Method for calculating epidermal thickness ratio, method for estimating vascular density, and system for estimating vascular density

Abstract

To provide a blemish attribute determination method capable of facilitating evaluation of blemishes.SOLUTION: A blemish attribute determination method includes: defining a skin region including a blemish; measuring an epidermal thickness of the defined region; calculating a ratio of the measured epidermal thickness to a prescribed reference epidermal thickness, as an epidermal thickness ratio; and determining an attribute of the blemish from the calculated epidermal thickness ratio, where the attribute of the blemish is effectiveness of laser treatment for the blemish, and it is so predicted that as the attribute of the blemish, a region having a thick epidermal thickness ratio is less effective by the laser treatment and a region having a thin epidermal thickness ratio is more effective by the laser treatment.SELECTED DRAWING: None

Description

[Technical Field]

[0001] The present invention relates to a method for determining stain attributes, a method for estimating vascular density, and a system for estimating vascular density. [Background technology]

[0002] Various techniques have been known for evaluating blemishes. For example, there is a technique to evaluate the recurrence of blemishes after phototherapy by checking the presence or absence of melanosomes or blood flow in the blemished area after phototherapy (see, for example, Patent Document 1). [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2008-11994 [Overview of the project] [Problems that the invention aims to solve]

[0004] However, with conventional technology, it is not possible to evaluate blemishes until after treatment such as phototherapy has been performed on the affected area, making it difficult to predict in advance whether or not the treatment will be effective on the blemished area.

[0005] The object of the present invention is to provide a stain attribute determination method that allows for easy evaluation of stains. [Means for solving the problem]

[0006] To solve the above problems, one aspect of the present invention defines a region of skin containing blemishes, and measures the epidermal thickness of the defined region. In order to determine the attributes of the aforementioned stain The ratio of the measured epidermal thickness to a predetermined standard epidermal thickness is calculated as the epidermal thickness ratio. death,The attribute of the freckle is the effectiveness of laser treatment for the freckle. As the attribute of the freckle, the region with a thick epidermal thickness ratio has a low effect by laser treatment, and the region with a thin epidermal thickness ratio has a high effect by laser treatment. A method for calculating the skin thickness ratio, which is shown and used to determine the attributes of the stain, is provided. is provided.

Advantages of the Invention

[0007] According to one aspect of the present invention, it is possible to provide a freckle attribute determination method capable of easily evaluating freckles.

Brief Description of the Drawings

[0008] [Figure 1] It is a flowchart showing an example of the algorithm of the freckle attribute determination method according to the present invention. [Figure 2] It is a diagram showing the configuration of a skin region (freckle site). [Figure 3] It is a diagram showing the principle of visualizing the skin region. [Figure 4] It is a diagram showing a two-dimensional blood flow image of the skin region. [Figure 5] It is a diagram showing a three-dimensional blood flow image of the skin region. [Figure 6] It shows a model formula for calculating the blood flow image of the skin region. [Figure 7] It is a flowchart showing an example of the algorithm for calculating the correlation coefficient between the blood vessel density and the freckle attribute in the skin region. [Figure 8] It is a diagram showing the surface and internal structure before treatment of a freckle site with a high treatment effect. [Figure 9] It is a diagram showing the surface of the freckle site shown in FIG. 8 three months after treatment. [Figure 10] It is a diagram showing the surface and internal structure before treatment of a freckle site with a low treatment effect. [Figure 11] It is a diagram showing the surface of the freckle site shown in FIG. 10 three months after treatment. [Figure 12] It is a diagram showing the correlation between the blood vessel density and the melanin value ratio at the freckle site. [Figure 13]This figure shows the correlation between blood vessel density and brightness ratio in areas with blemishes. [Figure 14] This figure shows the relationship between vascular density in areas with blemishes and the effectiveness of laser treatment. [Figure 15] This is a block diagram showing an embodiment of the stain attribute determination system according to the present invention. [Figure 16] This is a flowchart showing an example of the algorithm for the vascular density estimation method according to the present invention. [Figure 17] This is a diagram showing a cross-section of a skin area (normal area) with a reference epidermal thickness. [Figure 18] This diagram shows a cross-section of a skin area including the discolored area and the normal area. [Figure 19] This figure shows the correlation between epidermal thickness ratio and vascular density in the skin region. [Figure 20] This figure shows the correlation between the epidermal thickness ratio and the melanin value ratio in the skin region. [Figure 21] This is a block diagram showing an embodiment of the vascular density estimation system according to the present invention. [Modes for carrying out the invention]

[0009] Embodiments of the present invention will be described in detail with reference to the drawings. Note that parts common to all figures may be denoted by the same reference numerals, and their descriptions may be omitted.

[0010] <Method for determining the type of stain> Figure 1 is a flowchart showing an example of the algorithm for determining the blemish attribute according to the present invention. Figure 2 is a diagram showing the composition of a skin area (blemish area).

[0011] In the spot attribute determination method of this example, first, the skin area SA (hereinafter sometimes referred to as the skin area or spotted area) containing the spot SS is defined (see Figure 1, Step S1, and Figure 2). Here, spot SS indicates a condition in which melanin pigment is deposited in the skin (see Figure 2). Specific examples of spot SS include, for example, senile lentigines (solar lentigines), seborrheic keratosis, ephelides (freckles), melasma, and post-inflammatory hyperpigmentation.

[0012] Furthermore, the skin region (skin area) SA refers to the surface and internal areas 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 Figure 2). Definition means treating the skin region SA containing the spot SS as a certain area (spot area). Note that the manner in which the skin region (spot area) SA is defined is not limited. For example, the skin region (spot area) SA may be defined visually by a specialist or expert, or it may be defined mechanically by image analysis using a device.

[0013] In this example of a stain attribute determination method, the method for identifying stains SS contained in stain area SA is not limited. In this example of a stain attribute determination method, for example, stains SS can be identified based on the brightness or color of the skin area SA containing the stains SS. Here, the brightness of the skin area SA refers to the lightness of the surface of stain area SA. The color of the skin area SA refers to the color of the surface of stain area SA.

