KR101293728B1 - Detecting method of wounded portion of skin and the recorded medium thereof - Google Patents

Abstract

PURPOSE: A skin wound detection method and a recording medium thereof are provided to accurately observe skin wounds by detecting skin wounds including tiny wounds while conducting an autopsy. CONSTITUTION: A pretreatment is prepared (ST-110). The visibility of a photographed image is improved and the image with the improved visibility is stored (ST-120). A binary image is stored from the image with the improved visibility (ST-130). A calculation image is stored by processing pixels of the binary image (ST-140). Some of the photographed image pixels are converted into black. [Reference numerals] (ST-110) Pretreatment process; (ST-120) Visibility improving process; (ST-130) Detection process; (ST-140) Calculation process

Description

Detecting Method Of Wounded Portion Of Skin And The Recorded Medium Thereof}

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for detecting a wound site of the skin and a recording medium thereof, and more particularly, to a wound site remaining on a body from a photograph of a body, particularly a skin that can detect minute wounds that can be overlooked by visual observation. A wound site detection method and a recording medium thereof.

In forensics, an autopsy is a dissection of the dead body and its organs and structures, in which the forensic examiner visually examines the appearance of the trauma in the first stage and examines the inside and the organs in detail during the autopsy. . And then it is common to do more detailed examinations, including microscopic examination of cells and tissues.

Examination of the trauma is called the optometrist. In the optometry, the body is taken with a digital camera while the body is examined by the naked eye. If a wound is observed on the skin at the time of shooting, take a closer look at the wound. However, in the process of visual inspection, it is possible to overlook a minute wound such as a needle puncture wound, and even when observed at the optometrist through the photograph, the minute wound is not observed well, so there is a possibility of missing the opportunity to examine the minute wound in detail. There was. And there is a problem that can not be assured the detailed observation of the minute wound because the proposal to solve such a concern is not made.

Korean Patent Publication No. 10-2012-0074924

SUMMARY OF THE INVENTION The present invention has been proposed to solve the above-mentioned problem, and an object thereof is to provide a method for detecting a wound site of the skin in which a wound site, particularly a fine wound site, is detected from a photographed image, and a recording medium thereof.

In order to achieve the above object, the present invention provides a method for detecting a wound site of the skin, the visibility of the captured image is improved and stored as a visibility enhancement image (visibility enhancement step), and the visibility enhancement image is a binary image And a step of storing (detecting) and storing pixels of the binarized image as a computed image by removing morphology calculation and noise area (computing).

In the above description, the method may further include a step of preprocessing the image photographed before the visibility enhancing step (preprocessing step), wherein the preprocessing step is a step in which some of the pixels of the photographed image are converted to black.

,

,

와 같이 픽셀의 각 광에 대하여 연산되어 균등화되며, 상기 식에서

는 수학식

에 의하여 연산되어 대입되고,

는 수학식

에 의하여 연산되며, 상기에서

,

,

는 균등화 전 처리 이미지의 각 픽셀의 R, G, B값이고,

,

,

는 균등화 후 각 픽셀의 R, G, B 값이며, x와 y는 각 픽셀의 좌표, M은 각 픽셀의 x좌표의 수, N은 각 픽셀의 y좌표의 수 인 것을 특징으로 한다.In the above, the visibility enhancement step (ST-120) includes an equalization step; Equalization step is the equation

,

,

Is computed and equalized for each light of the pixel as

Is expressed by the following equation

Computed and substituted by

Is expressed by the following equation

Calculated by

,

,

Is the R, G, and B values of each pixel of the processed image before equalization,

,

,

Is the R, G, and B values of each pixel after equalization, x and y are the coordinates of each pixel, M is the number of x-coordinates of each pixel, and N is the number of y-coordinates of each pixel.

,

,

에 의하여 스트레칭 후의 각 픽셀의 R, G, B값이 연산되며; In the above, the visibility enhancement step ST-120 further includes a stretching step in which the R, G, and B histograms of each pixel executed after the equalization step ST-121 are adjusted to remove color distortion; The stretching step may be performed based on the R, G, and B values of each pixel.

