Stain attribute determination method, stain attribute determination system, vessel density estimation method, and vessel density estimation system

Abstract

A method for determining the attributes of pigmentation spots involves delineating areas of skin containing pigmentation spots, measuring the blood vessel density of the areas, and determining the attributes of the pigmentation spots based on the blood vessel density.

Description

Technical Field

[0001] This invention relates to a method for determining the attributes of pigmentation spots, a system for determining the attributes of pigmentation spots, a method for estimating the density of blood vessels, and a system for estimating the density of blood vessels. Background Technology

[0002] Various techniques are traditionally known as methods for evaluating pigmentation. For example, there is a technique that evaluates the recurrence of pigmentation after phototherapy based on the presence or absence of melanosomes or blood flow at the pigmentation site after phototherapy (see, for example, Patent Document 1).

[0003] <Prior art documents>

[0004] <Patent Documents>

[0005] Patent Document 1: Japanese Patent Application Publication No. 2008-11994 Summary of the Invention

[0006] <Problem to be solved by this invention>

[0007] However, in traditional techniques, if treatments such as phototherapy are not performed on the pigmented areas, it is impossible to evaluate the pigmentation and it is difficult to predict in advance whether the treatment will be effective.

[0008] The purpose of this invention is to provide a method for determining the attributes of pigmentation spots, which can easily evaluate pigmentation spots.

[0009] <Methods for solving problems>

[0010] To address the aforementioned problems, one aspect of the present invention provides a method for determining the attributes of pigmentation spots, which involves delineating areas of skin containing pigmentation spots, measuring the vascular density of the areas, and determining the attributes of the pigmentation spots based on the vascular density.

[0011] <The Effects of the Invention>

[0012] According to one aspect of the present invention, a method for determining the properties of pigmentation spots can be provided, which can easily evaluate pigmentation spots. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating an example of an algorithm for determining the color spot attributes according to the present invention.

[0014] Figure 2 It is a diagram showing the composition of a skin area (the pigmented area).

[0015] Figure 3 This is a diagram illustrating the principle of visualizing skin areas.

[0016] Figure 4 It is a two-dimensional image showing the blood flow in a skin area.

[0017] Figure 5 This is a three-dimensional image showing the blood flow in a skin region.

[0018] Figure 6 The model formula used to calculate blood flow images of skin regions is shown.

[0019] Figure 7 This is a flowchart illustrating an example of an algorithm used to calculate the correlation coefficient between vascular density and pigmentation properties in a skin region.

[0020] Figure 8 This is a diagram showing the surface and internal structure of pigmented areas before treatment, indicating areas where treatment is most effective.

[0021] Figure 9 It is shown Figure 8 Image of the surface of the pigmented area 3 months after treatment.

[0022] Figure 10 This is a diagram showing the surface and internal structure of pigmented areas with low treatment effectiveness before treatment.

[0023] Figure 11 It is shown Figure 10 Image of the surface of the pigmented area 3 months after treatment.

[0024] Figure 12 This is a graph showing the correlation between blood vessel density and melanin value at the pigmented area.

[0025] Figure 13 This is a graph showing the correlation between blood vessel density and brightness ratio at the pigmented area.

[0026] Figure 14 This is a graph showing the relationship between blood vessel density at the pigmented area and the effectiveness of laser treatment.

[0027] Figure 15 This is a block diagram illustrating an embodiment of the color spot attribute determination system according to the present invention.

[0028] Figure 16 This is a flowchart illustrating an example of an algorithm for the blood vessel density estimation method according to the present invention.

[0029] Figure 17 It is a cross-section of a skin region (normal area) showing the baseline epidermal thickness.

[0030] Figure 18 It is a diagram showing a cross-section of a skin area including pigmented and normal areas.

[0031] Figure 19 This is a graph showing the correlation between the epidermal thickness ratio and blood vessel density in a skin region.

[0032] Figure 20 This is a graph showing the correlation between the ratio of epidermal thickness to melanin value in a skin region.

[0033] Figure 21 This is a block diagram illustrating an embodiment of the blood vessel density estimation system according to the present invention. Detailed Implementation

[0034] Embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be noted that sometimes the same symbols are used for common parts in the various figures and their descriptions are omitted.

[0035] <Methods for Determining the Attributes of Pigmentation>

[0036] Figure 1 This is a flowchart illustrating an example of an algorithm for determining the color spot attributes according to the present invention. Figure 2 It is a diagram showing the composition of a skin area (the pigmented area).