[0014] Next, the blood vessels BV of the skin area (spotted area) SA are visualized (see Figure 1, Step S2, Figure 2). Specifically, an image of the defined skin area (spotted area) SA is acquired. Here, an image of the skin area (spotted area) SA refers to an image (or image data) obtained by photographing or imaging the defined skin area (spotted area) SA.

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

[0016] Image analysis, on the other hand, refers to the process of extracting basic elements from an image and obtaining statistical data. The method of image analysis is arbitrary; for example, it could involve visualizing a skin region by binarizing an image of a skin region (SA).

[0017] In the stain attribute determination method of this example, the target image used for image analysis to measure vascular density is not limited. In the stain attribute determination method of this example, the image used for image analysis is preferably a blood flow image of the skin region SA. Here, a blood flow image refers to an image of the area in the skin region SA where blood flows.

[0018] Figure 3 shows the principle of visualizing the skin region. In this example, images such as blood flow images are formed using an Optical Coherence Tomography (OCT) system VD shown in Figure 3. Here, the Optical Coherence Tomography system VD is a device that visualizes the skin region SA non-contact by irradiating the skin region SA with low-coherence near-infrared light from a light source LS and interfering with the reflected near-infrared light (see Figure 3). The wavelength of the near-infrared light is arbitrary. In this embodiment, the wavelength of the irradiated near-infrared light was set to approximately 1300 nm.

[0019] It should be noted that images such as blood flow images are not limited to those formed by OCT. Examples of images obtained by methods other than OCT include those obtained by laser speckle flowmeters, Doppler flowmeters, and video microscopes.

[0020] For images of the skin region (SA), a two-dimensional planar image (IM2) can be used (see Figure 4). In the planar image (IM2), the grayscale of the image reflects the intensity of reflected light from the tissue, and images of moving areas show the blood flow image (BF).

[0021] Furthermore, for images of the skin region SA, a three-dimensional image IM3 can be used instead of a planar image (2D image) IM2 (see Figure 5). Here, the three-dimensional image IM3 represents a stereoscopic image expressed in a three-dimensional Cartesian coordinate system (X axis, Y axis, Z axis). In the example shown in Figure 5, the three-dimensional image IM3 is shown as IM3e, which is a three-dimensional image of the skin region SA at a depth of 200 μm from the surface, and IM3d, which is a three-dimensional image of the skin region SA at a depth of 400 μm from the surface.

[0022] The blood flow image BF can be calculated using the predetermined model equation (1) shown in Figure 6. When the blood flow image BF is a two-dimensional image, the vascular density represents the ratio (%) of the area of ​​the blood vessels BV (blood flow image BF) to the area of ​​the defined skin region (spotted area) SA, and when the blood flow image BF is a three-dimensional image, it represents the ratio (%) of the volume of the blood vessels BV (blood flow image BF) to the volume of the defined skin region (spotted area) SA.

[0023] In the stain attribute determination method of this example, the location of the skin region SA from which the image used for image analysis is acquired is not limited. In the stain attribute determination method of this example, the image of the skin region SA is an image (3D image IM3e) obtained from a depth from the surface of the skin region SA in the range of, for example, 50 μm to 600 μm, preferably an image (3D image IM3d) obtained from the range of 200 μm to 500 μm, and more preferably an image (3D image IM3d) obtained from the range of 300 μm to 400 μm.

[0024] Here, the area of ​​skin region SA with a depth from the surface of 50 μm to 600 μm roughly corresponds to the area including the epidermis and dermis, the area of ​​200 μm to 500 μm roughly corresponds to the area including the dermis, and the area of ​​300 μm to 400 μm roughly corresponds to a part of the dermis.

[0025] In the spot attribute determination method of this example, the attributes of spot SS are further determined from the blood vessel density (see Figure 1, step S4, and Figure 2). Here, the attributes of spot SS (hereinafter sometimes referred to as spot attributes) refer to the unique properties and characteristics of spot SS. Determining spot attributes from blood vessel density means identifying spot attributes based on blood vessel density.

[0026] Furthermore, if, after determining the blemish attribute for a target skin area (blemish area) SA, it is necessary to determine the blemish attribute for another skin area (blemish area) SA, 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 spot attribute determination method of this example, the attributes of spot SS determined from vascular density are not limited. In the spot attribute determination method of this example, the effectiveness of laser treatment for spot SS is determined as an attribute of spot SS. Here, laser treatment is a type of phototherapy, and refers to a treatment that eliminates spot SS by selectively destroying melanin and other substances that cause spot SS by irradiating with a laser. The effectiveness of the treatment refers to the degree of the effect of the treatment.

[0028] Figure 7 is a flowchart illustrating an example of an algorithm for calculating the correlation coefficient between vascular density and the attributes of blemishes in a skin area (blemish area). Here, first, the vascular density and melanin value Mb of the skin area (blemish area) SA are measured before laser treatment (see Figures 2, 7, step S11, 8, and 10).

[0029] The melanin value indicates the degree of blackness in the skin area (spotted area) SA. The melanin value is one example of an indicator of skin color and is inversely proportional to brightness. The method of 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, laser treatment is applied to the skin area (spotted area) SA where the melanin value Mb was measured (see Figures 2 and 7, step S12). Specifically, the laser is irradiated onto the spotted area SA to eliminate (or lighten) the spots SS contained in the skin area SA.

[0031] For skin areas SA after laser treatment, the melanin value Ma is measured 3 months after laser treatment (see Figures 2, 7, step S13, 9, and 11). The measurement of the melanin value Ma is performed in the same manner as the measurement of the melanin value Mb of skin areas (spotted areas) SA before laser treatment (Figure 7, step S11).