,

,

R, G, and B values of each pixel after stretching are calculated by;

은 수학식

에 의하여 연산되고,

는

값 중 최대값,

은

값 중 최소값이며;

는 수학식

에 의하여 연산되고,

는

값 중 최대값,

은

값 중 최소값이며;

는 수학식

에 의하여 연산되고,

는

값 중 최대값,

은

값 중 최소값인 것을 특징으로 한다.In the above formula

Is the equation

Calculated by

The

The maximum of the values,

silver

The minimum of the values;

Is expressed by the following equation

Calculated by

The

The maximum of the values,

silver

The minimum of the values;

Is expressed by the following equation

Calculated by

The

The maximum of the values,

silver

Characterized in that the minimum value of the value.

In the above, the detecting step is characterized in that the visibility enhancement image is converted into HSV space (or also referred to as HSB space) and binarized.

In the above, when the H (chroma) is 50 to 70, S (saturation) is 180 to 255, and B (brightness) is 200 to 255 in the visibility enhancement image, the pixels representing the wound are displayed as pixels. The pixels are converted to have the same color, and the pixels corresponding to the other ranges are converted to the same color to have a different color from the wound area, thereby generating a binarized image.

In the above, the calculating step includes a morphology calculating step and a noise region removing step; The morphology calculation step is characterized in that the transformation is performed through the expansion and contraction process of the pixel representing the wound in the binarized image.

In the above, the noise region elimination step is a noise region elimination step (1), in the case where the number of pixels connected to each other among the pixels connected to each other in the binarized image is less than 7 or more than 50, It is characterized in that the conversion to have the same color as the pixel corresponding to.

In the above, the noise region elimination step is a noise region elimination step (2), where a minimum rectangular pixel region enclosing the pixels representing the wound region is derived, at each position where pixels representing the wound region are connected to each other in the binarized image. Counts the total number of pixels forming the smallest rectangle, counts the number of white pixels located within the smallest rectangle, and calculates the ratio of the number of white pixels to the total number of pixels forming the smallest rectangle (pixel number ratio). It is done.

In the above, when the pixel number ratio is 0.5 or less, the pixel number is converted to have the same color as the pixel corresponding to the other range.

On the other hand, the present invention provides a computer-readable recording medium in which a program for executing the method for detecting a wound site on the skin is recorded.

According to the wound site detection method of the skin and the recording medium of the present invention as described above, the wound site, especially the fine wound site is detected from the photographed image and provided to the site where the autopsy is performed, thereby remaining in the skin It has the effect of providing data to observe the site in detail.

Figure 1 shows the steps to achieve a method for detecting a wound site of the skin according to the present invention,
FIG. 2 is a view illustrating a visibility enhancement step among the steps illustrated in FIG. 1;
FIG. 3 is a diagram illustrating an operation step among the steps illustrated in FIG. 1.
4 is an exemplary diagram illustrating a histogram of light constituting a pixel of an image in the image of FIG. 7.
5 is an exemplary diagram illustrating a histogram stretched from the histogram of FIG. 4.
6 is an example of a first image photographed and stored at the time of optometry,
FIG. 7 is an example of a preprocessed image in which the first image of FIG. 6 is stored through a preprocessing step.
8 is an example of a visibility enhancement image in which the preprocessed image of FIG. 7 is stored through a visibility enhancement step.
FIG. 9 is an example of a binarized image in which the visibility enhancement image of FIG. 8 is binarized and stored.
FIG. 10 is an example of a calculated image in which the binarization image of FIG. 9 is stored through a morphology calculation or the like.
11 to 13 schematically illustrate pixels constituting an image to explain a morphology calculation.
14 is a result image in which a wound is displayed and stored in the pre-processed image of FIG. 7,
FIG. 15 is a schematic diagram for explaining the noise region removing step 2. As shown in FIG.

DETAILED DESCRIPTION Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art may easily implement the present invention.

Figure 1 shows the steps to achieve the wound site detection method of the skin according to the invention, Figure 2 is shown to explain the step of improving the visibility of the steps shown in Figure 1, Figure 3 is shown in Figure 1 FIG. 4 is an exemplary diagram illustrating a histogram of a plurality of lights constituting pixels of an image in the image of FIG. 7, and FIG. 5 illustrates an example of stretching in the histogram of FIG. 4. 6 is an example of a first image photographed and stored during an optometry, FIG. 7 is an example of a preprocessed image stored through a preprocessing step, and FIG. 8 is a visibility of the preprocessed image of FIG. 9 is an example of a visibility enhancement image stored through an enhancement step. FIG. 9 is an example of a binarization image in which the visibility enhancement image of FIG. 8 is binarized and stored. FIG. 11 to 13 are diagrams schematically illustrating the morphology calculation, and FIG. 14 is a result image in which a wound is displayed and stored in the preprocessing image of FIG. 7, and FIG. 15. Is a schematic diagram of pixels to explain the noise region removal step (2).

