Stains attribute determining method and stains attribute determining system

Abstract

A method for determining discoloration attributes, said method comprising demarcating a skin region including a discoloration, measuring the vascular density of the region, and determining attributes of the discoloration from the vascular density.

Description

[Technical field]

[0001] The present invention relates to a stain attribute determination method, and Stains attribute determination system M Regarding. [Background technology]

[0002] Various techniques have been conventionally known for evaluating pigmentation. For example, there is a technique for evaluating the recurrence of pigmentation after phototherapy of a pigmentation site based on the presence or absence of melanosomes or the blood flow rate at the pigmentation site after phototherapy (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2008-11994 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, with conventional techniques, it is not possible to evaluate spots until treatment such as phototherapy has been performed on the spots, making it difficult to predict in advance whether treatment will be effective on the spots.

[0005] An object of the present invention is to provide a method for determining blemish attributes that can easily evaluate blemishes. [Means for solving the problem]

[0006] In order to solve the above problems, one aspect of the present invention is a method for determining an attribute of a skin blemish by determining a blood vessel density of a skin region including the blemish, and determining an attribute of the blemish from the blood vessel density. the attribute of the pigmentation is the effectiveness of a laser treatment for the pigmentation, and it is predicted that the region having a high blood vessel density will be less effective in the laser treatment, and the region having a low blood vessel density will be more effective in the laser treatment, as an attribute of the pigmentation. A method for determining blemish attributes is provided. Effect of the Invention

[0007] According to one aspect of the present invention, a method for determining attributes of blemishes that can easily evaluate blemishes can be provided. [Brief description of the drawings]

[0008] [Figure 1] 3 is a flowchart showing an example of an algorithm for a blemish attribute determination method according to the present invention. [Diagram 2] FIG. 2 is a diagram showing the structure of a skin area (spot area). [Diagram 3] FIG. 1 illustrates the principle of visualizing a skin area. [Figure 4] FIG. 1 is a two-dimensional blood flow image of a skin region. [Diagram 5] FIG. 1 shows a three-dimensional blood flow image of a skin region. [Figure 6] 2 shows a model formula for calculating a blood flow image of a skin region. [Figure 7] 11 is a flowchart showing an example of an algorithm for calculating a correlation coefficient between blood vessel density in a skin region and an attribute of a blemish. [Figure 8] This is a diagram showing the surface and internal structure before treatment of a blemish area that has a high therapeutic effect. [Figure 9] FIG. 9 is a photograph showing the surface of the pigmented spot in FIG. 8 three months after treatment. [Figure 10] This is a diagram showing the surface and internal structure before treatment of a spot area where the treatment effect is low. [Figure 11] FIG. 11 is a photograph showing the surface of the pigmented spot in FIG. 10 three months after treatment. [Figure 12] FIG. 13 is a graph showing the correlation between blood vessel density and melanin value ratio in spots. [Figure 13] FIG. 13 is a diagram showing the correlation between blood vessel density and brightness ratio in a pigmented area. [Figure 14] FIG. 1 is a graph showing the relationship between blood vessel density in a pigmented area and the effect of laser treatment. [Figure 15] 1 is a block diagram showing an embodiment of a blemish attribute determination system according to the present invention. [Figure 16] 1 is a flowchart showing an example of an algorithm for a blood vessel density estimation method according to the present invention. [Figure 17] FIG. 1 shows a cross section of a skin area (normal site) with a reference epidermal thickness. [Figure 18] FIG. 1 is a diagram showing a cross section of a skin region including a blemished area and a normal area. [Figure 19] FIG. 1 shows the correlation between epidermal thickness ratio and vascular density in skin regions. [Figure 20] FIG. 1 is a diagram showing the correlation between epidermal thickness ratio and melanin value ratio in a skin region. [Figure 21] 1 is a block diagram showing an embodiment of a blood vessel density estimation system according to the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will now be described in detail with reference to the accompanying drawings. In addition, common parts in the drawings are designated by the same reference numerals, and the description thereof may be omitted.

[0010] <How to determine stain attributes> Fig. 1 is a flow chart showing an example of an algorithm of a blemish attribute determination method according to the present invention Fig. 2 is a diagram showing the configuration of a skin region (blemish site).

[0011] In the present example of the method for determining the attributes of pigmentation spots, first, a skin area SA including the pigmentation spots SS (hereinafter, sometimes referred to as skin area or pigmentation spot) is defined (see FIG. 1, step S1, FIG. 2). Here, the pigmentation spots SS indicate a state in which melanin pigmentation is deposited on the skin (see FIG. 2). Specific examples of pigmentation spots SS include, for example, senile lentigo (solar lentigo), seborrheic keratosis, freckles, melasma, and the like, as well as post-inflammatory pigmentation.

[0012] Moreover, the skin area (skin area) SA refers to the surface and internal area of ​​the skin. Specifically, the surface of the skin corresponds to the epidermis EM, and the internal area of ​​the skin corresponds to the dermis DM (see FIG. 2). Defining means treating the skin area SA including the blemish SS as a certain range (blemish area). The manner in which the skin area (blemish area) SA is defined is not limited. For example, the skin area (blemish area) SA may be defined visually by an expert or a skilled person, or may be defined mechanically by image analysis using a device.

[0013] In the present example of the blemish attribute determination method, the manner of identifying the blemish SS included in the blemish area SA is not limited. In the present example of the blemish attribute determination method, for example, the blemish SS can be identified based on the brightness or color of the skin area SA including the blemish SS. Here, the brightness of the skin area SA refers to the brightness of the surface of the blemish area SA. Also, the color of the skin area SA refers to the color of the surface of the blemish area SA.

[0014] Next, blood vessels BV in the skin area (spot area) SA are visualized (see FIG. 1, step S2, and FIG. 2). Specifically, an image of the defined skin area (spot area) SA is obtained. Here, the image of the skin area (spot area) SA refers to an image (or image data) obtained by photographing or capturing the defined skin area (spot area) SA.