[0032] Next, in the skin area (spotted area) SA, the ratio of melanin value Ma after laser treatment to melanin value Mb before laser treatment (Ma / Mb, hereinafter referred to as the melanin value ratio M) is determined. * The melanin value ratio M is calculated (see Figures 2, 7, and step S14). * The higher the value, the lower the effectiveness of laser treatment, and the melanin value ratio M * A lower value indicates a higher effectiveness of laser treatment.

[0033] Furthermore, the ratio of vascular density and melanin value M in the skin area (spotted area) SA * The correlation coefficient is calculated (Figure 7, step S15). In this example, for 13 cases involving 11 subjects, the correlation coefficient between vascular density and melanin value M in the skin area (spotted area) SA was calculated. * The measured data was plotted, and the correlation coefficient was 0.63 (see Figure 12).

[0034] This results in the relationship between the blood vessel density and melanin value ratio M in the skin area (spotted area) SA. * It can be seen that there is a correlation between this and the melanin value ratio M. This correlation is that when the blood vessel density in the skin area (spotted area) SA is high, the melanin value ratio M * When the melanin value ratio M is high and the vascular density of the skin area (spotted area) SA is low, *This indicates that it becomes lower. That is, it can be predicted that the skin area (spot area) SA with a high blood vessel density has a low effect by laser treatment, and the skin area (spot area) SA with a low blood vessel density has a high effect by laser treatment (see Fig. 14).

[0035] In addition, the brightness L * a at 12 weeks after laser treatment in the skin area (spot area) SA and the brightness L * b before laser treatment, the ratio L * a / L * b (hereinafter referred to as brightness ratio L * r) was calculated. The method for measuring the brightness is arbitrary. In this example, using a skin analyzer (manufactured by Gadelious Medical Co., Ltd., ANTERA 3D), the brightness L * a and L * b were measured. Note that the higher the value of the brightness ratio L * r, the higher the laser treatment effect, and the lower the value of the brightness ratio L * r, the lower the laser treatment effect is shown.

[0036] The correlation coefficient between the blood vessel density of the skin area (spot area) SA and the obtained brightness ratio L * r was calculated. The correlation coefficient between the blood vessel density of the skin area (spot area) SA and the brightness ratio L * r was plotted using the data measured for the 11 subjects (13 cases) described above, and the correlation coefficient was -0.629 (see Fig. 13).

[0037] As a result, it was found that there is also a correlation between the blood vessel density of the skin area (spot area) SA and the brightness ratio L * r. This correlation shows that when the blood vessel density of the skin area (spot area) SA is high, the brightness ratio L * r is low, and when the blood vessel density of the skin area (spot area) SA is low, the brightness ratio L * r is high. That is, it can be predicted that the skin area (spot area) SA with a high blood vessel density has a low effect by laser treatment, and the skin area (spot area) SA with a low blood vessel density has a high effect by laser treatment (see Fig. 14).

[0038] As described above, the inventors of the present invention have found that in the skin area SA containing blemishes SS (hereinafter referred to as the blemish area), the vascular density can be high or low, and that the attributes of the blemishes SS contained in the blemish area SA differ depending on the level of vascular density. Specifically, they found that after laser treatment of the blemish area SA, the blemishes SS may disappear and remain so, or they may recur, and that this difference in treatment effectiveness tends to be caused by the level of vascular density. In other words, they found a correlation between the attributes of the blemishes SS and the vascular density of the blemish area SA.

[0039] The method for determining the blemish attribute in this example was derived from the above considerations. By determining the blemish attribute from the vascular density of the defined skin area SA, the properties and characteristics of the blemish area SA can be determined simply by measuring the vascular density of the skin area SA. As a result, in this example, it is possible to predict in advance the blemish attribute, such as whether or not treatment will be effective for the blemish area SA. Therefore, according to the blemish attribute determination method in this example, it is possible to easily evaluate the blemish SS before performing treatments such as phototherapy.

[0040] In this example of a method for determining the properties of skin blemishes, objective information about the vascular density of the skin region can be obtained by measuring the vascular density through image analysis of the skin region SA. Therefore, by determining the properties of skin blemishes from the vascular density measured by image analysis of the skin region SA, it is possible to determine the properties of skin blemishes with high accuracy.

[0041] In this example of a method for determining the blemish attribute, vascular density can be measured by image analysis of blood flow images (BF) of the skin region SA, thereby obtaining more objective information about the vascular density of the skin region SA. Therefore, by determining the blemish attribute from the vascular density measured by image analysis of blood flow images (BF) of the skin region SA, the blemish attribute can be determined with higher accuracy.

[0042] In this example of a spot attribute determination method, by measuring the vascular density of the skin region SA through image analysis of the 3D image IM3 of the skin region SA, more objective information about the vascular density of the skin region SA can be obtained. Therefore, by determining the spot attribute based on the vascular density measured from the image analysis of the 3D image IM3 of the skin region SA, the spot attribute can be determined with even higher accuracy.

[0043] In this example of a spot attribute determination method, by measuring the vascular density of images obtained from a depth of 50 μm to 600 μm from the surface of the skin area SA, comprehensive information about the vascular density of the skin area SA can be obtained. Therefore, by determining the spot attribute based on the vascular density measured by image analysis of images obtained from areas of the skin area SA where the depth from the surface falls within this range, the spot attribute can be determined with even higher accuracy.

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

[0045] In the spot attribute determination method of this embodiment, by determining the effectiveness of laser treatment for spot SS as an attribute of spot SS, it is possible to predict in advance whether or not laser treatment will be effective for the spot SA simply by measuring the vascular density of the skin area SA. Therefore, according to this embodiment, it is possible to evaluate spot SS more easily before performing laser treatment on the spot SA.

[0046] <Stain Attribute Determination System>

[0047] Figure 15 is a block diagram showing an embodiment of the stain attribute determination system according to the present invention. The stain attribute determination system 1 according to this embodiment has an information input unit 10, an image forming unit 20, a blood vessel density measurement unit 30, a stain attribute determination unit 40, an information output unit 50, a central processing unit (CPU) 60, and a memory 70 (Figure 15). The stain attribute determination system 1 is an example of the stain attribute determination system according to the present invention and can perform the stain attribute determination method according to the present invention.