The wound site detection method according to the present invention relates to a method of detecting the wound site from an image taken and stored with the optometry (hereinafter referred to as a 'first image'). As shown in FIG. 1, the wound site detection method according to the present invention includes the steps of improving the visibility of the first image and storing the image as a visibility enhancement image (ST-120, visibility enhancement step), and storing the visibility enhancement image as a binarized image. And a step in which the pixels of the binarized image are stored as a computed image by removing the morphology calculation and the noise region (ST-140, the calculating step). The first image may be further preprocessed and stored as a preprocessed image (ST-110, preprocessing step) before the visibility enhancement step ST-120.

In the pre-processing step ST-110, the first image as shown in FIG. 6 converts the colors of some pixels to black as shown in FIG.

Skin wounds (torn, scratches, needle marks, squeezing, etc.) are not smooth on the skin surface, even if fine. Chromatic aberration, which occurs due to different media, is prominent in the first image photographed and stored. By visualizing this phenomenon, the visibility of the wound may be increased. Due to the characteristics of the human body, most of the image is occupied by the skin color region, so that the color distribution is skewed to one side, which hinders the visualization of the chromatic aberration. Therefore, some pixels constituting the image are stored in a color having a large difference from the skin color (for example, black (R = 0, G = 0, B = 0)). As shown in FIG. 6, it is possible to take a picture with a white chair or the like and to change the white pixel area to black. For example, the image is taken with a white ruler and information about pixels of the first image is obtained so that all of the R, G, and B values of R, G, and B that are all 250 or more are changed to 0. It is possible. Alternatively, as the step proceeds, the first image may be displayed to allow the user to designate an area to be converted into black.

As a part of the pixels forming the first image is changed to a color having a large difference from the skin color, chromatic aberration between the wound part and the remaining part is greatly formed in the visibility enhancement image obtained after the visibility enhancement step.

As a result of the inventor's repetitive experiments, the visibility of R, G and B is changed to a color having a difference of 100 or more from the skin color, and when the pixels in the range of 10 to 30% are changed in the region of the first image, The chromatic aberration was large after the enhancement step (ST-120).

FIG. 7 is a pre-processed image in which some pixels of the first image illustrated in FIG. 6 are converted to black and stored.

As shown in FIG. 7, the visibility of the preprocessed and stored preprocessed image is improved (ST-120, visibility improvement step).

To measure the degree of color distortion, the input device comes in three signals: R, G, and B, so you must measure the difference between them. But hue contains a lot of noise. In the case of a camera, the incoming light is not a constant light, but different light comes in at various angles, and the light intensity is also different. The pixel absorbs the photon of light and converts it into a signal using an analog-to-digital (A / D) converter. After that, color adjustment and gamma adjustment are performed, followed by Jpeg compression. Noise enters at each stage of the image acquisition process. Thus, even when shooting a very uniform place, the acquired image is not even.

Therefore, in order to remove the influence of the phenomenon that the color is transformed according to the light intensity and the input characteristics of R, G, B, it is necessary to normalize (Normalize). The equalization step ST-121 is calculated as in the equation below to equalize R, G, and B values of each pixel.

,

,

는 전처리 이미지의 각 픽셀의 R, G, B 값을 나타내고,

,

,

는 균등화 후 각 픽셀의 R, G, B 값을 나타내며, x와 y는 전처리 이미지에서 픽셀의 좌표, M은 각 픽셀의 x좌표의 수, N은 각 픽셀의 y좌표의 수를 나타낸다.In Equations 1, 2, and 3

,

,

Represents the R, G, and B values of each pixel in the preprocessed image,

,

,

Represents the R, G, and B values of each pixel after equalization, x and y represent the coordinates of the pixel in the preprocessed image, M represents the number of x-coordinates of each pixel, and N represents the number of y-coordinates of each pixel.