[0037] In the method for determining pigmentation attributes in this example, firstly, the skin area containing pigmentation SS (hereinafter, sometimes referred to as skin area or pigmentation site) SA is delineated (see [reference]). Figure 1 Step S1 Figure 2 Here, pigmentation SS refers to the state of melanin deposition on the skin (see [reference]). Figure 2 ) As specific examples of pigmentation SS, in addition to senile lentigines (solar lentigines), seborrheic keratosis, freckles, melasma, etc., it also includes post-inflammatory hyperpigmentation.

[0038] Additionally, the skin region (SA) refers to both the surface and interior areas of the skin. Specifically, the skin surface corresponds to the epidermis (EM), and the skin interior corresponds to the dermis (DM) (see [link to relevant documentation]). Figure 2 Delineation refers to treating the skin area (SA) containing pigmentation spots (SS) as a defined area (pigmentation site). It should be noted that there are no limitations on the method used to delineate the skin area (pigmentation site) SA. For example, the skin area (pigmentation site) SA can be delineated visually by an expert or skilled person, or it can be delineated mechanically using image analysis with equipment.

[0039] It should be noted that the method for determining the pigmentation attribute in this example is not limited in terms of how the pigmentation SS contained in the pigmentation region SA is identified. In this example, the pigmentation SS can be determined, for example, based on the brightness or color of the skin region SA containing the pigmentation SS. Here, the brightness of the skin region SA refers to the brightness of the surface of the pigmentation region SA. Furthermore, the color of the skin region SA refers to the color of the surface of the pigmentation region SA.

[0040] Next, the blood vessel volume (BV) of the skin area (pigmented area) SA was visualized (see...). Figure 1 Step S2 Figure 2 Specifically, an image of the defined skin region (pigmented area) SA is obtained. Here, the image of the skin region (pigmented area) SA refers to the image (or image data) obtained by photographing or imaging the defined skin region (pigmented area) SA.

[0041] In this example of the method for determining the attributes of pigmentation, the vascular density of the skin area (pigmentation site) SA is further measured (see...). Figure 1 Step S3 Figure 2 Here, vascular density refers to the degree of density of blood vessel BV (see [reference]). Figure 2 It should be noted that there are no limitations on the method for measuring vascular density. In this example, vascular density is measured through image analysis of an image of the skin region SA, as described above.

[0042] Furthermore, image analysis refers to extracting basic elements from an image and obtaining statistical data. The methods of image analysis are arbitrary; for example, it can include image analysis that visualizes skin regions (SA) by binarizing the image.

[0043] In this example of the method for determining pigmentation attributes, the object of the image used for image analysis to measure blood vessel density is not limited. In this example of the method for determining pigmentation attributes, 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 where blood flows in the skin region SA.

[0044] Figure 3 This is a diagram illustrating the principle of visualizing skin areas. In this example, through... Figure 3 The optical coherence tomography (OCT) device shown forms images such as blood flow images. Here, the optical coherence tomography device VD is a device that irradiates low-coherence near-infrared light from a light source LS onto a skin region SA, visualizing the skin region SA in a non-contact manner through interference with the reflected near-infrared light (see [link to documentation]). Figure 3 It should be noted that the wavelength of near-infrared radiation is arbitrary. In this embodiment, the wavelength of the irradiated near-infrared radiation is set to approximately 1300 nm.

[0045] It should be noted that images such as blood flow images are not limited to those generated by OCT. Images obtained by methods other than OCT include, for example, images obtained by laser speckle flowmeter, Doppler flowmeter, video microscope, etc.

[0046] As an image of the skin region SA, a two-dimensional planar image IM2 can be used (see...). Figure 4 In the planar image IM2, the black and white of the image reflects the intensity of reflected light from the tissue, and the presence of moving areas in the image represents the blood flow image BF.

[0047] Additionally, for the image of the skin region SA, a three-dimensional image IM3 can be used instead of the planar image (two-dimensional image) IM2 (see...). Figure 5 Here, the 3D image IM3 refers to a stereoscopic image represented by a three-dimensional orthogonal coordinate system (X-axis, Y-axis, Z-axis). It should be noted that... Figure 5 In the example shown, as three-dimensional images IM3, three-dimensional images IM3e with a depth of 200 μm from the surface of the skin region SA and IM3d with a depth of 400 μm from the surface of the skin region SA are displayed.

