives the first to third threshold values based on Equations 1 to 3 below.

At this time, the example of FIG. 3 shows an example of a process for deriving the first to third threshold values, but the present invention is limited to deriving the first to third threshold values. It is not. For example, only the first and second two thresholds may be derived and the subsequent process may be processed based on those values. However, when the threshold value is more than three, a problem arises in that efficiency decreases in computation speed and accuracy. Therefore, it is preferable to set the maximum threshold value to three.

4 is a schematic process flowchart of the image conversion process of FIG. 2. 1 and 4, the image conversion process S400 of FIG. 2 is as follows.

First, in step S410, the image conversion unit 130 performs binarization based on the first threshold value. For this reason, in step S410, the grayscale image generated by the threshold setting unit 120 is converted into the first black and white image.

In operation S420, the image conversion unit 130 performs binarization based on the second threshold value. For this reason, in step S420, the grayscale image generated by the threshold setting unit 120 is converted into a second black and white image.

In operation S430, the image switching unit 130 performs binarization based on the third threshold value. For this reason, in operation S430, the grayscale image generated by the threshold setting unit 120 is converted into a third monochrome image.

That is, the image conversion process S400 binarizes the grayscale image into a black and white image for each of the at least one threshold, and as a result, generates the same number of black and white images as the number of the thresholds.

6 and 7 are diagrams for explaining the effects of the present invention, and are diagrams for explaining the results of performance evaluation by applying the present invention step by step.

FIG. 6A illustrates an example of a case in which a specialist examiner displays a lesion area A on an endoscope image, and FIG. 6B illustrates a first threshold derived by Equation 1. After performing the binarization on the basis of Figure 2 shows an example in which only the resulting contour is displayed on the original image, Figure 6 (c) is derived after performing the binarization based on the first threshold (Threshold) An example of a case in which the extracted contour is integrated and displayed in the original image after binarization is performed based on the generated contour and the second threshold _bright generated additionally is shown.

Referring to FIG. 6B, the lesion area A indicated by the specialist doctor in FIG. 6A is not indicated by an outline. This is due to an error caused by the initial threshold being set to the average brightness value as the camera lights are concentrated in the lower right corner of the original image.

Referring to FIG. 6C, an outline B similar to the lesion area A indicated by the specialist medical doctor is displayed in FIG. 6A. This indicates that a more accurate result can be obtained by performing additional binarization once more based on the second threshold _bright derived by Equation 2 and then adding the derived contour.

An example of numerically expressing this effect is illustrated in FIG. 7. In FIG. 7, a first stage (1 ^st stage) is a stage when only an average threshold value (ie, a first threshold value) is applied, and a second stage (2 ^nd stage) is a second threshold value. _bright ) If necessary, a third threshold (Threshold _dark ) can be added to proceed to the third stage (3 ^rd stage), but as shown in FIG. proceeds up to value (Threshold _bright) a second step of adding (2 ^nd stage).

The result generated in the first stage of FIG. 7 (1 ^st stage) is shown in FIG. Referring to FIG. 6B and FIG. 7 together, the boundary lines shown in FIG. 6B are suspected lesions due to the difference in brightness with the surroundings even though there are no lesions. This is defined as 'false positive' because it is considered to be positive (lesion). On the other hand, since the actual lesion site was not detected as a lesion, this part is 'false negative' which judges the positive (lesion) as negative (normal).

On the other hand, the results of the second stage (2 ^nd stage) of Figure 7 is illustrated in Figure 6 (c). Referring to FIG. 6C and FIG. 7 together, at this stage, the portion suspected of the lesion, that is, the false positive, increased to eight even though the lesion was not a lesion. And since the actual lesion is detected as a lesion, it is defined as 'true positive'. As a result, 'false negative' has been removed.