는 수학식 4에 의하여 연산된다.In Equations 1, 2, 3

Is calculated by equation (4).

는 수학식 5에 의해서 연산된다.In Equations 1, 2, 3

Is calculated by equation (5).

When the equalization step is completed, histograms of R, G, and B of each pixel are adjusted to perform a stretching step (ST-123) to remove color distortion. 4 shows the histograms of R, G, B pixels after equalization before the stretching step, and FIG. 5 shows histograms of R, G, B pixels after the stretching step.

As shown in FIG. 4, in the histogram of the first light 260 that is red, the second light 270 that is green, and the third light 280 that is blue, stretching is performed by Equations 6 to 11 below. The first light 265, the second light 275, and the third light 285 are calculated.

는 스트레칭 된 픽셀의 제1 광(265)을 나타낸다.In the above formula

Represents the first light 265 of the stretched pixel.

는 수학식 7에 의하여 연산되어 대입되고, 수학식 6에서

는

값 중 최대값을,

은

값 중 최소값을 나타낸다. In Equation (6)

Is calculated by Equation 7 and substituted, and in Equation 6

The

The maximum of the values,

silver

It represents the minimum value among the values.

는 스트레칭 된 픽셀의 제2 광(275)을 나타낸다.In the above formula

Represents the second light 275 of the stretched pixel.

는 수학식 9에 의하여 연산되어 대입되고, 수학식 8에서

는

값 중 최대값을,

은

값 중 최소값을 나타낸다.In Equation (8)

Is calculated and substituted by Equation 9, and

The

The maximum of the values,

silver

It represents the minimum value among the values.

는 스트레칭 된 픽셀의 제3 광(285)을 나타낸다.In the above formula

Represents the third light 285 of the stretched pixel.

는 수학식 11에 의하여 연산되어 대입되고, 수학식 10에서

는

값 중 최대값을,

은

값 중 최소값을 나타낸다.
In Equation (10)

Is calculated and substituted by Equation 11,

The

The maximum of the values,

silver

It represents the minimum value among the values.

By this equalization and stretching, the intensity of a plurality of lights constituting the pixel is constant.

After the visibility enhancement step ST-120, the preprocessed image of FIG. 7 is stored as the visibility enhancement image of FIG.

The visibility enhancement image stored through the visibility enhancement step (ST-120) in which the result calculated on the pixels is obtained through the above operation process is binarized in the detection step (ST-130) and stored as a binarized image. The visibility enhancement image shown in FIG. 8 is converted into HSV space (or also referred to as HSB space) and binarized.

After the above pretreatment step (ST-110) and the visibility enhancement step (ST-120) as shown in Figure 8, the wound site will appear in a yellow series, but other parts of the wound may also appear in a yellow series. Therefore, in the image shown in FIG. 8, the chromaticity, saturation, and brightness values are converted into HSV space and binarized.

The present inventors confirmed that the wound area appeared in yellow after the above steps, and the wound area in the HV range of 50 to 70, S (saturation) of 180 to 255, B in the HSV space among the yellow series. It was confirmed that (brightness) was in the range of 200 to 255. Therefore, pixels in the above range are changed to white (R = 255, G = 255, B = 255), and pixels outside the above range are changed to black (R = 0, G = 0, B = 0). Binarized images are created and stored. 9 shows the binarized and stored image. As illustrated in FIG. 9, there are eight white regions in the binarization image.

The calculation step ST-140 is a morphology calculation step ST-141 which causes the pixels of each location to be aggregated due to the noise, and the noise region removal step 1 where the parts corresponding to the noise of each location are removed. 143) and a noise region removing step (2) (ST-143) in which a portion corresponding to noise is removed.

The morphology calculation step ST-141 will be described with reference to FIGS. 11 to 13. FIG. 11 schematically illustrates one of eight white regions of FIG. 9, where 'W' represents white pixels and 'B' represents black pixels.

The morphology calculation step (ST-141) consists of expansion and contraction processes. In the expansion process, pixels surrounding white pixels in the binarized image are converted to white. The pixels of FIG. 11 undergo an expansion process and black pixels surrounding the white pixels are converted into white pixels as shown in FIG. 12. Therefore, in FIG. 11, the black pixels surrounded by the white pixels are converted into white pixels and stored. After the expansion process, the outer pixels among the pixels having a white color, specifically, pixels in contact with at least one of the black pixels, are converted into black pixels and stored as shown in FIG. 13 (reduction process).

