WO2022149566A1 - 遠隔皮膚疾患リスク判定システムおよび遠隔皮膚疾患リスク判定方法 - Google Patents

遠隔皮膚疾患リスク判定システムおよび遠隔皮膚疾患リスク判定方法 Download PDF

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Publication number
WO2022149566A1
WO2022149566A1 PCT/JP2022/000025 JP2022000025W WO2022149566A1 WO 2022149566 A1 WO2022149566 A1 WO 2022149566A1 JP 2022000025 W JP2022000025 W JP 2022000025W WO 2022149566 A1 WO2022149566 A1 WO 2022149566A1
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Prior art keywords
image
skin disease
risk determination
disease risk
remote
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Ceased
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PCT/JP2022/000025
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English (en)
French (fr)
Japanese (ja)
Inventor
公一朗 木島
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Pinpoint Photonics Inc
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Pinpoint Photonics Inc
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Instruments for taking body samples for diagnostic purposes; Other methods or instruments for diagnosis, e.g. for vaccination diagnosis, sex determination or ovulation-period determination; Throat striking implements
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • Melanoma is a type of skin cancer and is a skin disease that has been increasing in Japan in recent years. Melanoma is present on the surface of the skin. That is, the melanoma exists in a position where it can be visually recognized without using a device such as an endoscope or an X-ray device possessed only by a medical institution. Therefore, as shown in Patent Document 1, the discolored part of the skin is photographed by a portable device such as a smartphone, and the photographed image is transferred to a remote server via an internet line to determine the risk of melanoma. A remote skin disease risk determination system has been proposed.
  • a user who may be a patient takes a picture of a mole 12 existing on the skin surface 9 of a foot by a smartphone 11, and an internet line is used. Transfer to the server 20 owned by the service provider via 19. After confirming the patient information by the image reception reception tool 21, the service provider confirms by the determination tool 22 whether or not this image is a target image for determining the risk of skin diseases such as moles and melanoma. Then, when it is determined that the image is for determining the risk of skin disease, the image data such as the texture, morphology, and color information of the mole 12 is extracted from the image by the feature parameter extraction tool 23. Next, this image data is classified by the image classification tool 24 and the risk determination tool 25 that have been machine-learned by the case learning data, and then the determination result is given to the user.
  • This remote skin disease risk determination system has the advantage that the burden on the user is small because the user does not have to go to the hospital. Also, on the medical institution side, there is an advantage that the burden on the medical staff is small because the screening is performed by the machine-learned device.
  • Image data such as texture, morphology, and color information of the hokuro 12 used in general machine learning image judgment systems vary depending on the lighting conditions, so it is necessary to prepare training data corresponding to many lighting conditions. There are drawbacks.
  • the present invention relates to a remote skin disease risk determination system and a remote skin disease risk determination method that can extract image data such as texture, morphology, and color information of mole 12 without variation even when the lighting conditions vary.
  • image data such as texture, morphology, and color information of mole 12 without variation even when the lighting conditions vary.
  • the variation in the color information of the subject photographed by an image photographing means such as a camera due to the illumination state can be corrected by a method generally used as a white balance correction.
  • a method for correcting variations in texture and morphology image data due to lighting conditions is not generally used.
  • FIG. 2 shows a photograph in which the mole is arranged almost in the center.
  • FIGS. 3 (a) to 3 (d) are photographs in which the luminance information of the photograph shown in FIG. 2 is changed as an example of images taken under different lighting conditions.
  • 3A is a photograph in which the right end of the photograph has the same luminance as the photograph shown in FIG. 2, but the luminance information is changed so that the left edge of the photograph has a luminance of 75%.
  • the upper left coordinate of the image is (0,0)
  • the upper right coordinate is (X, 0)
  • the lower left coordinate is (0, Y)
  • the lower right coordinate is (X).
  • the luminance I (i, j) of each coordinate (i, j) in FIG. 4A is relative to the luminance P (i, j) of the image shown in FIG. 2 as shown in Equation 1. It is an image having a brightness such that the brightness changes linearly according to the coordinates x after being multiplied by 0.75.
  • FIG. 3B is a photograph in which the right end of the photograph has the same luminance as the photograph shown in FIG. 2, but the luminance information is changed so that the left edge of the photograph has a luminance of 50%.