[0048] It should be noted that blood flow imaging (BF) can be obtained through... Figure 6 The predetermined model formula (1) is used for calculation. In the case that the blood flow image BF is a two-dimensional image, the vascular density refers to the ratio (%) of the area of ​​the blood vessel BV (blood flow image BF) to the area of ​​the defined skin region (pigmentation area) SA. In the case that the blood flow image BF is a three-dimensional image, the vascular density refers to the ratio (%) of the volume of the blood vessel BV (blood flow image BF) to the volume of the defined skin region (pigmentation area) SA.

[0049] In the spot attribute determination method of this example, there is no limitation on the location of the skin region SA used to obtain the image for image analysis. In the spot attribute determination method of this example, the image of the skin region SA is an image obtained from a depth of, for example, 50 μm or more and 600 μm or less from the surface of the skin region SA (three-dimensional image IM3e), and preferably an image obtained from a depth of 200 μm or more and 500 μm or less, more preferably 300 μm or more and 400 μm or less (three-dimensional image IM3d).

[0050] Here, the range of 50 μm or more and 600 μm from the surface of the skin region SA roughly corresponds to the range including the epidermis and dermis, the range of 200 μm or more and 500 μm roughly corresponds to the range including the dermis, and the range of 300 μm or more and 400 μm roughly corresponds to a part of the dermis.

[0051] In the pigmentation attribute determination method of this example, the attributes of the pigmentation SS are further determined based on the blood vessel density (see [link]). Figure 1 Step S4 Figure 2 Here, the attributes of a pigmented spot (SS) (hereinafter sometimes referred to as pigmented spot attributes) refer to the inherent properties and characteristics of the pigmented spot. Determining the attributes of a pigmented spot based on vascular density means distinguishing the attributes of a pigmented spot based on vascular density.

[0052] It should be noted that after determining the pigmentation attribute of the skin region (pigmentation area) SA, when determining the pigmentation attribute of another skin region (pigmentation area) SA, the process from defining the pigmentation area SA to determining the pigmentation attribute is repeated (see [link to documentation]). Figure 1 (Steps S1 to S4).

[0053] In this example of the method for determining the attributes of pigmentation spots, there are no limitations on the attributes of the pigmentation spots (SS) determined based on blood vessel density. In this example of the method for determining the attributes of pigmentation spots, the effectiveness of laser treatment for the pigmentation spots (SS) is determined as an attribute of the SS. Here, laser treatment is a type of phototherapy, which refers to treatment that selectively destroys melanin and other substances that cause pigmentation spots (SS) by irradiating them with a laser, thereby causing the pigmentation spots (SS) to disappear. The effectiveness of the treatment refers to the magnitude of the treatment effect.

[0054] Figure 7 This is a flowchart illustrating an example of an algorithm for calculating the correlation coefficient between vascular density and pigmentation properties in a skin region (pigmentation site). Here, firstly, the vascular density and melanin value (Mb) of the skin region (pigmentation site) SA before laser treatment are measured (see...). Figure 2 , Figure 7 Step S11 Figure 8 , Figure 10 ).

[0055] It should be noted that melanin value refers to the blackness of the skin area (pigmented area) SA. Melanin value is an example of an indicator of skin color and is inversely proportional to lightness. The method for measuring melanin value is arbitrary. In this example, a skin analyzer (manufactured by Gadelius Medical, ANTERA 3D) was used to measure the melanin value Mb.

[0056] Next, laser treatment was performed on the skin area (pigmented area) SA where the melanin value (Mb) was measured (see...). Figure 2 , Figure 7 Step S12). Specifically, laser is applied to the pigmented area SA to make the pigmented spots SS contained in the skin area SA disappear (or lighten).

[0057] For the skin area SA after laser treatment, the melanin value (Ma) was measured 3 months after the laser treatment (see...). Figure 2 , Figure 7 Step S13 Figure 9 , Figure 11 The measurement of melanin value Ma and the measurement of melanin value Mb in the skin area (pigmented area) SA before laser treatment (). Figure 7 Step S11) is performed in the same manner.