[0048] The information input unit 10 is an interface that allows input of various information about the subject (e.g., identification number, gender, age, location of blemishes, etc.) (see Figure 15). The information input unit 10 is communicatively connected to the central processing unit (CPU) 60 and the memory 70 (see Figure 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.

[0049] The image forming unit 20 forms images (images IM2, IM3, etc.) of the skin region (spot area) SA containing the spot SS (see Figures 2, 4, 5, and 15). Specifically, the image forming unit 20 performs a part of the spot attribute determination method described above (Figure 1, step S2).

[0050] The image forming unit 20 is communicated to the information input unit 10, the central processing unit (CPU) 60, and the memory 70 (see Figure 15). The image forming unit 20 is controlled by the central processing unit (CPU) 60, and the 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 part of the stain attribute determination system according to the present invention.

[0051] The vascular density measurement unit 30 measures the vascular density of the skin area (spotted area) SA from the image IM of the skin area (spotted area) SA (see Figures 2, 4, 5, and 15). Specifically, the vascular density measurement unit 30 performs a part of the spot attribute determination method described above (Figure 1, step S3).

[0052] The vascular density measurement unit 30 is communicatively connected to the image forming unit 20, the central processing unit (CPU) 60, and the memory 70 (see Figure 15). The vascular density measurement unit 30 is controlled by the central processing unit (CPU) 60, and the vascular density information obtained by the vascular density measurement unit 30 can be stored in the memory 70. The vascular density measurement unit 30 is an example of a vascular density measurement unit that constitutes part of the stain attribute determination system according to the present invention.

[0053] The spot attribute determination unit 40 determines the attribute of spot SS from the blood vessel density of the skin area (spotted area) SA (see Figures 2 and 15). Specifically, the spot attribute determination unit 40 executes a part of the spot attribute determination method described above (Figure 1, step S4).

[0054] The stain attribute determination unit 40 is communicatively connected to the blood vessel density measurement unit 30, the central processing unit (CPU) 60, and the memory 70 (see Figure 15). The stain attribute determination unit 40 is controlled by the central processing unit (CPU) 60, and the determination result information obtained by the stain attribute determination unit 40 can be stored in the memory 70. Note that the stain attribute determination unit 40 is an example of a stain attribute determination unit that constitutes a part of the stain attribute determination system according to the present invention.

[0055] The information output unit 50 is an interface that can output information of the stain attribute determination result from the stain attribute determination unit 40 to the outside of the stain attribute determination system 1 (see Figure 15). The information output unit 50 is communicated with the stain attribute determination unit 40, the central processing unit (CPU) 60, and the memory 70 (see Figure 15). The information output unit 50 is controlled by the central processing unit (CPU) 60.

[0056] The information output unit 50 may directly output the judgment result information from the spot attribute determination unit 40, or it may read the judgment result information stored in the memory 70 and output it. In addition, the information output unit 50 may output various subject information, image data of the skin area (spotted area) SA, and vascular density information in addition to the judgment result from the spot attribute determination unit 40.

[0057] Furthermore, 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, when the information output unit 50 and the display device are connected by wire, a display of a personal computer or the like. When the stain attribute determination system 1 and the display device are connected wirelessly, a display of a general-purpose mobile terminal such as a smartphone may be used.

[0058] 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 measurement unit 30, the spot attribute determination unit 40, the information output unit 50, and the memory 70 (see Figure 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 measurement unit 30, the spot attribute determination unit 40, the information output unit 50, and the memory 70 (see Figure 15).

[0059] Memory 70 stores various types of information (subject information, image data of skin area (spotted area) SA, vascular density, correlation coefficient between vascular density and melanin value ratio, correlation coefficient between vascular density and brightness ratio, and other judgment results) (see Figure 15). As described above, Memory 70 is connected to the information input unit 10, image forming unit 20, vascular density measurement unit 30, spot attribute determination unit 40, information output unit 50, and central processing unit (CPU) 60 (see Figure 15). In the example shown in Figure 15, Memory 70 is arranged independently of the central processing unit (CPU) 60, but this embodiment is not limited to this configuration, and Memory 70 may be placed inside the central processing unit (CPU) 60.

[0060] Furthermore, in the spot attribute determination system 1, from the viewpoint of determining spot attributes with high accuracy, it is preferable that the image IM used for image analysis is a blood flow image BF of the skin region SA. In the spot attribute determination system 1 of this embodiment, spot attributes are determined from the blood vessel density measured by image analysis of the blood flow image BF of the defined skin region SA (Figure 1, steps S1 to S4).

[0061] The stain attribute determination system 1 of this embodiment is essentially a system that performs the stain attribute determination method of this embodiment described above. That is, in this embodiment, the skin region SA containing the stain SS is defined, the vascular density of the skin region SA is measured, and the attribute of the stain SS can be determined from the vascular density (see Figures 1 and 2).

[0062] As a result, in this embodiment, by simply measuring the vascular density of the skin area SA, the properties and characteristics of the blemish area SA can be determined, making it possible to predict in advance the blemish attributes, such as whether or not treatment will be effective for the blemish area SA. Therefore, according to this embodiment, it is possible to easily evaluate the blemish SS before performing treatments such as phototherapy.

[0063] Furthermore, in this embodiment, by measuring vascular density through image analysis of blood flow images BF of the skin region SA, more objective information about the vascular density of the skin region SA can be obtained. Therefore, by determining the blemish attribute from the vascular density measured by image analysis of blood flow images BF of the skin region SA, the blemish attribute can be determined with higher accuracy.