The noise is removed in two stages. The following two steps can be performed selectively.

The present invention is to detect the micro-wound portion of the wound, which is not clearly visible to the naked eye, the present inventors have identified the pixel for the wound that is not visible to the naked eye and confirmed through the microscopic observation as a result of the morphology calculation step In the binarized image stored in (ST-141) (exemplified in FIG. 10), it was determined that the number of white pixels connected to each other among each location was not wound or not more than seven. Therefore, in the binarized image stored through the morphology calculation step (ST-141), pixels having 7 or less white pixels or 50 or more in the white areas connected to each other are converted to black and stored (ST-143, noise area removing step ( One)).

FIG. 15 is a diagram schematically illustrating the noise region removing step (2) (ST-145), in which the hatched portions represent white pixels and the remaining portions represent black pixels. At each point representing the wound site, an area surrounding the white pixels is calculated. For example, the maximum and minimum values of the X-axis coordinates are derived from the coordinates of the white pixel at some points, and the maximum and minimum values of the Y-axis coordinates are derived. Four vertices are derived from the maximum and minimum values of the X-axis coordinates and the maximum and minimum values of the Y-axis coordinates. The four vertices are (X-axis minimum, Y-axis minimum), (X-axis minimum, Y-axis maximum), (X-axis maximum, Y-axis minimum), (X-axis maximum, Y-axis maximum). The pixel region that falls within the four vertices becomes the smallest rectangular pixel region that includes some white pixels as shown in FIG.

The total number of pixels forming the smallest rectangle is counted, and the number of white pixels located in the smallest rectangle is counted to calculate the ratio of the number of white pixels to the total number of pixels forming the smallest rectangle (pixel number ratio).

For example, in FIG. 15, the number of pixels of the minimum rectangular pixel area including white pixels is 42, the number of white pixels is 17, and the ratio of the number of white pixels to the total number of pixels is calculated as 0.4047. The inventors calculated the ratio of the number of white pixels to the total number of pixels forming the smallest rectangle with respect to the areas represented as the wound area by calculating the pixel area ratio by observing the wound area in detail with a microscope or the like. When the pixel filling ratio) is 0.5 or less, it is confirmed that the wound is not a wound part (ST-145, noise area removing step (2)).

FIG. 14 is a resultant image stored so that the pixel fill ratio of the preprocessed image overlaps with the minimum rectangular pixel area at a position larger than 0.5. FIG.

The wound site detection method of the skin may be programmed and stored in a personal PC or mobile phone to be executed to receive a first image and to display a calculation image as shown in FIG. 10.

In the above description, the present invention has been described with reference to preferred embodiments, but the present invention is not necessarily limited thereto, and a person having ordinary skill in the art to which the present invention pertains does not depart from the technical spirit of the present invention. It will be readily appreciated that various substitutions, modifications and variations can be made.

ST-110: pretreatment step, ST-120: visibility enhancement step
ST-130: detection step, ST-140: operation step

Claims (16)

In the method of detecting a wound on the skin, the visibility of the captured image is improved and stored as a visibility enhancement image (ST-120, visibility enhancement step), and the visibility enhancement image is stored as a binarization image (ST- 130, a detection step), and the pixels of the binarization image is removed as a morphology calculation and the noise area is stored as a calculation image (ST-140, calculation step). The method of claim 1, further comprising: pre-processing the image photographed before the visibility enhancement step ST-120 and storing the pre-processed image as a pre-processing image. And a part of the pixels of the image is converted to black. The method of claim 1, wherein the visibility enhancement step (ST-120) comprises an equalization step (ST-121); The equalization step (ST-121) is a mathematical equation

,

,

Is computed and equalized for each light of the pixel as

Is expressed by the following equation

Computed and substituted by

Is expressed by the following equation

Calculated by

,

,

Is the R, G, and B values of each pixel before equalization,

,

,

Is the R, G, and B values of each pixel after equalization, x and y are the coordinates of each pixel, M is the number of x-coordinates of each pixel, and N is the number of y-coordinates of each pixel. Site detection method. The method of claim 2, wherein the visibility enhancement step (ST-120) comprises an equalization step (ST-121); The equalization step (ST-121) is a mathematical equation