  • the image is multiplied by 0.5 with respect to the brightness P (i, j) of the image shown in FIG. 2, and then the brightness is set so that the brightness changes linearly according to the coordinates x. Is. Therefore, the image shown in FIG. 3A is an image in which the illumination state has an inclination of 25%, and the image shown in FIG. 3B is an image in which the illumination state has an inclination of 50%.
  • the image shown in FIG. 3C is an image in which the lighting state is uniformly reduced by 25%
  • the image shown in FIG. 3D is an image in which the lighting state is uniformly reduced by 50%.
  • the image shown in FIG. 3 (c) and the image shown in FIG. 3 (d) depend on the coordinate position with respect to the brightness P (i, j) of the image shown in FIG. 2 as shown in Equations 3 and 4. No, 0.75x and 0.5x images.
  • FIG. 5 shows the results of texture analysis of the three areas shown by the white lines in the image shown in FIG. 2 and the four images shown in FIG.
  • the caption described as original in FIG. 5 is the result of texture analysis of the portions shown in areas 1 to 3 of the image of FIG. 2, and (a) gradation (25%), (b) gradation (50%), ( c) The captions described as reduction (25%) and (d) reduction (50%) were subjected to texture analysis of the parts shown in areas 1 to 3 of the images shown in FIGS. 3 (a) to 3 (d), respectively.
  • a general entropy was used as the texture analysis method.
  • FIG. 6 shows an image feature analysis method using the phase stretch transform, which is the boundary detection processing step shown in Patent Document 2 and Non-Patent Document 1, on the images shown in FIGS. 2 and 3 (a) to 3 (d). After application, the results of texture analysis of the three areas shown by the white lines are shown.
  • the phase stretch transform is a method of performing edge detection by a non-linear detection method that combines phase information and derivative information of phase information in the phase information of an image obtained by Fourier transforming the image information. Since the edge information detected by this method is not binarized information but information having a gradation value, it is also possible to exclude the luminance information which is the background information by performing this edge detection. be.
  • the texture analysis method the same general entropy as the result in FIG. 5 was used.
  • the luminance information is removed by the boundary detection processing step that outputs the information having the gradation value before the texture information and the morphology information are analyzed, so that the texture analysis result varies in the lighting state. It turns out that it is not affected by.
  • the fact that the texture analysis results are not affected by the variation in lighting conditions indicates that even if the lighting conditions of the image taken by the user vary, the texture can be analyzed without being affected by the variation. Therefore, it is not necessary to prepare training data corresponding to many lighting conditions.
  • the present invention has been made in consideration of the above points, and by allowing variations in the lighting conditions of the shooting environment, variations in image data such as the texture and morphology of the hokuro become large, and therefore, many lighting conditions can be obtained. It provides a solution to the problem that it is necessary to prepare the corresponding learning data.
  • a boundary detection processing step of outputting information having a gradation value is used before analyzing texture information and morphology information.
  • the effect of variation in lighting conditions is excluded from the analysis results of the texture information and morphology information.
  • a member for correcting color information is arranged on a member that holds the relative position between the position of the smartphone and the mole, and by taking a picture of this member, the color information of the lighting is acquired and the color is corrected. Enables.
  • the remote skin disease risk determination system and the remote skin disease risk determination method of the present invention can exclude the influence of variations in lighting conditions from the analysis results of texture information and morphology information, learning data corresponding to many lighting conditions can be excluded. The need to prepare is reduced. Further, the remote skin disease risk determination system and the remote skin disease risk determination method of the present invention illuminate by arranging a member that holds a relative position between the position of the smartphone and the mole when taking an image of the skin with the smartphone. Even if the state changes, the influence of the variation in the lighting state can be excluded from the analysis results of the texture information and the morphology information. Therefore, it is possible to arrange a member that holds the relative position between the position of the smartphone and the mole, and it becomes easy to take an image without blurring.
  • a member on which a scale is formed is placed on a member that holds the relative position between the position of the smartphone and the mole, and the image is photographed.
  • This is an example of a mole image used in a remote skin disease risk determination system.
  • This is an example of a mole image with variations in lighting conditions used in a remote skin disease risk determination system.
  • It is a figure which shows the method of making the image of the mole with the variation of lighting conditions to be used for the remote skin disease risk determination system.
  • It is a figure of the experimental result which shows that the texture analysis in the remote skin disease risk determination system as a prior art varies depending on the variation of lighting conditions.