[0015] The blemish attribute determination method of this example further measures the blood vessel density in the skin area (blemish site) SA (see FIG. 1, step S3, and FIG. 2). Here, blood vessel density refers to the degree to which blood vessels BV are densely packed (see FIG. 2). The manner in which blood vessel density is measured is not limited. In this example, blood vessel density is measured by image analysis of the image of the skin area SA as described above.

[0016] Image analysis refers to extracting basic elements from an image and obtaining statistical data. The image analysis may be performed in any manner, and may include, for example, image analysis that visualizes the skin area SA by binarizing the image of the skin area SA.

[0017] In the present example of the blemish attribute determination method, the subject of the image used in the image analysis for measuring blood vessel density is not limited. In the present example of the blemish attribute determination method, the image used in the image analysis is preferably a blood flow image of the skin area SA. Here, the blood flow image refers to an image of the area where blood flows in the skin area SA.

[0018] FIG. 3 is a diagram showing the principle of visualizing a skin region. In this example, an image such as a blood flow image is formed by an optical coherence tomography (hereinafter sometimes referred to as OCT) device VD shown in FIG. 3. Here, the optical coherence tomography device VD is a device that irradiates a skin region SA with low-coherence near-infrared light from a light source LS, and visualizes the skin region SA in a non-contact manner by interference with the reflected near-infrared light (see FIG. 3). The wavelength of the near-infrared light is arbitrary. In this embodiment, the wavelength of the irradiated near-infrared light is set to about 1300 nm.

[0019] In addition, images such as blood flow images are not limited to those formed by OCT. Examples of images obtained by techniques other than OCT include images obtained by techniques such as laser speckle blood flow meters, Doppler blood flow meters, and video microscopes.

[0020] A two-dimensional planar image IM2 can be used for the image of the skin area SA (see FIG. 4). In the planar image IM2, black and white in the image reflect the intensity of light reflected from the tissue, and an image of a moving area represents a blood flow image BF.

[0021] Also, for the image of the skin area SA, a three-dimensional image IM3 can be used instead of the planar image (two-dimensional image) IM2 (see FIG. 5). Here, the three-dimensional image IM3 indicates a stereoscopic image expressed in a three-dimensional orthogonal coordinate system (X-axis, Y-axis, Z-axis). In the example shown in FIG. 5, a three-dimensional image IM3e having a depth of 200 μm from the surface of the skin area SA and a three-dimensional image IM3d having a depth of 400 μm from the surface of the skin area SA are shown as the three-dimensional image IM3.

[0022] The blood flow image BF can be calculated by a predetermined model formula (1) shown in Fig. 6. When the blood flow image BF is a two-dimensional image, the blood vessel density indicates the ratio (%) of the area of ​​the blood vessels BV (blood flow image BF) to the area of ​​the defined skin area (spot area) SA, and when the blood flow image BF is a three-dimensional image, the blood vessel density indicates the ratio (%) of the volume of the blood vessels BV (blood flow image BF) to the volume of the defined skin area (spot area) SA.

[0023] In the present example of the blemish attribute determination method, the location of the skin area SA from which the image used for image analysis is obtained is not limited. In the present example of the blemish attribute determination method, the image of the skin area SA is an image (three-dimensional image IM3e) obtained from a range of, for example, 50 μm to 600 μm deep from the surface of the skin area SA, preferably 200 μm to 500 μm deep, more preferably 300 μm to 400 μm deep (three-dimensional image IM3d).

[0024] Here, the range of depth from the surface of the skin region SA of 50 μm or more and 600 μm or less roughly corresponds to the range including the epidermis and dermis, the range of 200 μm or more and 500 μm or less roughly corresponds to the range including the dermis, and the range of 300 μm or more and 400 μm or less roughly corresponds to a portion of the dermis.

[0025] In the blemish attribute determination method of this example, the attribute of the blemish SS is further determined from the blood vessel density (see FIG. 1, step S4, and FIG. 2). Here, the attribute of the blemish SS (hereinafter sometimes referred to as the blemish attribute) refers to the inherent properties and characteristics of the blemish SS. Determining the blemish attribute from the blood vessel density refers to distinguishing the blemish attribute based on the blood vessel density.

[0026] In addition, if the blemish attribute of a target skin area (blemish area) SA is to be determined after the blemish attribute of another skin area (blemish area) SA has been determined, the process from defining the blemish area SA to determining the blemish attribute (see Figure 1, steps S1 to S4) is repeated.

[0027] In the present example of the method for determining the attributes of the blemishes SS, the attributes of the blemishes SS determined from the blood vessel density are not limited. In the present example of the method for determining the attributes of the blemishes SS, the effectiveness of laser treatment for the blemishes SS is determined as the attribute of the blemishes SS. Here, the laser treatment is a type of light therapy, and refers to a treatment in which melanin, which causes the blemishes SS, is selectively destroyed by irradiating a laser, thereby eliminating the blemishes SS. The effectiveness of the treatment refers to the magnitude of the effect of the treatment.

[0028] Fig. 7 is a flow chart showing an example of an algorithm for calculating a correlation coefficient between blood vessel density in a skin region (spot area) and a spot attribute. First, the blood vessel density and melanin value Mb of the skin region (spot area) SA before laser treatment are measured (see Figs. 2, 7, step S11, 8, and 10).

[0029] The melanin value indicates the blackness of the skin area (spot area) SA. The melanin value is an example of an index that indicates the color of the skin, and is inversely proportional to the brightness. The method for measuring the melanin value is arbitrary. In this example, the melanin value Mb was measured using a skin analyzer (ANTERA 3D, manufactured by Gadelius Medical Co., Ltd.).

[0030] Next, the skin area (spot area) SA where the melanin value Mb was measured is subjected to laser treatment (see FIG. 2, FIG. 7, step S12). Specifically, the spot area SA is irradiated with a laser to eliminate (or lighten) the spots SS contained in the skin area SA.

[0031] For the skin area SA after the laser treatment, the melanin value Ma is measured three months after the laser treatment (see Fig. 2, Fig. 7, step S13, Fig. 9, Fig. 11). The measurement of the melanin value Ma is performed in the same manner as the measurement of the melanin value Mb of the skin area (spot area) SA before the laser treatment (Fig. 7, step S11).