[0058] Next, the ratio of melanin value Ma after laser treatment to melanin value Mb before laser treatment in the skin area (pigmented area) SA is calculated as Ma / Mb (hereinafter referred to as the melanin value ratio M). * ) perform calculations (see Figure 2 , Figure 7 (Step S14). It should be noted that the displayed melanin value is higher than M. * The higher the value, the lower the effectiveness of laser treatment; while the melanin value is higher than M... * The lower the value, the higher the effect of laser treatment.

[0059] Then, the ratio of blood vessel density to melanin value in the skin area (pigmented area) SA was compared with that in the M area. * The correlation coefficient between them is calculated. Figure 7 Step S15). In this example, for 13 cases involving 11 subjects, the vascular density and melanin value of the skin area (pigmented area) SA were compared with M. * The data obtained from the measurements were plotted, and the correlation coefficient was 0.63 (see [reference]). Figure 12 ).

[0060] Therefore, it can be determined that the blood vessel density and melanin value of SA in the skin area (pigmented area) are higher than those of M. * There is a correlation between them. This correlation shows that if the blood vessel density of the skin area (pigmented area) is higher in SA, the melanin value is higher than that in M. * The melanin value is higher in SA than in M ​​if the blood vessel density in the skin area (pigmented area) is lower. * Lower. That is, it can be predicted that skin areas with higher vascular density (pigmented areas) will have lower efficacy with laser treatment, while skin areas with lower vascular density (pigmented areas) will have higher efficacy with laser treatment (see...). Figure 14).

[0061] In addition, the luminance L at the SA site of the skin (pigmented area) was calculated 12 weeks after laser treatment. * a and brightness L before laser treatment * The ratio of b to L * a / L * b (hereinafter referred to as luminance ratio L) * It should be noted that the method for measuring brightness is arbitrary. In this example, a skin analyzer (manufactured by Gadelius Medical, ANTERA 3D) was used to measure the brightness L. * a、L * b was measured. It should be noted that the displayed brightness is L... * The higher the value of r, the better the laser treatment effect, and the brighter the laser compared to L... * The lower the value of r, the less effective the laser treatment.

[0062] The vascular density of the skin region (pigmented area) SA was calculated and its brightness ratio L was obtained. * The correlation coefficient between r. Regarding the ratio of vascular density and luminance of SA to L in skin regions (pigmented areas). * r, plotting the data obtained from the above 11 subjects (13 examples), has a correlation coefficient of -0.629 (see [link to data]). Figure 13 ).

[0063] Therefore, it can be determined that the blood vessel density and brightness of SA in the skin area (pigmented area) are higher than those of L. * There is also a correlation between r and . This correlation shows that if the blood vessel density of SA in the skin area (pigmented area) is higher, the brightness is higher than that of L. * r is lower, and if the blood vessel density of SA in the skin area (pigmented area) is lower, the brightness is higher than L. * The r is relatively high. That is, it can be predicted that skin areas with high vascular density (pigmented areas) will have lower efficacy with laser treatment, while skin areas with low vascular density (pigmented areas) will have higher efficacy with laser treatment (see [reference]). Figure 14 ).

[0064] As described above, the inventors of this invention have discovered that skin areas containing pigmented spots (SS) (hereinafter referred to as pigmented areas) SA exhibit both high and low vascular density, and that the properties of the pigmented spots contained in the pigmented areas SA differ depending on the vascular density. Specifically, it has been observed that there are cases where laser treatment of the pigmented areas SA results in the disappearance of the pigmented spots while maintaining their original appearance, and cases where the pigmented spots recur, and there is a trend of differences in treatment effectiveness due to variations in vascular density. In other words, a correlation has been found between the properties of the pigmented spots and the vascular density of the pigmented areas SA.

[0065] The pigmentation attribute determination method in this example is based on this consideration. It determines the pigmentation attribute solely by measuring the vascular density of the defined skin region SA, thereby identifying the nature and characteristics of the pigmentation site SA. Therefore, in this example, pigmentation attributes such as the potential therapeutic effect on the pigmentation site SA can be predicted in advance. Thus, according to the pigmentation attribute determination method in this example, pigmentation SS can be easily evaluated before treatments such as phototherapy.

[0066] In the pigmentation attribute determination method of this example, by measuring the blood vessel density using image analysis of the skin region SA, objective information about the blood vessel density of the skin region SA can be obtained. Therefore, by determining the pigmentation attribute based on the blood vessel density measured through image analysis of the skin region SA, the pigmentation attribute can be determined with high accuracy.