  • It is a figure of the experimental result which shows that the influence of the variation of a lighting state is excluded from the analysis result of the texture information and the morphology information by performing the boundary detection processing step before the texture analysis step.
  • the remote skin disease risk determination system having a step of excluding distortion characteristics of a camera by a scale of a member that fits a member that holds a relative position between the position of a smartphone and a mole of the present invention and a step of correcting color information of lighting.
  • It is a schematic block diagram. It is a schematic process diagram which shows the process of the application which a user performs through a smartphone in the remote skin disease risk determination system of this invention. It is a schematic process diagram which shows the process which the user acquires the condition which corrects the distortion of an image in the remote skin disease risk determination system of this invention using the application of a smartphone.
  • FIG. 7 shows a schematic configuration diagram of the remote skin disease risk determination system of the present invention.
  • a mole 12 formed on the skin surface 9 of a foot is photographed by a smartphone 11 which is a portable device having an image capturing function and a data transfer function, and an internet line 19 is taken.
  • the data is transferred to the server 31 owned by the service provider.
  • the service provider After confirming the patient information by the image reception reception tool 21, the service provider confirms by the determination tool 22 whether or not this image is a target image for determining the risk of skin diseases such as moles and melanoma.
  • the image feature analysis method using the phase stretch transform which is the boundary detection processing step shown in Patent Document 2 and Non-Patent Document 1
  • the background brightness information is excluded by the boundary / texture extraction tool 29 that outputs information having a gradation value.
  • the image data such as the texture and morphology of the mole 12 is extracted from the image by the feature parameter extraction tool 23. After that, this image data is classified by the image classification tool 24 and the risk determination tool 25 that have been machine-learned by the case learning data, and then the determination result is given.
  • the feature parameter extraction tool 23 is applied after the background brightness information is excluded by the boundary / texture extraction tool 29.
  • image data such as texture and morphology of the hokuro 12 that does not depend on the lighting conditions from the image
  • Image data can be extracted. Furthermore, since it is not necessary to use learning data considering variations in lighting conditions for the data machine-learned by the case learning data used in the image classification tool 24 and the risk determination tool 25, less learning data than in the conventional example. Can be used.
  • FIG. 8 shows a schematic configuration diagram of the member 40 that holds the relative position between the position of the smartphone and the mole.
  • This member is a macro that makes it easy to align the focal position of the camera of the smartphone 11 with the spacer member 41 that keeps the distance between the smartphone and the mole, which is a portable device having an image capturing function and a data transfer function, constant.
  • the structure is such that the photographing lens 42 is fixed.
  • a clip member 43 for holding a state in which the macro photography lens 42 is positioned substantially in front of the camera of the smartphone 11 is also provided.
  • the clip member has a structure in which the two clip holders 44 and 45 can be opened and closed from the fulcrum 46, and the smartphone 11 can be sandwiched by the spring force of the spring 47.
  • FIG. 9 shows a configuration diagram in which the mole 12 is photographed in a form in which the member 40 that holds the relative position between the position of the smartphone and the mole is fixed to the smartphone 11 by using the clip member 43.
  • the spacer member 41 of the member 40 that holds the relative position between the position of the smartphone and the mole is formed of a transparent member that transmits external light. Since the spacer member 41 is made of a transparent member, the image of the mole 12 can be taken by using outside light such as indoor lighting without having a lighting mechanism.
  • the spacer member 41 has a cylindrical shape or a conical shape without apex portions as shown in FIGS. 8 and 9, and the inner surface 41a thereof is a sand-treated surface. Since the inner surface 41a is a surface treated with sand, it is possible to prevent the shadow of the illumination from being clearly reflected in the image. Since the sand-treated surface is arranged on the inner surface 41a side instead of the outer peripheral surface of the spacer member 41, the risk of the photographer or the like touching the sand-treated surface is reduced, and oil or the like adheres to the sand-treated surface. It is possible to reduce the risk of deterioration of the diffused reflection characteristics of the surface.
  • the illumination state of the image of the mole 12 is poor as compared with the case where the member 40 that holds the relative position between the position of the smartphone and the mole is not used.
  • the image will be uniform, in the present invention, by excluding the background brightness information by the boundary / texture extraction tool 29 that outputs information having a gradation value, the texture due to the variation in the illumination state in the captured image, Since variations in image data such as morphology can be excluded, it is possible to use the member 40 that holds the relative position between the position of the smartphone and the mole.