[0032] Next, the ratio Ma / Mb (hereinafter, the melanin value ratio Mb) of the melanin value Ma after laser treatment to the melanin value Mb before laser treatment in the skin area (spot area) SA was calculated. *(See FIG. 2, FIG. 7, step S14). * The higher the value, the lower the effect of laser treatment, and the melanin value ratio M * The lower the value, the higher the effect of laser treatment.

[0033] And the blood vessel density and melanin value ratio M in the skin area (spot area) SA * In this example, the correlation coefficient between the blood vessel density and the melanin value ratio M in the skin area (spot area) SA is calculated for 13 cases from 11 subjects. * The measured data was plotted, and the correlation coefficient was 0.63 (see FIG. 12).

[0034] This allows the blood vessel density and melanin value ratio M * This correlation is between the high blood vessel density in the skin area (spot area) SA and the melanin value ratio M * When the blood vessel density of the skin area (spot area) SA is low, the melanin value ratio M * In other words, it can be predicted that the effect of laser treatment will be low in skin areas (spot areas) SA with high blood vessel density, and that the effect of laser treatment will be high in skin areas (spot areas) SA with low blood vessel density (see Figure 14).

[0035] In addition, the brightness L of the skin area (spot area) SA 12 weeks after laser treatment * a and brightness before laser treatment L * Relative to b L * a / L * b (hereinafter, the brightness ratio L * The brightness Lr was calculated. The brightness may be measured by any method. In this example, a skin analyzer (ANTERA 3D, manufactured by Gadelius Medical Co., Ltd.) was used to measure the brightness Lr. * a, L * The brightness ratio L * The higher the value of r, the higher the effectiveness of laser treatment, and the brightness ratio L * A lower value of r indicates a lower effect of laser treatment.

[0036] Blood vessel density in the skin area (spot area) SA and the resulting brightness ratio L * The correlation coefficient with r was calculated. * For r, the data measured for the above-mentioned 11 subjects (13 cases) were plotted, and the correlation coefficient was −0.629 (see FIG. 13).

[0037] This allows the blood vessel density and brightness ratio L * It was also found that there is a correlation between the brightness ratio L and the blood vessel density in the skin area (spot area) SA. * When the r is low and the blood vessel density in the skin area (spot area) SA is low, the brightness ratio L * In other words, it can be predicted that the effect of laser treatment will be low in skin areas (spot areas) SA with high blood vessel density, and that the effect of laser treatment will be high in skin areas (spot areas) SA with low blood vessel density (see Figure 14).

[0038] As described above, the inventors of the present invention have found that in the skin region (hereinafter referred to as the blemish site) SA containing the blemish SS, the blood vessel density may be high or low, and that the attributes of the blemish SS contained in the blemish site SA differ depending on the blood vessel density. Specifically, after laser treatment of the blemish site SA, the blemish SS may disappear and remain, or the blemish SS may recur, and it has been found that this difference in treatment effect tends to occur depending on the blood vessel density. In other words, it has been found that there is a correlation between the attributes of the blemish SS and the blood vessel density of the blemish site SA.

[0039] The age spot attribute determination method of this example was obtained from such considerations, and by determining the age spot attribute from the blood vessel density of the defined skin area SA, the nature and characteristics of the age spot area SA can be ascertained simply by measuring the blood vessel density of the skin area SA. As a result, in this example, the age spot attribute, such as the presence or absence of a treatment effect on the age spot area SA, can be predicted in advance. Therefore, according to the age spot attribute determination method of this example, the age spot SS can be easily evaluated before performing treatment such as phototherapy.

[0040] In the present example of the blemish attribute determination method, the blood vessel density is measured by image analysis of the skin area SA, thereby making it possible to obtain objective information about the blood vessel density of the skin area SA. Therefore, by determining the blemish attribute from the blood vessel density measured by image analysis of the skin area SA, the blemish attribute can be determined with high accuracy.

[0041] In the present example of the blemish attribute determination method, the blood vessel density is measured by image analysis of the blood flow image BF of the skin area SA, thereby making it possible to obtain more objective information about the blood vessel density of the skin area SA. Therefore, by determining the blemish attribute from the blood vessel density measured by image analysis of the blood flow image BF of the skin area SA, the blemish attribute can be determined with higher accuracy.

[0042] In the present example of the blemish attribute determination method, by measuring the blood vessel density through image analysis of the three-dimensional image IM3 of the skin area SA, more objective information on the blood vessel density of the skin area SA can be obtained. Therefore, by determining the blemish attribute based on the blood vessel density measured through image analysis of the three-dimensional image IM3 of the skin area SA, the blemish attribute can be determined with even higher accuracy.

[0043] In the blemish attribute determination method of this example, blood vessel density is measured by image analysis of an image obtained from a range of 50 μm to 600 μm deep from the surface of the skin area SA, thereby making it possible to obtain complete information on the blood vessel density of the skin area SA. Therefore, by determining the blemish attribute based on the blood vessel density measured by image analysis of an image obtained from a portion of the skin area SA whose depth from the surface of the skin area SA falls within such a range, the blemish attribute can be determined with even higher accuracy.

[0044] In the present example of the blemish attribute determination method, the skin area SA containing the blemish SS can be objectively defined by identifying the blemish SS based on the brightness or color of the skin area SA containing the blemish SS. Therefore, by determining the blemish attribute from the blood vessel density of the skin area SA in which the blemish SS is identified in this way, the blemish attribute can be determined with high accuracy.

[0045] In the pigmentation attribute determination method of the present embodiment, the effectiveness of laser treatment for pigmentation SS is determined as an attribute of pigmentation SS, and the effectiveness of laser treatment for pigmentation area SA can be predicted in advance simply by measuring the blood vessel density in the skin area SA. Therefore, according to the present embodiment, evaluation of pigmentation SS can be more easily performed before performing laser treatment on pigmentation area SA.