[0064] <Vessel density estimation method> Figure 16 is a flowchart showing an example of the algorithm for the vascular density estimation method according to the present invention. Figure 17 is a diagram showing a cross-section of a skin area (normal area) with a reference epidermal thickness, and Figure 18 is a diagram showing a cross-section of a skin area including a spotted area and a normal area. In Figures 16 to 18, parts common to Figures 1 and 2 may be denoted by the same or corresponding reference numerals and their explanation may be omitted.

[0065] In the vascular density estimation method described in this example, the skin area is first defined (Figure 16, step S21). The skin area to be measured may be the skin area of ​​the blemish (the area of ​​skin containing the blemish), or it may be a skin area where it is previously unknown whether or not it is a blemish area. The skin areas to be measured are, for example, skin areas SA1 and SA2 (Figures 17 and 18).

[0066] Skin region SA1 is a normal skin region (not a blemish area) and is composed of epidermis EM1 and dermis DM1. The epidermis EM1 of skin region SA1 has a thickness (epidermal thickness) ET1. The epidermal thickness ET1 of epidermis EM1 in skin region SA1 (normal area) corresponds to the reference epidermal thickness described later. In addition, blood vessels BV1 are densely concentrated in the dermis DM1 of skin region SA1.

[0067] Skin region SA2 is the skin region of the blemish area and is composed of the epidermis EM2 and the dermis DM2. The epidermis EM1 of skin region SA1 has a thickness (epidermal thickness) ET2. The epidermal thickness ET2 of skin region SA2 (blemish area) is thicker than the epidermal thickness ET1 (reference epidermal thickness) of skin region SA1 (normal area). In addition, the dermis DM2 of skin region SA2 is densely packed with blood vessels BV2. The blood vessel density of BV2 in skin region SA2 is higher than that of the dermis DM1 in skin region SA1.

[0068] Next, the epidermal thickness ET1 and ET2 of the defined skin regions SA1 and SA2 are measured (see Figure 16, step S22, and Figure 17). In this example of the vascular density estimation method, the epidermal thickness ET1 and ET2 of skin regions SA1 and SA2 are measured by image analysis of the images of skin regions SA1 and SA2. As mentioned above, a method for visualizing the skin region (such as a method using OCT) can be used for the image analysis of the images of skin regions SA1 and SA2 (see Figure 3).

[0069] Furthermore, the images to be analyzed are those of the epidermal EM1 and EM2 regions of skin areas SA1 and SA2. In other words, the areas of skin areas SA1 and SA2 from which images used for image analysis are the epidermal EM1 and EM2 regions of skin areas SA1 and SA2.

[0070] In the blood vessel density estimation method of this example, the images of skin regions SA1 and SA2 are images (3D image IM3e) obtained from a depth from the surface of skin regions SA1 and SA2 (epidermal thickness EM1 and EM2) in the range of, for example, 50 μm to 600 μm, preferably images (3D image IM3d) obtained from the range of 50 μm to 300 μm, and more preferably images (3D image IM3d) in the range of 50 μm to 200 μm (see Figure 5).

[0071] Specifically, the range of 50 μm to 600 μm in depth from the surface of skin regions SA1 and SA2 roughly corresponds to the range including epidermal EM1 and EM2 and dermal DM1 and DM2; the range of 50 μm to 400 μm roughly corresponds to the range including epidermal EM1 and EM2 and a portion of dermal DM1 and DM2; and the range of 300 μm to 200 μm roughly corresponds to a portion of dermal DM1 and DM2.

[0072] In the vascular density estimation method described here, the ratio of the measured epidermal thickness to a predetermined reference epidermal thickness is calculated as the epidermal thickness ratio (see Figure 16, step S23, and Figure 17). Here, the predetermined reference epidermal thickness refers to the epidermal thickness of a predetermined normal skin area. The epidermal thickness ratio is expressed as MT / ST, where ST is the predetermined reference epidermal thickness and MT is the measured epidermal thickness.

[0073] A skin thickness ratio (MT / ST) greater than 1 indicates that the epidermal thickness of the measured skin area is thicker than that of a normal skin area. A skin thickness ratio (MT / ST) of 1 indicates that the epidermal thickness of the measured skin area is equivalent to that of a normal skin area.

[0074] In this example, for instance, the epidermal thickness of epidermis EM1 in skin area SA1 (normal area) can be measured in advance, and the resulting epidermal thickness ET1 can be used as the predetermined reference epidermal thickness. Alternatively, the epidermal thickness ET2 of epidermis EM2 in skin area SA2 (spotted area) can be measured, and the resulting epidermal thickness ET2 can be used as the measured epidermal thickness.

[0075] In the vascular density estimation method of this example, the vascular density of the skin region is further estimated from the calculated epidermal thickness ratio MT / ST (see Figure 16, step S24, and Figure 17). Here, vascular density indicates the degree to which blood vessels are densely packed within the skin region. Estimating vascular density from the epidermal thickness ratio means measuring and determining vascular density based on the epidermal thickness ratio.

[0076] Furthermore, if the vascular density is to be estimated for another skin region after the target skin regions SA1 and SA2 have been estimated, the process from skin region demarcation to vascular density estimation is repeated (Figure 16, steps S21 to S24).

[0077] Vascular density can be calculated, for example, as follows: First, the vascular density (reference vascular density) MD1 of a skin area SA1 (normal area) with a predetermined reference epidermal thickness and the vascular density MD2 of a skin area SA2 with a measured epidermal thickness are measured using the vascular density method adopted in the aforementioned spot attribute determination method. Then, the ratio of the vascular density MD2 of skin area SA2 to the predetermined reference vascular density MD1 (vascular density ratio MD2 / MD1) is calculated as the vascular density.

[0078] Figure 19 shows the correlation between the epidermal thickness ratio and vascular density in the skin region. The inventors proposed the epidermal thickness ratio MT / ST(E * We found a correlation between the epidermal thickness ratio MT / ST(E) and the vascular density ratio MD2 / MD1 (Figure 19). Here, we found a correlation between the epidermal thickness ratio MT / ST(E) * The relationship between ) and the vascular density ratio MD2 / MD1 was investigated with a sample size of 25 (Figure 19).