  • FIG. 10 shows a schematic configuration diagram of a second member 39 that holds a relative position between the position of the smartphone and the mole.
  • the member 39 has a structure in which a color correction sheet 48 having a white color on the member 40 shown in FIG. 8 is fixed to a spacer member 41, and the color correction sheet 48 has an opening 48a so that a mole can be photographed.
  • the color correction sheet 48 is also arranged so as to be photographed in a part of the field of view.
  • the clip member 43 is used to fix the member 39 that holds the relative position between the position of the smartphone and the mole to the smartphone 11, and the configuration diagram of the case where the mole 12 is photographed is shown in FIG. Shown in.
  • FIG. 12 shows an example of an image of the mole 12 taken by using the second member 39 that holds the relative position between the position of the smartphone and the mole.
  • the image 49 of the color correction sheet 48 is photographed around the mole 12.
  • Luminance information of a plurality of arbitrary points of the image 49 of the color correction sheet for example, three points of 49a, 49b, and 49c is analyzed, and the red, green, and blue information of those points are (100, 110, 60), respectively.
  • the photographed lighting environment has the strongest green and the weakest blue. It can be seen from this information, and in the captured image shown in FIG. 12, the green luminance information is left as it is, and the red luminance information is multiplied by 1.1 in the three luminance information of 49a, 49b, and 49c to be blue. By multiplying the luminance information by 110/60, it can be obtained as (110, 110, 110), (165, 165, 165), (66, 66, 66), respectively.
  • the same correction is applied to the image portion of the hokuro 12, specifically, the green luminance information is left as it is, and the red luminance information is multiplied by 1.1 in the three luminance information of 49a, 49b, and 49c to obtain the blue luminance information.
  • the red luminance information is multiplied by 1.1 in the three luminance information of 49a, 49b, and 49c to obtain the blue luminance information.
  • FIG. 13 shows a cross-sectional structural view of a member 39 that holds the relative position between the position of the smartphone and the mole and the scale member 51 that fits into the spacer member 41 of the member 40.
  • FIG. 14 shows a configuration diagram of the scale member 51 attached to the member 40 that holds the relative position between the position of the smartphone attached to the smartphone 11 and the mole.
  • the scale member 51 has a configuration in which the surface 51a is arranged at the position of the skin when an image of a mole is taken by the smartphone 11 using the spacer member 41, and the scale member 51 has a scale arranged on the surface 51a.
  • FIG. 15 shows an example of a scale formed on the surface 51a of the scale member 51.
  • the scale includes, for example, a concentric scale pattern 52 and linear scale patterns 53 and 54 orthogonal to each other.
  • the scale patterns 52, 53, 54 By photographing the scale patterns 52, 53, 54 with the smartphone 11, it is possible to know the distortion information of the image of the image of the mole 12 located on the skin surface 9. That is, when the linear scale patterns 53 and 54 are distorted in the image in which the scale pattern is photographed, it can be seen that the same distortion is added to the image in the image in which the mole 12 is photographed. Therefore, if the scale patterns 52, 53, 54 are distorted in the image obtained by capturing the scale pattern, the distortion correction is performed so that the distorted scale patterns 52, 53, 54 become the actual scale pattern shown in FIG. That is, it is possible to generate an image without distortion by performing distortion correction. Further, it is possible to correctly know the shooting magnification by shooting the scale patterns 52, 53, 54 with the smartphone 11.
  • the remote skin disease risk determination system of the present invention by transferring the photograph of the scale 51 together with the image of the mole 12 from the smartphone to the server, the risk can be determined in the image without distortion, and the scale information is also a risk. It can be used for judgment.
  • FIG. 16 shows an image of the mole 12 taken by using the member 39 or the member 40 that holds the relative position between the position of the smartphone and the mole, and further, the member 39 or the member 40 that holds the relative position between the position of the smartphone and the mole.
  • the schematic block diagram of the remote skin disease risk determination system 60 which uses the image which image
  • a mole formed on the skin surface 9 of a foot is photographed by a smartphone 11 using a member 40 that holds a relative position between the position of the smartphone and the mole, and an internet line is used. Transfer to the server 61 owned by the service provider via 19.