[0046] <Stain attribute determination system> Fig. 15 is a block diagram showing an embodiment of a blemish attribute determination system according to the present invention. The blemish attribute determination system 1 according to this embodiment has an information input section 10, an image forming section 20, a blood vessel density measuring section 30, a blemish attribute determination section 40, an information output section 50, a central processing unit (CPU) 60, and a memory 70 (Fig. 15). The blemish attribute determination system 1 is an example of the blemish attribute determination system according to the present invention, and can execute the blemish attribute determination method according to the present invention.

[0047] The information input unit 10 is an interface that can input various information about the subject (e.g., identification number, sex, age, location of spots, etc.) (see FIG. 15). The information input unit 10 is communicatively connected to a central processing unit (CPU) 60 and a memory 70 (see FIG. 15). The information input unit 10 is controlled by the central processing unit (CPU) 60. The input information can be stored in the memory 70.

[0048] The image forming unit 20 forms images (images IM2, IM3, etc.) of the skin area (spot area) SA including the blemish SS (see Figs. 2, 4, 5, and 15). Specifically, the image forming unit 20 executes a part of the blemish attribute determination method described above (Fig. 1, step S2).

[0049] The image forming unit 20 is communicatively connected to the information input unit 10, a central processing unit (CPU) 60, and a memory 70 (see FIG. 15). The image forming unit 20 is controlled by the central processing unit (CPU) 60, and image data obtained by the image forming unit 20 can be stored in the memory 70. The image forming unit 20 is an example of an image forming unit that constitutes a part of the blemish attribute determination system according to the present invention.

[0050] The blood vessel density measuring unit 30 measures the blood vessel density of the skin area (spot area) SA from the image IM of the skin area (spot area) SA (see Figs. 2, 4, 5, and 15). Specifically, the blood vessel density measuring unit 30 executes a part of the above-mentioned blemish attribute determination method (Fig. 1, step S3).

[0051] The blood vessel density measuring unit 30 is communicatively connected to the image forming unit 20, a central processing unit (CPU) 60, and a memory 70 (see FIG. 15). The blood vessel density measuring unit 30 is controlled by the central processing unit (CPU) 60, and blood vessel density information obtained by the blood vessel density measuring unit 30 can be stored in the memory 70. The blood vessel density measuring unit 30 is an example of a blood vessel density measuring unit that constitutes a part of the blemish attribute determination system according to the present invention.

[0052] The blemish attribute determining unit 40 determines the attribute of the blemish SS from the blood vessel density in the skin area (blemish site) SA (see Figs. 2 and 15). Specifically, the blemish attribute determining unit 40 executes a part of the blemish attribute determining method described above (Fig. 1, step S4).

[0053] The blemish attribute determination section 40 is communicatively connected to the blood vessel density measurement section 30, a central processing unit (CPU) 60, and a memory 70 (see FIG. 15). The blemish attribute determination section 40 is controlled by the central processing unit (CPU) 60, and information on the determination result obtained by the blemish attribute determination section 40 can be stored in the memory 70. The blemish attribute determination section 40 is an example of a blemish attribute determination section that constitutes a part of the blemish attribute determination system according to the present invention.

[0054] The information output unit 50 is an interface that can output information on the determination result in the blemish attribute determination unit 40 to the outside of the blemish attribute determination system 1 (see FIG. 15). The information output unit 50 is communicatively connected to the blemish attribute determination unit 40, a central processing unit (CPU) 60, and a memory 70 (see FIG. 15). The information output unit 50 is controlled by the central processing unit (CPU) 60.

[0055] The information output section 50 may directly output information on the determination result in the blemish attribute determination section 40, or may read and output information on the determination result stored in the memory 70 from the memory 70. Furthermore, the information output section 50 may output various information on the subject, image data of the skin area (blemish site) SA, and information on blood vessel density in addition to the determination result in the blemish attribute determination section 40.

[0056] A display device (not shown) capable of wired or wireless communication may be connected to the information output unit 50. Specific examples of the display device include a display of a personal computer or the like when the information output unit 50 and the display device are connected by wire. Also, examples of the display device include a display of a general-purpose mobile terminal such as a smartphone when the blemish attribute determination system 1 and the display device are connected wirelessly.

[0057] The central processing unit (CPU) 60 is a processor that controls the information input unit 10, the image forming unit 20, the blood vessel density measuring unit 30, the blemish attribute determining unit 40, the information output unit 50, and the memory 70 (see FIG. 15). As described above, the central processing unit (CPU) 60 is connected to the information input unit 10, the image forming unit 20, the blood vessel density measuring unit 30, the blemish attribute determining unit 40, the information output unit 50, and the memory 70 (see FIG. 15).

[0058] The memory 70 stores various information (information on the subject, image data of the skin area (spot area) SA, blood vessel density, correlation coefficient between blood vessel density and melanin value ratio, correlation coefficient between blood vessel density and brightness ratio, other information on the determination results, etc.) (see FIG. 15). As described above, the memory 70 is connected to the information input unit 10, image forming unit 20, blood vessel density measuring unit 30, blemish attribute determining unit 40, information output unit 50, and central processing unit (CPU) 60 (see FIG. 15). In the example shown in FIG. 15, the memory 70 is arranged independently of the central processing unit (CPU) 60, but this embodiment is not limited to this configuration, and the memory 70 may be arranged inside the central processing unit (CPU) 60.

[0059] In order to determine the blemish attribute with high accuracy, the image IM used for image analysis is preferably a blood flow image BF of the skin area SA in the blemish attribute determination system 1. In the blemish attribute determination system 1 of this embodiment, the blemish attribute is determined from the blood vessel density measured by image analysis of the blood flow image BF of the defined skin area SA (FIG. 1, steps S1 to S4).

[0060] The blemish attribute determination system 1 of the embodiment is a system that essentially executes the blemish attribute determination method of the present embodiment described above. That is, in the present embodiment, a skin area SA including a blemish SS is defined, the blood vessel density of the skin area SA is measured, and the attribute of the blemish SS can be determined from the blood vessel density (see Figs. 1 and 2).

[0061] As a result, in this embodiment, by simply measuring the blood vessel density in the skin area SA, the nature and characteristics of the blemish site SA can be ascertained, and the blemish attributes, such as the presence or absence of a treatment effect for the blemish site SA, can be predicted in advance. Therefore, according to this embodiment, the blemish SS can be easily evaluated before treatment such as phototherapy is performed.