[0079] In this example of a method for estimating vascular density, the attributes of blemishes occurring in the skin area (blemish attributes) can be determined from the estimated vascular density. Here, determining blemish attributes from vascular density means distinguishing blemish attributes based on vascular density.

[0080] The method for estimating vascular density in this example can be viewed differently from the calculated epidermal thickness ratio MT / ST(E * It can be said that the attributes of a blemish can be determined from the epidermal thickness ratio. Here, determining the blemish attributes from the epidermal thickness ratio means distinguishing the blemish attributes based on the epidermal thickness ratio. Furthermore, the skin area determined when calculating the epidermal thickness ratio may be known in advance to be the blemish area (the area of ​​skin containing the blemish).

[0081] Furthermore, the method for identifying blemishes within a blemish area in the vascular density estimation method of this example is not limited. In the vascular density estimation method of this example, for example, blemishes can be identified based on the brightness or color of the skin region SA2 containing the blemish.

[0082] In the vascular density estimation method of this example, the attributes of blemishes determined from vascular density are not limited. For example, the effectiveness of laser treatment for blemishes can be determined using the vascular density estimation method of this example, similar to the blemish attribute determination method described above.

[0083] Here, for skin area SA2, similar to the spot attribute determination method described above, the vascular density and melanin value Mb before laser treatment and the melanin value Ma 3 months after laser treatment are measured, and the melanin value ratio Ma / Mb(M * ) is calculated (see Figure 7, steps S11-S14, Figure 8, Figure 10, and Figure 18). Note that skin area SA2 corresponds to the skin area (spotted area) SA in which the melanin value Mb was measured using the above-described spot attribute determination method (see Figure 2).

[0084] And, the epidermal thickness ratio E of the skin area (spotted area) SA2 * and melanin value ratio M * The correlation coefficient is calculated (Figure 7, step S15). In this example, for 12 cases involving 11 subjects, the epidermal thickness ratio E of the skin area (spotted area) SA2 was calculated. * and melanin value ratio M * The measured data was plotted, and the correlation coefficient was 0.75 (see Figure 20).

[0085] This results in the epidermal thickness ratio E of the skin area (spotted area) SA2. * and melanin value ratio M * It can be seen that there is a correlation between this and the epidermal thickness ratio E of the skin area (spotted area) SA2. * When the skin is thick, the melanin value ratio M * The ratio of epidermal thickness in the skin region is high. * If the melanin content is low, the melanin value ratio M * This indicates that it will be lower.

[0086] In other words, areas of skin with a thicker epidermal thickness ratio tend to have higher vascular density, while areas of skin with a thinner epidermal thickness ratio tend to have lower vascular density. As a result, it can be predicted that areas of skin with a thicker epidermal thickness ratio will respond less well to laser treatment, while areas of skin with a thinner epidermal thickness ratio will respond more well to laser treatment (Figure 20).

[0087] The method for estimating vascular density in this example was derived from such considerations. By estimating the vascular density of a skin area from the epidermal thickness ratio (the ratio of the measured epidermal thickness to a predetermined standard epidermal thickness) in a defined skin area, it is possible to determine the properties and characteristics of a skin area (such as whether the skin area is a normal area or an area with blemishes) simply by measuring the epidermal thickness of the skin area.

[0088] In this example's method for estimating vascular density, by adopting the vascular density ratio (the ratio of the vascular density of a region to a predetermined standard vascular density) as the vascular density, it is possible to derive a correlation between the epidermal thickness ratio and vascular density in the skin region. This allows for obtaining objective information about the vascular density of the skin region.

[0089] In this example of a method for estimating vascular density, more objective information about the vascular density of the skin region can be obtained by measuring the epidermal thickness through image analysis of images of the skin region. Therefore, by calculating the epidermal thickness ratio from the epidermal thickness measured by image analysis of images of the skin region, the vascular density can be estimated with high accuracy.

[0090] In this example of a method for estimating vascular density, more objective information about the epidermal thickness of the skin region can be obtained by performing image analysis on images of the epidermis in the skin region. Therefore, by calculating the epidermal thickness ratio from the epidermal thickness measured by image analysis of images of the epidermal thickness of the skin region, vascular density can be estimated with even higher accuracy.

[0091] In this example of a method for estimating vascular density, three-dimensional images are used as images of the skin region, allowing for more objective information about the epidermal thickness of the skin region. Therefore, by calculating the epidermal thickness ratio based on the epidermal thickness measured from image analysis of the three-dimensional images of the skin region, vascular density can be estimated with even higher accuracy.

[0092] In the vascular density estimation method described here, complete information about the epidermal thickness of a skin region can be obtained by measuring the epidermal thickness through image analysis of images obtained from a depth of 50 μm to 600 μm from the surface of the skin region. Therefore, by calculating the epidermal thickness ratio based on the epidermal thickness measured by image analysis of images obtained from skin regions within this depth range, vascular density can be estimated with even higher accuracy.

[0093] In the vascular density estimation method presented here, by determining the attributes of blemishes occurring in a skin area from the estimated vascular density, it is possible to determine whether a skin area is normal or blemished simply by measuring the epidermal thickness of the skin area. This makes it possible to predict whether a skin area that is unclear as to whether it is blemished is normal or blemished. Therefore, according to the vascular density estimation method presented here, it is possible to determine in advance whether a skin area is suitable for treatment such as phototherapy.

[0094] In this example of a vascular density estimation method, the skin area containing the blemish is defined, the epidermal thickness of the defined skin area is measured, and the attributes of the blemish are determined from the calculated epidermal thickness ratio, thereby allowing the nature and characteristics of the blemish area to be identified. This makes it possible to predict in advance the blemish attributes, such as whether or not treatment will be effective for the blemish area. Therefore, using this vascular density estimation method, it is possible to easily evaluate blemishes before performing treatments such as phototherapy.