,

,

Computed and equalized for each light as

Is expressed by the following equation

Computed and substituted by

Is expressed by the following equation

Calculated by

,

,

Is the R, G, and B values of each pixel before equalization,

,

,

Is an R, G, B value of each pixel after equalization, and x and y are coordinates of each pixel. The method of claim 3, wherein the visibility enhancement step ST-120 is performed by adjusting the R, G, and B histograms of each pixel executed after the equalization step ST-121 to remove color distortion. 123); In the stretching step ST-123, equations for the R, G, and B values of each pixel

,

,

R, G, and B values of each pixel after stretching are calculated by;
In the above formula

Is the equation

Calculated by

The

The maximum of the values,

silver

The minimum of the values;

Is expressed by the following equation

Calculated by

The

The maximum of the values,

silver

The minimum of the values;

Is expressed by the following equation

Calculated by

The

The maximum of the values,

silver

The wound site detection method of skin characterized by the minimum value. The stretching step (ST-120) of claim 4, wherein the visibility enhancement step (ST-120) is performed by adjusting the R, G, and B histograms of each pixel executed after the equalization step (ST-121) to remove color distortion. 123); In the stretching step ST-123, equations for the R, G, and B values of each pixel

,

,

R, G, and B values of each pixel after stretching are calculated by;
In the above formula

Is the equation

Calculated by

The

The maximum of the values,

silver

The minimum of the values;

Is expressed by the following equation

Calculated by

The

The maximum of the values,

silver

The minimum of the values;

Is expressed by the following equation

Calculated by

The

The maximum of the values,

silver

The wound site detection method of skin characterized by the minimum value. 7. The wound site detection method according to any one of claims 3 to 6, wherein the detecting step ST-130 converts the visibility enhancement image into an HSV space (also called an HSB space) and binarizes it. Way. The wound part of claim 7, wherein when the H (chromatity) of the pixels of the visibility enhancement image is in the range of 50 to 70, S (saturation) is in the range of 180 to 255, and B (brightness) is in the range of 200 to 255, The pixel is converted to have the same color as the representing pixel, the pixels corresponding to the other range is changed to the same color so as to have a different color from the wound area to generate a binarized image, characterized in that the skin. 8. The method according to claim 7, wherein said calculating step (ST-140) comprises a morphological calculation step (ST-141) and a noise area removing step; The morphology calculation step (ST-141) is transformed through the expansion and contraction process of the pixel representing the wound site in the binarized image, characterized in that the wound site detection method. 10. The method of claim 9, wherein the noise area elimination step is a noise area elimination step (1) when the number of pixels connected to each other among the pixels connected to each other in the binarized image is less than 7 or 50 or more. And wound area detection method of the skin, characterized in that it is converted to have the same color as the pixel corresponding to the other range. 10. The method according to claim 9, wherein the noise region removing step is a noise region removing step (2), in which at least a pixel region representing a wound region is derived at each point where pixels representing a wound region are connected to each other in a binarized image. Counts the total number of pixels that form the smallest rectangle at each point, counts the number of white pixels that are within the smallest rectangle, and the ratio of the number of white pixels to the total number of pixels that form the smallest rectangle (pixel number ratio) The wound site detection method of skin characterized in that it is calculated. The method of claim 11, wherein the pixel number ratio is converted to have the same color as a pixel corresponding to the other range when the pixel number ratio is 0.5 or less. 12. The pixel of claim 10, wherein the noise area removing step is a noise area removing step (2), wherein pixels representing a wound area are connected to each other where pixels representing a wound area are interconnected in a binarized image that has passed the noise area removal step (1). The smallest rectangular pixel area surrounding them is derived, the total number of pixels forming the smallest rectangle at each point is counted, the number of white pixels located within the smallest rectangle is counted, and the white pixels to the total number of pixels forming the smallest rectangle. A method of detecting a wound site of skin, wherein a number ratio (pixel number ratio) is calculated. The method of claim 13, wherein the pixel number ratio is converted to have the same color as a pixel corresponding to the other range when the pixel number ratio is 0.5 or less. A computer-readable recording medium having recorded thereon a program for executing the method for detecting a wound site of skin. A computer-readable recording medium having recorded thereon a program for executing the method for detecting a wound site of skin according to any one of claims 1 to 6.

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