[0062] In addition, in this embodiment, by measuring the blood vessel density through image analysis of the blood flow image BF of the skin area SA, more objective information on the blood vessel density of the skin area SA can be obtained. Therefore, by determining the blemish attribute from the blood vessel density measured through image analysis of the blood flow image BF of the skin area SA, the blemish attribute can be determined with higher accuracy.

[0063] <Vessel density estimation method> Fig. 16 is a flow chart showing an example of an algorithm for the blood vessel density estimation method according to the present invention. Fig. 17 is a diagram showing a cross section of a skin region (normal area) having a reference epidermal thickness, and Fig. 18 is a diagram showing a cross section of a skin region including a blemished area and a normal area. In Figs. 16 to 18, parts common to Figs. 1 and 2 may be given the same or corresponding reference numerals and explanations thereof may be omitted.

[0064] In the blood vessel density estimation method of this example, first, a skin area is defined (FIG. 16, step S21). The skin area to be measured may be a skin area of ​​a blemish (a skin area including a blemish), or may be a skin area that is unknown in advance as being a blemish or not. The skin area to be measured is, for example, skin areas SA1 and SA2 (FIGS. 17 and 18).

[0065] Skin area SA1 is a skin area in a normal area (not a blemished area) and is composed of epidermis EM1 and dermis DM1. The epidermis EM1 of skin area SA1 has a thickness (epidermal thickness) ET1. The epidermal thickness ET1 of the epidermis EM1 of skin area SA1 (normal area) corresponds to the reference epidermal thickness described below. Furthermore, blood vessels BV1 are densely concentrated in the dermis DM1 of skin area SA1.

[0066] Skin area SA2 is a skin area in a blemish area, and is composed of epidermis EM2 and dermis DM2. The epidermis EM1 of skin area SA1 has a thickness (epidermal thickness) ET2. The epidermal thickness ET2 of skin area SA2 (blemish area) is thicker than the epidermal thickness ET1 (reference epidermal thickness) of skin area SA1 (normal area). Furthermore, blood vessels BV2 are densely packed in the dermis DM2 of skin area SA2. The blood vessels BV2 of skin area SA2 are denser than the dermis DM1 of skin area SA1.

[0067] Next, the epidermal thicknesses ET1 and ET2 of the defined skin regions SA1 and SA2 are measured (see FIG. 16, step S22, and FIG. 17). In the blood vessel density estimation method of this example, the epidermal thicknesses ET1 and ET2 of the skin regions SA1 and SA2 are measured by image analysis of each image of the skin regions SA1 and SA2. Note that the image analysis of each image of the skin regions SA1 and SA2 can use a method for visualizing the skin region as described above (a method using OCT or the like) (see FIG. 3).

[0068] The images to be analyzed are images of the epidermis EM1 and EM2 of the skin regions SA1 and SA2. That is, the locations of the skin regions SA1 and SA2 from which the images to be used in the image analysis are obtained are the epidermis EM1 and EM2 of the skin regions SA1 and SA2.

[0069] In the present example of the vascular density estimation method, each image of skin regions SA1, SA2 is an image (three-dimensional image IM3e) obtained from a depth from the surface of skin regions SA1, SA2 (epidermal thickness EM1, EM2) in the range of, for example, 50 μm or more and 600 μm or less, preferably 50 μm or more and 300 μm or less, and more preferably 50 μm or more and 200 μm or less (three-dimensional image IM3d) (see FIG. 5).

[0070] That is, the range of depth from the surface of skin regions SA1, SA2 from 50 μm to 600 μm roughly corresponds to the range including epidermis EM1, EM2 and dermis DM1, DM2, the range of 50 μm to 400 μm roughly corresponds to the range including epidermis EM1, EM2 and portions of dermis DM1, DM2, and the range of 300 μm to 200 μm roughly corresponds to portions of dermis DM1, DM2.

[0071] In the blood vessel density estimation method of this example, the ratio of the measured epidermal thickness to a predetermined reference epidermal thickness is calculated as the epidermal thickness ratio (see FIG. 16, step S23, and FIG. 17). Here, the predetermined reference epidermal thickness indicates the epidermal thickness of a skin region of a predetermined normal part. The epidermal thickness ratio is expressed as MT / ST, where ST is the predetermined reference epidermal thickness and MT is the measured epidermal thickness.

[0072] When the epidermal thickness ratio MT / ST is greater than 1, it indicates that the epidermal thickness of the measured skin area is thicker than that of the skin area of ​​a normal site. When the epidermal thickness ratio MT / ST is 1, it indicates that the epidermal thickness of the measured skin area is equivalent to that of a normal site.

[0073] In this example, for example, the epidermal thickness of the epidermis EM1 in the skin area SA1 (normal area) is measured in advance, and the obtained epidermal thickness ET1 can be set as a predetermined reference epidermal thickness. Also, the epidermal thickness ET2 of the epidermis EM2 in the skin area SA2 (spot area) can be measured, and the obtained epidermal thickness ET2 can be set as the measured epidermal thickness.

[0074] In the blood vessel density estimation method of this example, the blood vessel density of the skin region is further estimated from the calculated skin thickness ratio MT / ST (see FIG. 16, step S24, and FIG. 17). Here, the blood vessel density indicates the degree to which blood vessels are densely packed in the skin region. Estimating the blood vessel density from the epidermal thickness ratio means measuring the blood vessel density based on the epidermal thickness ratio.

[0075] In addition, if the blood vessel density is to be estimated for another skin area after the blood vessel density has been estimated for the target skin areas SA1 and SA2, the processes from defining the skin area to estimating the blood vessel density are repeated (Figure 16, steps S21 to S24).