[0095] In this example of a method for estimating vascular density, blemishes can be identified based on the brightness or color of the skin area containing the blemish, thereby objectively defining the skin area containing the blemish. Therefore, by determining the blemish attribute from the vascular density of the skin area in which the blemish has been identified in this way, the blemish attribute can be determined with high accuracy.

[0096] In this embodiment's method for estimating vascular density, by determining the effectiveness of laser treatment for blemishes as an attribute of the blemish, it is possible to predict in advance whether or not laser treatment will be effective on a blemish area simply by measuring the vascular density of the skin region. Therefore, according to this embodiment, it is possible to evaluate blemishes more easily before performing laser treatment on the blemish area.

[0097] <Vascular density estimation system> Figure 21 is a block diagram showing an embodiment of the vascular density estimation system according to the present invention. In Figure 21, parts common to Figure 15 may be denoted by the same or corresponding reference numerals and their descriptions may be omitted.

[0098] The vascular density estimation system 100 according to this embodiment includes an information input unit 110, an image forming unit 120, an epidermal thickness measurement unit 131, an epidermal thickness ratio calculation unit 132, a vascular density estimation unit 133, a stain attribute determination unit 140, an information output unit 150, a central processing unit (CPU) 160, and a memory 170 (Figure 21). The vascular density estimation system 100 is an example of the vascular density estimation system according to the present invention and can perform the vascular density estimation method according to the present invention.

[0099] The information input unit 110 can input various information about the subject (for example, identification number, gender, age, location of normal areas, location of blemishes, etc.) (Figure 21).

[0100] The image forming unit 120 forms images (images IM2, IM3, etc.) of skin region SA1 (normal area) and skin region SA2 (spotted area) (see Figures 2, 4, and 5). Specifically, the image forming unit 120 executes a part of the blood vessel density estimation method described above (Figure 16, step S21) to determine each skin region (skin regions SA1 and SA2).

[0101] The image forming unit 120 is communicatively connected to the information input unit 110, the skin thickness measurement unit 131, the central processing unit (CPU) 160, and the memory 170 (Figure 21). The image forming unit 120 is controlled by the central processing unit (CPU) 160, and the 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 stain attribute determination system according to the present invention.

[0102] The epidermal thickness measurement unit 131 measures the epidermal thickness ET1 and ET2 of each skin region (skin region SA1, SA2) from the image IM3 of each skin region (Figures 16 to 18). Specifically, the epidermal thickness measurement unit 131 performs a part of the aforementioned spot attribute determination method (Figure 16, step S22).

[0103] The epidermal thickness measurement unit 131 is communicatively connected to the image forming unit 120, the epidermal thickness ratio calculation unit 132, the central processing unit (CPU) 160, and the memory 170 (Figure 21). The epidermal thickness measurement unit 131 is controlled by the central processing unit (CPU) 160, and the epidermal thickness information obtained by the epidermal thickness measurement unit 131 can be stored in the memory 70. The epidermal thickness measurement unit 131 is an example of an epidermal thickness measurement unit that constitutes part of the vascular density estimation system according to the present invention.

[0104] The epidermal thickness ratio calculation unit 132 calculates the ratio of the measured epidermal thickness ET2 of skin region SA2 to the epidermal thickness ET1 (predetermined reference epidermal thickness) of skin region SA1 (epidermal thickness ratio ET2 / ET1(E * The )) is calculated (Figures 16-18). Specifically, the epidermal thickness ratio calculation unit 132 executes a part of the vascular density estimation method described above (Figure 16, step S23).

[0105] The epidermal thickness ratio calculation unit 132 is communicatively connected to the epidermal thickness measurement unit 131, the vascular density estimation unit 133, the central processing unit (CPU) 160, and the memory 170 (Figure 21). The epidermal thickness ratio calculation unit 132 is controlled by the central processing unit (CPU) 160, and the epidermal thickness ratio information obtained by the epidermal thickness ratio calculation unit 132 can be stored in the memory 170. Note that the epidermal thickness ratio calculation unit 132 is an example of an epidermal thickness ratio calculation unit that constitutes part of the vascular density estimation system according to the present invention.

[0106] The vascular density estimation unit 133 calculates the epidermal thickness ratio ET2 / ET1(E * The vascular density of the skin region SA2 is estimated from the above (Figures 16-18). Specifically, the vascular density estimation unit 133 executes a part of the vascular density estimation method described above (Figure 16, step S24).

[0107] 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 (Figure 21). The vascular density estimation unit 133 is controlled by the central processing unit (CPU) 160, and the vascular density information obtained by the vascular density estimation unit 133 can be stored in the memory 170. Note that the vascular density estimation unit 133 is an example of a vascular density estimation unit that constitutes a part of the vascular density estimation system according to the present invention.

[0108] The spot attribute determination unit 140 determines the attributes of the spots occurring in the skin region SA2 based on the estimated blood vessel density (see Figures 17 to 19). Specifically, the spot attribute determination unit 140 performs a part of the spot attribute determination method described above (determination of spot attributes occurring in the skin region).

[0109] The spot attribute determination unit 140 is communicated to the vascular density estimation unit 133, the central processing unit (CPU) 160, and the memory 170 (Figure 21). The spot attribute determination unit 140 is controlled by the central processing unit (CPU) 160, and the determination result information obtained by the spot attribute determination unit 140 can be stored in the memory 170. Note that the spot attribute determination unit 140 is an example of a spot attribute determination unit that constitutes a part of the vascular density estimation system according to the present invention.

[0110] The information output unit 150 can output information such as the determination result from the spot attribute determination unit 40, as well as various information about the subject, image data of skin areas (normal areas, spotted areas), reference epidermal thickness, and reference blood vessel density.

[0111] 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 vascular density estimation unit 133, the spot attribute determination unit 140, the information output unit 150, and the memory 170 (Figure 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 vascular density estimation unit 133, the spot attribute determination unit 140, the information output unit 150, and the memory 70 (Figure 21).