  • the service provider After confirming the patient information by the image reception reception tool 21, the service provider confirms by the determination tool 22 whether or not this image is a target image for determining the risk of skin diseases such as moles and melanoma. Then, when it is determined that the image is for determining the risk of skin disease, the white balance correction analyzed by the image around the skull and the scale pattern of the scale 51 sent almost at the same time as the image of the skull.
  • the image correction tool 62 performs distortion correction from the image or the image using the scale pattern distortion information of the scale pattern image of the scale 51 sent in advance by the same user.
  • the boundary / texture extraction tool 29 that outputs information having gradation values to which the image feature analysis method using the phase stretch transform, which is the boundary detection processing step shown in Patent Document 2 and Non-Patent Document 1, is applied to the background. Exclude brightness information.
  • the image data such as the texture and morphology of the mole 12 is extracted from the image by the feature parameter extraction tool 23. After that, this image data is classified by the image classification tool 24 and the risk determination tool 25 that have been machine-learned by the case learning data, and then the determination result is notified to the user.
  • the remote skin disease risk determination system 60 shown in FIG. 16 performs processing after the mole image is in a state where the color information is not distorted due to distortion and lighting, the influence of the image distortion information on the risk determination is reduced. At the same time, it is possible to obtain the effect that the risk can be determined with the image magnification information added.
  • FIG. 17 (a) a screen 100 requesting the image of the mole 12 is displayed as shown in FIG. 17 (a).
  • the screen 101 including the image of the mole taken as shown in FIG. 17 (b) is displayed. It is displayed, and the user is requested to confirm the image such as whether the mole 12 is photographed without missing or is in focus, and the user performs the confirmation work.
  • the image data is transferred to the service provider, and as shown in FIG. 17C, a screen 102 indicating that the image transfer is performed and the instruction to wait for the determination result is displayed is displayed. After that, as shown in FIG. 17D, the screen 103 showing the determination result is displayed. In this example, the user goes to the hospital and a screen indicating that a diagnosis at the hospital is desired is displayed.
  • FIG. 18 shows a process of acquiring image distortion information, which is a work performed by a user using a smartphone.
  • the user attaches a member 39 or a member 40 that holds the relative position between the position of the smartphone and the mole to the smartphone 11, and further attaches the scale member 51 shown in FIG. 13 to the member 39 or the member.
  • It is an image confirmation screen after attaching to 40 and taking a picture. The user confirms that the scales 52, 53, and 54 are captured in the focused state on this image, and transfers the captured image.
  • the present invention has an advantage that the burden on the patient is small because the risk of the skin disease such as melanoma can be evaluated without the patient going to the hospital. Also, on the medical institution side, there is an advantage that the burden on the medical staff is small because the equipment that has been machine-learned performs screening with learning data that reduces the risk of skin diseases.

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PCT/JP2022/000025 2021-01-06 2022-01-04 遠隔皮膚疾患リスク判定システムおよび遠隔皮膚疾患リスク判定方法 Ceased WO2022149566A1 (ja)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006090864A (ja) * 2004-09-24 2006-04-06 Denka Seiken Co Ltd ワクチン中の特定成分の含量を求める方法
JP2013007803A (ja) * 2011-06-23 2013-01-10 Access:Kk 商品撮影装置
US20170140545A1 (en) * 2014-05-05 2017-05-18 The Regents Of The University Of California Phase transform for object and shape detection in digital images
US20190188851A1 (en) * 2012-03-28 2019-06-20 University Of Houston System Methods for Screening and Diagnosing a Skin Condition
US20200336630A1 (en) * 2010-10-29 2020-10-22 The Regents Of The University Of California Cellscope apparatus and methods for imaging

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006090864A (ja) * 2004-09-24 2006-04-06 Denka Seiken Co Ltd ワクチン中の特定成分の含量を求める方法
US20200336630A1 (en) * 2010-10-29 2020-10-22 The Regents Of The University Of California Cellscope apparatus and methods for imaging
JP2013007803A (ja) * 2011-06-23 2013-01-10 Access:Kk 商品撮影装置
US20190188851A1 (en) * 2012-03-28 2019-06-20 University Of Houston System Methods for Screening and Diagnosing a Skin Condition
US20170140545A1 (en) * 2014-05-05 2017-05-18 The Regents Of The University Of California Phase transform for object and shape detection in digital images

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