As described above, the present invention may apply multiple thresholds to detect and display all the parts suspected of being lesions, thereby reducing the probability of missing the lesions by guiding the examination to an unsuccessful doctor and a trained trainee. That is, removing the 'false negative' which is not recognized as a lesion even though it is a lesion is a top priority of the present invention. Referring to FIGS. 6 and 7, it can be confirmed that this goal has been achieved through setting of multiple thresholds.

Meanwhile, the above-described embodiments of the present invention can be written as a program that can be executed in a computer, and can be implemented in a general-purpose digital computer that operates the program using a computer-readable recording medium.

The computer-readable recording medium may include a magnetic storage medium (eg, a ROM, a floppy disk, a hard disk, etc.) and an optical read medium (eg, a CD-ROM, DVD, etc.).

So far I looked at the center of the preferred embodiment for the present invention.

Those skilled in the art will appreciate that the present invention can be implemented in a modified form without departing from the essential features of the present invention. Therefore, the disclosed embodiments should be considered in descriptive sense only and not for purposes of limitation. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the scope will be construed as being included in the present invention.

Claims

An input unit for receiving an original image collected through a medical device;

A threshold setting unit configured to set at least one threshold based on brightness values of pixels constituting the grayscale image after converting the original image into a grayscale image;

An image conversion unit for binarizing the grayscale image into a black and white image based on the at least one threshold value; And

And a contour processing unit for extracting contours by searching for components suspected as lesions from the binarized image and displaying the contours on the original image.
The method of claim 1, wherein the input unit

Lesion position automatic detection device, characterized in that receiving the original image from the endoscope.
The method of claim 1, wherein the threshold setting unit

After setting the average value of the brightness values for all the pixels constituting the grayscale image as a first threshold value, further setting at least one additional threshold value based on the first threshold value Sensing device.
The method of claim 3, wherein the threshold setting unit

And automatically setting an average value of brightness values for pixels having a brightness value greater than the first threshold value among pixels constituting the grayscale image as a second threshold value.
The method of claim 4, wherein the threshold setting unit

And an average value of brightness values of pixels of which the brightness value of the pixels constituting the grayscale image is smaller than the first threshold value is set to a third threshold value.
The method of claim 1, wherein the image conversion unit

And binarizing the grayscale image into a black-and-white image for each of the at least one threshold, thereby generating the same number of black-and-white images as the number of thresholds.
The method of claim 6, wherein the contour processing unit

And automatically extracting the contour line from each of the black and white images and displaying the contour lines in one original image.
According to claim 1, wherein the contour processing unit

And a size range of the contour to be displayed in the original image in advance, and display only the contour within the size range.
Converting the original image collected by the medical device into a grayscale image;

Setting at least one threshold value based on brightness values of pixels constituting the grayscale image;

Binarizing the grayscale image to a black and white image based on the at least one threshold; And

And a contour processing step of extracting contours by searching for components suspected to be lesions from the binarized image and displaying the contours on the original image.
The method of claim 9, wherein the threshold setting step

Setting an average value of brightness values for all pixels constituting the grayscale image as a first threshold value; And

And further setting at least one additional threshold value based on the first threshold value.
The method of claim 10, wherein the additional threshold setting step

And automatically setting an average value of brightness values for pixels having a brightness value greater than the first threshold value among pixels constituting the grayscale image as a second threshold value.
The method of claim 11, wherein the additional threshold setting step

And automatically setting an average value of brightness values of pixels of the grayscale image, the brightness values of which are smaller than the first threshold value, to a third threshold value. .
The method of claim 9, wherein the image conversion step

And binarizing the grayscale image into a black and white image for each of the at least one threshold value, and thereby generating the same number of black and white images as the number of the threshold values.
The method of claim 13, wherein the contour processing step

Extracting the contour for each of the black and white images; And

And automatically displaying the extracted contour lines in the one original image.
The method of claim 1, wherein the contour processing step

Automatic detection of lesion position, characterized in that for displaying only the contour line within the size range of the preset contour line.