[0076] The blood vessel density can be calculated, for example, as follows. First, the blood vessel density (reference blood vessel density) MD1 of the skin area SA1 (normal area) having a predetermined reference epidermal thickness and the blood vessel density MD2 of the skin area SA2 whose epidermal thickness has been measured are measured by measuring the blood vessel density using the blood vessel density measurement method described above. Then, the ratio of the blood vessel density MD2 of the skin area SA2 to the predetermined reference blood vessel density MD1 (blood vessel density ratio MD2 / MD1) is calculated as the blood vessel density.

[0077] FIG. 19 is a diagram showing the correlation between the epidermal thickness ratio and the blood vessel density in a skin region. * ) and the blood vessel density ratio MD2 / MD1 (Figure 19). Here, the epidermal thickness ratio MT / ST (E * The N number for investigating the relationship between the vascular density ratio MD2 / MD1 and the vascular density ratio MD2 / MD1 was 25 (Figure 19).

[0078] In the blood vessel density estimation method of this example, the attribute of a blemish (blemish attribute) occurring in a skin region can be determined from the estimated blood vessel density. Here, determining the blemish attribute from the blood vessel density means distinguishing the blemish attribute based on the blood vessel density.

[0079] The method for estimating vascular density in this example can be seen from another perspective as * ) the attribute of the blemish can be determined. Here, determining the attribute of the blemish from the epidermal thickness ratio means distinguishing the attribute of the blemish based on the epidermal thickness ratio. In addition, it may be known in advance that the skin area determined when calculating the epidermal thickness ratio is a blemish site (an area of ​​the skin that includes a blemish).

[0080] In the blood vessel density estimation method of this example, the manner in which the blemishes included in the blemish site are identified is not limited. In the blood vessel density estimation method of this example, for example, the blemishes can be identified based on the brightness or color of the skin area SA2 including the blemishes.

[0081] In the blood vessel density estimation method of this example, the attribute of the blemish determined from the blood vessel density is not limited. For example, the attribute of the blemish determined by the blood vessel density estimation method of this example can be the effectiveness of laser treatment for the blemish, as in the above-mentioned blemish attribute determination method.

[0082] Here, for the skin area SA2, the blood vessel density and melanin value Mb before the laser treatment and the melanin value Ma three months after the laser treatment were measured in the same manner as in the above-mentioned pigmentation attribute determination method, and the melanin value ratio Ma / Mb (M * ) is calculated (see FIG. 7, steps S11 to S14, FIG. 8, FIG. 10, FIG. 18). The skin area SA2 corresponds to the skin area (spot area) SA where the melanin value Mb was measured by the above-mentioned spot attribute determination method (see FIG. 2).

[0083] And the epidermal thickness ratio E of the skin area (spot area) SA2 * and melanin value ratio M * In this example, the epidermal thickness ratio E of the skin area (spot area) SA2 is calculated for 12 cases of 11 subjects (FIG. 7, step S15). * and melanin value ratio M * The measured data was plotted, and the correlation coefficient was 0.75 (see FIG. 20).

[0084] As a result, the epidermal thickness ratio E * and melanin value ratio M * This correlation is due to the epidermal thickness ratio E * When the melanin value ratio M * The epidermal thickness ratio E * When the melanin value ratio M * This indicates that the

[0085] In other words, skin areas with a thicker epidermal thickness ratio tend to have a higher blood vessel density, while skin areas with a thinner epidermal thickness ratio tend to have a lower blood vessel density. As a result, it can be predicted that skin areas with a thicker epidermal thickness ratio will be less effective with laser treatment, and skin areas with a thinner epidermal thickness ratio will be more effective with laser treatment (Figure 20).

[0086] The blood vessel density estimation method of this example was derived from such considerations, and by estimating the blood vessel density of a defined skin region from the epidermal thickness ratio (the ratio of measured epidermal thickness to a predetermined reference epidermal thickness) in the skin region, it is possible to determine the nature and characteristics of the skin region (whether the skin region is normal or a blemished area, etc.) simply by measuring the epidermal thickness of the skin region.

[0087] In the present embodiment, the vascular density estimation method employs the vascular density ratio (the ratio of the vascular density of a region to a predetermined reference vascular density) as the vascular density, thereby deriving the correlation between the epidermal thickness ratio and the vascular density in a skin region, thereby obtaining objective information on the vascular density of a skin region.

[0088] In the blood vessel density estimation method of this example, the epidermal thickness is measured by image analysis of the image of the skin region, so that more objective information on the blood vessel density of the skin region can be obtained. Therefore, the blood vessel density can be estimated with high accuracy by calculating the epidermal thickness ratio from the epidermal thickness measured by image analysis of the image of the skin region.

[0089] In the blood vessel density estimation method of this example, more objective information on the epidermal thickness of a skin region can be obtained by performing image analysis on an image of the epidermis of a skin region. Therefore, the blood vessel density can be estimated with higher accuracy by calculating the epidermal thickness ratio from the epidermal thickness measured by image analysis on the image of the epidermal thickness of the skin region.

[0090] In the blood vessel density estimation method of this example, a three-dimensional image is used as the image of the skin region, so that more objective information on the epidermal thickness of the skin region can be obtained. Therefore, by calculating the epidermal thickness ratio based on the epidermal thickness measured by image analysis of the three-dimensional image of the skin region, the blood vessel density can be estimated with higher accuracy.

[0091] In the blood vessel density estimation method of this example, the epidermal thickness is measured by image analysis of an image obtained from a region of the skin region having a depth from the surface of the skin region of 50 μm to 600 μm, thereby obtaining complete information on the epidermal thickness of the skin region. Therefore, the blood vessel density can be estimated with higher accuracy by calculating the epidermal thickness ratio based on the epidermal thickness measured by image analysis of an image obtained from a portion of the skin region having a depth from the surface of the skin region in such a range.

[0092] In the blood vessel density estimation method of this example, by determining the attribute of the blemish occurring in the skin region from the estimated blood vessel density, it is possible to determine whether the skin region is a normal region or a blemish region simply by measuring the epidermal thickness of the skin region. This makes it possible to predict whether a skin region that is unclear whether it is a blemish region is a normal region or a blemish region. Therefore, according to the blood vessel density estimation method of this example, it is possible to determine in advance whether the skin region is suitable for performing treatment such as phototherapy.