[0112] Memory 170 stores various types of information (subject information, location of normal areas, location 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 judgment results) (Figure 21).

[0113] As described above, the memory 170 is connected to the information input unit 110, the image forming unit 120, the epidermal thickness ratio calculation unit 132, the vascular density estimation unit 133, the spot attribute determination unit 140, the information output unit 150, and the central processing unit (CPU) 160 (Figure 21). In the example shown in Figure 15, the memory 170 is arranged independently of the central processing unit (CPU) 160, but this embodiment is not limited to this configuration, and the memory 170 may be arranged inside the central processing unit (CPU) 160.

[0114] The vascular density estimation system 100 of this embodiment is essentially a system that performs the vascular density estimation method of this embodiment described above. That is, the vascular density estimation system 100 of this embodiment defines a skin region, measures the epidermal thickness of the defined skin region, calculates the epidermal thickness ratio (the ratio of the measured epidermal thickness to a predetermined reference epidermal thickness), and estimates the vascular density of the skin region from the calculated epidermal thickness ratio (see Figures 16 to 18).

[0115] As a result, in this embodiment, the properties and characteristics of a skin area (such as whether the skin area is a normal area or an area with blemishes) can be determined simply by measuring the epidermal thickness of the skin area.

[0116] Furthermore, the blood vessel density estimation system 100 according to this embodiment can determine the attributes of blemishes occurring in the skin region from the estimated blood vessel density (see Figures 16 to 20).

[0117] This allows us to predict whether an area of ​​skin that is unclear as to be a blemish area is a normal area or a blemish area. Therefore, using the blood vessel density estimation method in this example, it is possible to determine in advance whether an area of ​​skin is suitable for treatment such as phototherapy.

[0118] Although embodiments of the present invention have been described above, the present invention is not limited to any particular embodiment, and various modifications and changes are possible within the scope of the invention as described in the claims.

[0119] This application claims priority under international application PCT / JP2019 / 044031, filed on 8 November 2019, which is incorporated herein by reference in its entirety. [Explanation of Symbols]

[0120] 1. Stain Attribute Determination System 10. Information Input Section 20 Image forming unit 30 Blood vessel density measurement section 40. Stain attribute determination unit 50 Information Output Unit 60 Central Processing Unit (CPU) 70 memory 100 Vascular density estimation system 110 Information Input Section 120 Image forming unit 131 Skin thickness measurement section 132 Skin thickness ratio calculation section 133 Blood vessel density estimation section 140 Stain attribute determination unit 150 Information Output Unit 160 Central Processing Unit (CPU) 170 memory SS stains SA skin area SA1 skin area (normal area) SA2 Skin area (area with blemishes) EM epidermis EM1 Epidermis (normal site) EM2 Epidermis (areas with blemishes) DM dermis DM1 dermis (normal area) DM2 Dermis (areas with blemishes) BV blood vessels BV1 blood vessels (normal site) BV2 Blood vessels (spotted areas) IM2 2D image IM3 3D image IM3e 3D image of the epidermis IM3d 3D image of the dermis BF blood flow images ET1 Epidermal thickness (normal area) ET2 Epidermal thickness (areas with blemishes)

Claims

1. Define the area of ​​skin containing the blemish, The skin thickness of the defined region is measured, In order to determine the attributes of the aforementioned blemish, the ratio of the measured epidermal thickness to a predetermined standard epidermal thickness is calculated as the epidermal thickness ratio. The attribute of the aforementioned blemish is the effectiveness of laser treatment for the aforementioned blemish, As for the attributes of the aforementioned blemishes, it was shown that areas with a thicker epidermal thickness ratio showed a lower response to laser treatment, while areas with a thinner epidermal thickness ratio showed a higher response to laser treatment. A method for calculating the epidermal thickness ratio for determining the attributes of the aforementioned stains.

2. Define the area of ​​skin containing blemishes, The skin thickness of the defined region is measured, In order to determine the attributes of the aforementioned blemish, the ratio of the measured epidermal thickness to a predetermined standard epidermal thickness is calculated as the epidermal thickness ratio. The density of blood vessels in the region is estimated from the calculated epidermal thickness ratio. The attribute of the aforementioned blemish is the effectiveness of laser treatment for the aforementioned blemish, As for the attributes of the aforementioned blemishes, it was shown that areas with a high density of blood vessels respond poorly to laser treatment, and areas with a low density of blood vessels respond well to laser treatment. A method for estimating vascular density.

3. The method for estimating vascular density according to claim 2, wherein the vascular density is the ratio of the vascular density of the region to a predetermined reference vascular density.

4. The method for estimating vascular density according to claim 2 or 3, wherein the epidermal thickness is measured by image analysis of the image of the region.

5. The method for estimating vascular density according to claim 4, wherein the aforementioned image is an image of the epidermis of the region.

6. The method for estimating vascular density according to claim 4 or 5, wherein the aforementioned image is a three-dimensional image.

7. The method for estimating vascular density according to any one of claims 4 to 6, wherein the image is obtained from a depth of 50 μm or more and 600 μm or less from the surface of the region.

8. A method for estimating vascular density according to any one of claims 2 to 7, wherein the stain is identified based on the brightness or color of the area.

9. An image forming unit that forms an image of the skin region, A skin thickness measuring unit that measures the skin thickness of the region from the aforementioned image, A skin thickness ratio calculation unit calculates the ratio of the measured skin thickness to a predetermined standard skin thickness as the skin thickness ratio, A vascular density estimation unit that estimates the vascular density of the region from the calculated epidermal thickness ratio, It includes a stain attribute determination unit that determines the attributes of stains occurring in the region based on the estimated blood vessel density, The attribute of the aforementioned blemish is the effectiveness of laser treatment for the aforementioned blemish, A vascular density estimation system predicts that areas with high vascular density will have a low effect from laser treatment, and areas with low vascular density will have a high effect from laser treatment, based on the attributes of the aforementioned blemishes.

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