[0067] In the pigmentation attribute determination method of this example, by measuring the vascular density using image analysis of the blood flow image (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 pigmentation attribute based on the vascular density measured through image analysis of the blood flow image (BF) of the skin region (SA), the pigmentation attribute can be determined with higher accuracy.

[0068] In the pigmentation attribute determination method of this example, the blood vessel density is measured by image analysis of the 3D image IM3 of the skin region SA, thereby obtaining further objective information about the blood vessel density of the skin region SA. Therefore, by determining the pigmentation attribute based on the blood vessel density obtained by measuring the 3D image IM3 of the skin region SA using image analysis, the pigmentation attribute can be determined with even higher accuracy.

[0069] In the pigmentation attribute determination method of this example, by measuring blood vessel density using image analysis of an image obtained from a region of skin area SA at a depth of 50 μm or more but less than 600 μm from the surface, comprehensive information about the blood vessel density of the skin area SA can be obtained. Therefore, by determining pigmentation attributes based on blood vessel density measured using image analysis of an image obtained from a location of skin area SA at a depth of this range from the surface, pigmentation attributes can be determined with even higher accuracy.

[0070] In the pigmentation attribute determination method of this example, the pigmentation SS is determined based on the brightness or color of the skin region SA containing the pigmentation SS, thereby enabling objective delineation of the skin region SA containing the pigmentation SS. Therefore, by determining the pigmentation attribute based on the blood vessel density of the skin region SA containing the pigmentation SS determined in this way, the pigmentation attribute can be determined with high accuracy.

[0071] In the pigmentation attribute determination method of this example, the effectiveness of laser treatment on pigmentation SS is determined by using the attributes of pigmentation SS. Therefore, by measuring the vascular density of skin area SA, it is possible to predict in advance whether laser treatment will be effective on pigmentation site SA. Thus, according to this embodiment, pigmentation SS can be more easily evaluated before performing treatments such as laser treatment on pigmentation site SA.

[0072] <Spot Attribute Determination System>

[0073] Figure 15 This is a block diagram illustrating an embodiment of the pigmentation attribute determination system according to the present invention. The pigmentation attribute determination system 1 according to this embodiment includes an information input unit 10, an image forming unit 20, a blood vessel density measuring unit 30, a pigmentation 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 a stain attribute determination system according to the present invention, and can perform the stain attribute determination method according to the present invention.

[0074] Information input unit 10 is an interface capable of inputting various information about the subject (such as identification number, gender, age, location of pigmentation, etc.) (see [link]). Figure 15 The information input unit 10 is connected to the central processing unit (CPU) 60 and the memory 70 in a communicative manner (see [link]). 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.

[0075] Image forming unit 20 forms an image of the skin region (spotted portion) SA containing the pigmented spot SS (images IM2, IM3, etc.) (see...) Figure 2 , Figure 4 , Figure 5 , Figure 15 Specifically, a portion of the aforementioned color spot attribute determination method is performed in the image forming unit 20. Figure 1 Step S2).

[0076] The image forming unit 20 is communicatively connected to the information input unit 10, the central processing unit (CPU) 60, and the memory 70 (see [link]). 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. It should be noted that the image forming unit 20 is an example of an image forming unit that constitutes part of the color spot attribute determination system according to the present invention.

[0077] The vascular density measurement unit 30 measures the vascular density of the skin region (pigmented area) SA based on the image IM of the skin region (pigmented area) SA (see [reference]). Figure 2 , Figure 4 , Figure 5 , Figure 15 Specifically, a portion of the above-described method for determining the properties of pigmentation is performed in the vascular density measuring unit 30. Figure 1 Step S3).

[0078] The vascular density measuring unit 30 is communicatively connected to the image forming unit 20, the central processing unit (CPU) 60, and the memory 70 (see [link]). Figure 15 The vascular density measuring unit 30 is controlled by the central processing unit (CPU) 60, and the vascular density information obtained by the vascular density measuring unit 30 can be stored in the memory 70. It should be noted that the vascular density measuring unit 30 is an example of a vascular density measuring unit that constitutes part of the pigmentation attribute determination system according to the present invention.

[0079] The pigmentation attribute determination unit 40 determines the attribute of the pigmentation SS based on the blood vessel density of the skin area (pigmentation site) SA (see...). Figure 2 , Figure 15 Specifically, a portion of the aforementioned stain attribute determination method is performed in the stain attribute determination unit 40. Figure 1 Step S4).