[0093] In the blood vessel density estimation method of this example, a skin region containing a blemish is defined, the epidermal thickness of the defined skin region is measured, and the attribute of the blemish is determined from the calculated epidermal thickness ratio, thereby making it possible to determine the nature and characteristics of the blemish site. This makes it possible to predict in advance the blemish attribute, such as the presence or absence of a treatment effect on the blemish site. Therefore, according to the blood vessel density estimation method of this example, it is possible to easily evaluate the blemish before performing treatment such as phototherapy.

[0094] In the blood vessel density estimation method of this example, the skin area containing the blemish can be objectively defined by identifying the blemish based on the brightness or color of the skin area containing the blemish. Therefore, the blemish attribute can be determined with high accuracy by determining the blemish attribute from the blood vessel density of the skin area in which the blemish is identified.

[0095] In the blood vessel density estimation method of the present embodiment, the effectiveness of laser treatment for a blemish is determined as an attribute of the blemish, and the effectiveness of the laser treatment for the blemish can be predicted in advance simply by measuring the blood vessel density in the skin area. Therefore, according to the present embodiment, it is possible to more easily evaluate the blemish before performing the laser treatment on the blemish.

[0096] <Vascular Density Estimation System> Fig. 21 is a block diagram showing an embodiment of a blood vessel density estimation system according to the present invention. In Fig. 21, parts common to Fig. 15 may be denoted by the same or corresponding reference numerals and description thereof may be omitted.

[0097] The vascular density estimation system 100 according to this embodiment includes an information input section 110, an image forming section 120, an epidermal thickness measurement section 131, an epidermal thickness ratio calculation section 132, a vascular density estimation section 133, a blemish attribute determination section 140, an information output section 150, a central processing unit (CPU) 160, and a memory 170 (FIG. 21). The vascular density estimation system 100 is an example of a vascular density estimation system according to the present invention, and can execute a vascular density estimation method according to the present invention.

[0098] The information input unit 110 can input various information about the subject (for example, identification number, sex, age, position of normal areas, position of spots, etc.) (FIG. 21).

[0099] The image forming unit 120 forms images (images IM2, IM3, etc.) of the skin area SA1 (normal area) and the skin area SA2 (spot area) (see Figs. 2, 4, and 5). Specifically, the image forming unit 120 executes a part of the above-mentioned blood vessel density estimation method (Fig. 16, step S21) to determine each skin area (skin areas SA1, SA2).

[0100] The image forming unit 120 is communicatively connected to the information input unit 110, the epidermal thickness measuring unit 131, a central processing unit (CPU) 160, and a memory 170 (FIG. 21). The image forming unit 120 is controlled by the central processing unit (CPU) 160, and image data obtained by the image forming unit 120 can be stored in the memory 70. The image forming unit 120 is an example of an image forming unit that constitutes a part of the blemish attribute determination system according to the present invention.

[0101] The epidermal thickness measuring unit 131 measures the epidermal thicknesses ET1 and ET2 of the skin regions from the images IM3 of the skin regions (skin regions SA1 and SA2) (FIGS. 16 to 18). Specifically, the epidermal thickness measuring unit 131 executes a part of the above-mentioned blemish attribute determination method (FIG. 16, step S22).

[0102] The epidermal thickness measuring unit 131 is communicatively connected to the image forming unit 120, the epidermal thickness ratio calculating unit 132, the central processing unit (CPU) 160, and the memory 170 (FIG. 21). The epidermal thickness measuring unit 131 is controlled by the central processing unit (CPU) 160, and information on the epidermal thickness obtained by the epidermal thickness measuring unit 131 can be stored in the memory 70. The epidermal thickness measuring unit 131 is an example of an epidermal thickness measuring unit constituting a part of the blood vessel density estimation system according to the present invention.

[0103] The epidermal thickness ratio calculation unit 132 calculates the ratio of the epidermal thickness ET2 of the measured skin area SA2 to the epidermal thickness ET1 of the skin area SA1 (a predetermined reference epidermal thickness) (epidermal thickness ratio ET2 / ET1(E * ) is calculated (FIGS. 16 to 18). Specifically, the epidermal thickness ratio calculation unit 132 executes a part of the above-mentioned blood vessel density estimation method (FIG. 16, step S23).

[0104] The epidermal thickness ratio calculation unit 132 is communicatively connected to the epidermal thickness measurement unit 131, the blood vessel density estimation unit 133, the central processing unit (CPU) 160, and the memory 170 (FIG. 21). The epidermal thickness ratio calculation unit 132 is controlled by the central processing unit (CPU) 160, and information on the epidermal thickness ratio obtained by the epidermal thickness ratio calculation unit 132 can be stored in the memory 170. The epidermal thickness ratio calculation unit 132 is an example of an epidermal thickness ratio calculation unit constituting a part of the blood vessel density estimation system according to the present invention.

[0105] The blood vessel density estimation unit 133 calculates the epidermal thickness ratio ET2 / ET1 (E * ) to estimate the blood vessel density of the skin area SA2 (FIGS. 16 to 18). Specifically, the blood vessel density estimation unit 133 executes a part of the above-mentioned blood vessel density estimation method (FIG. 16, step S24).

[0106] The vascular density estimation unit 133 is communicatively connected to the epidermal thickness ratio calculation unit 132, the blemish attribute determination unit 140, the central processing unit (CPU) 160, and the memory 170 (FIG. 21). The vascular density estimation unit 133 is controlled by the central processing unit (CPU) 160, and information on vascular density obtained by the vascular density estimation unit 133 can be stored in the memory 170. The vascular density estimation unit 133 is an example of a vascular density estimation unit constituting a part of the vascular density estimation system according to the present invention.

[0107] The blemish attribute determining unit 140 determines the attributes of blemishes occurring in the skin area SA2 from the estimated blood vessel density (see Figs. 17 to 19). Specifically, the blemish attribute determining unit 140 executes a part of the above-mentioned blemish attribute determining method (determination of the attributes of blemishes occurring in the skin area).

[0108] The blemish attribute determination unit 140 is communicatively connected to the blood vessel density estimation unit 133, a central processing unit (CPU) 160, and a memory 170 (FIG. 21). The blemish attribute determination unit 140 is controlled by the central processing unit (CPU) 160, and information on the determination result obtained by the blemish attribute determination unit 140 can be stored in the memory 170. The blemish attribute determination unit 140 is an example of a blemish attribute determination unit that constitutes a part of the blood vessel density estimation system according to the present invention.

[0109] The information output section 150 can output information such as the determination result in the blemish attribute determining section 40, various information on the subject, image data of the skin area (normal area, blemish area), reference epidermal thickness, reference blood vessel density, and the like.

[0110] The central processing unit (CPU) 160 controls the information input unit 110, the image forming unit 120, the epidermal thickness ratio calculation unit 132, the blood vessel density estimation unit 133, the blemish attribute determination unit 140, the information output unit 150, and the memory 170 (FIG. 21). As described above, the central processing unit (CPU) 160 is connected to the information input unit 110, the image forming unit 120, the epidermal thickness ratio calculation unit 132, the blood vessel density estimation unit 133, the blemish attribute determination unit 140, the information output unit 150, and the memory 70 (FIG. 21).

[0111] The memory 170 stores various information (information on the subject, positions of normal areas, positions of blemishes, image data of skin areas (normal areas, blemishes), epidermal thickness, epidermal thickness ratio, vascular density (vascular density ratio), vascular density, correlation coefficient between epidermal thickness ratio and vascular density, correlation coefficient between epidermal thickness ratio and melanin value ratio, and other information on the results of assessment) ( FIG. 21 ).

[0112] As described above, the memory 170 is connected to the information input section 110, the image forming section 120, the epidermal thickness ratio calculation section 132, the blood vessel density estimation section 133, the blemish attribute determination section 140, the information output section 150, and the central processing unit (CPU) 160 (FIG. 21). Note that in the example shown in FIG. 15, the memory 170 is arranged independently of the central processing unit (CPU) 160, but the present embodiment is not limited to this configuration, and the memory 170 may be arranged inside the central processing unit (CPU) 160.

[0113] The blood vessel density estimation system 100 of the embodiment is a system that substantially executes the blood vessel density estimation method of the present embodiment described above. That is, the blood vessel density estimation system 100 of the present embodiment can define a skin area, measure the epidermal thickness of the defined skin area, calculate an epidermal thickness ratio (ratio of the measured epidermal thickness to a predetermined reference epidermal thickness), and estimate the blood vessel density of the skin area from the calculated epidermal thickness ratio (see Figs. 16 to 18).

[0114] As a result, in this embodiment, the nature and characteristics of a skin region (whether the skin region is a normal area or a blemished area, etc.) can be determined simply by measuring the epidermal thickness of the skin region.

[0115] Moreover, the blood vessel density estimation system 100 according to this embodiment can determine the attributes of a blemish occurring in a skin region from the estimated blood vessel density (see FIGS. 16 to 20).

[0116] This makes it possible to predict whether a skin region that is unclear as a blemish region is a normal region or a blemish region. Therefore, the blood vessel density estimation method of this example makes it possible to determine in advance whether a skin region is suitable for treatment such as phototherapy.

[0117] Although an embodiment of the present invention has been described above, the present invention is not limited to a specific embodiment, and various modifications and changes are possible within the scope of the invention described in the claims.

[0118] This application claims priority to International Application No. PCT / JP2019 / 044031, filed November 8, 2019, the entire contents of which are incorporated herein by reference. [Explanation of symbols]

[0119] 1. Spot attribute determination system 10 Information input section 20 Image forming section 30 Blood vessel density measurement section 40 Spot attribute determination section 50 Information output section 60 Central Processing Unit (CPU) 70 Memory 100 Vascular Density Estimation System 110 Information input section 120 Image forming section 131 Skin thickness measurement section 132 Skin thickness ratio calculation section 133 Blood vessel density estimation section 140 Stains attribute determination section 150 Information output section 160 Central Processing Unit (CPU) 170 Memory SS Stains SA skin area SA1 skin area (normal area) SA2 Skin area (spot area) EM epidermis EM1 Epidermis (normal site) EM2 Epidermis (spot area) DM dermis DM1 dermis (normal area) DM2 Dermis (spot area) BV blood vessels BV1 blood vessels (normal site) BV2 Blood vessels (spot area) IM2 2D image IM3 3D image IM3e 3D image of the epidermis IM3d 3D image of the dermis BF Blood flow image ET1 Epidermal thickness (normal area) ET2 Epidermal thickness (spot area)

Claims

1. Defining an area of ​​skin containing a blemish; measuring the vascular density of said area; determining an attribute of the blemish from the blood vessel density; the attribute of the blemish is the effectiveness of laser treatment for the blemish, The method for determining attributes of the spots predicts that the area with a high blood vessel density will have a low effect from laser treatment, and that the area with a low blood vessel density will have a high effect from laser treatment, as attributes of the spots.

2. The method for determining attributes of spots according to claim 1 , wherein the blood vessel density is measured by image analysis of an image of the area.

3. The blemish attribute determination method according to claim 2 , wherein the image is a blood flow image of the area.

4. The blemish attribute determination method according to claim 3 , wherein the image is a three-dimensional image.

5. 5. The method for determining attributes of a blemish according to claim 3, wherein the image is obtained from a region having a depth from the surface of the region of 50 to 600 μm.

6. The blemish attribute determination method according to claim 1 , further comprising the step of: identifying the blemish based on brightness or color of the area.

7. an imaging unit for forming an image of an area of ​​skin containing a blemish; a blood vessel density measuring unit for measuring a blood vessel density of the region from the image; a pigmentation attribute determining unit for determining an attribute of the pigmentation based on the blood vessel density, the attribute of the blemish is the effectiveness of laser treatment for the blemish, The age spot attribute determination unit predicts, as an attribute of the age spot, that the area with a high blood vessel density will have a low effect from laser treatment, and that the area with a low blood vessel density will have a high effect from laser treatment.

8. The blemish attribute determination system according to claim 7 , wherein the image is a blood flow image of the area.

Images