[0080] The pigmentation attribute determination unit 40 is communicatively connected to the vascular density measurement unit 30, the central processing unit (CPU) 60, and the memory 70 (see [link]). Figure 15The 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. It should be noted that the stain attribute determination unit 40 is an example of a stain attribute determination unit that constitutes part of the stain attribute determination system according to the present invention.

[0081] Information output unit 50 is an interface that can output the determination result information from the color spot attribute determination unit 40 to the outside of the color spot attribute determination system 1 (see [link]). Figure 15 The information output unit 50 is communicatively connected to the color spot attribute determination unit 40, the central processing unit (CPU) 60, and the memory 70 (see [link]). Figure 15 The information output unit 50 is controlled by the central processing unit (CPU) 60.

[0082] It should be noted that the information output unit 50 can directly output the determination result information from the pigmentation attribute determination unit 40, or it can read the determination result information stored in the memory 70 and output it. In addition to outputting the determination result from the pigmentation attribute determination unit 40, the information output unit 50 can also output various information about the subject, image data of the skin area (pigmentation site) SA, and information on blood vessel density.

[0083] Additionally, a display device (not shown) capable of wired or wireless communication can be connected to the information output unit 50. Specific examples of display devices include a monitor such as a personal computer when the information output unit 50 is connected to the display device via a wired connection. Furthermore, when the colorimetric attribute determination system 1 is connected to the display device wirelessly, it includes the display of a common mobile terminal such as a smartphone.

[0084] 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 pigmentation attribute determination unit 40, the information output unit 50, and the memory 70 (see [link]). 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 measuring unit 30, the pigmentation attribute determination unit 40, the information output unit 50, and the memory 70 (see [link to relevant documentation]). Figure 15 ).

[0085] The memory 70 stores various information (various information about the subject, image data of skin area (pigmentation site) SA, blood vessel density, correlation coefficient between blood vessel density and melanin value ratio, correlation coefficient between blood vessel density and brightness ratio, and other judgment results, etc.) (see [link to relevant documentation]). Figure 15As described above, the memory 70 is connected to the information input unit 10, the image forming unit 20, the blood vessel density measuring unit 30, the pigmentation attribute determination unit 40, the information output unit 50, and the central processing unit (CPU) 60 (see above). Figure 15 It should be noted that, in Figure 15 In the example shown, although the memory 70 is arranged independently of the central processing unit (CPU) 60, this embodiment is not limited to this configuration, and the memory 70 may also be arranged inside the central processing unit (CPU) 60.

[0086] It should be noted that, in the pigmentation attribute determination system 1, from the viewpoint of determining pigmentation attributes with high accuracy, the image IM used in image analysis is preferably the blood flow image BF of the skin region SA. In the pigmentation attribute determination system 1 of this embodiment, the pigmentation attribute is determined based on 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).

[0087] The pigmentation attribute determination system 1 of this embodiment is essentially a system that performs the pigmentation attribute determination method of this embodiment described above. That is, in this embodiment, it is possible to delineate the skin region SA containing pigmentation SS, measure the blood vessel density of the skin region SA, and determine the attribute of the pigmentation SS based on the blood vessel density (see [link to documentation]). Figure 1 , Figure 2 ).

[0088] Therefore, in this embodiment, since the nature and characteristics of the SA in the pigmented area can be identified simply by measuring the vascular density of the SA in the skin region, it is possible to predict in advance the pigmented characteristics, such as whether there will be a therapeutic effect on the SA in the pigmented area. Thus, according to this embodiment, the SS in pigmented areas can be easily evaluated before treatments such as phototherapy.

[0089] Furthermore, in this embodiment, by measuring the vascular density using image analysis of the blood flow image (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 pigmentation attribute based on the vascular density measured through image analysis of the blood flow image (BF) of the skin region (SA), the pigmentation attribute can be determined with higher accuracy.

[0090] <Methods for estimating vascular density>

[0091] Figure 16 This is a flowchart illustrating an example of an algorithm for the blood vessel density estimation method according to the present invention. Figure 17This is a cross-sectional view of a skin region (normal area) showing a baseline epidermal thickness. Figure 18 This is a cross-sectional view of a skin area including both pigmented and normal areas. It should be noted that... Figures 16-18 In China, sometimes it is aimed at... Figure 1 and Figure 2 Common parts are given the same or corresponding symbols and their descriptions are omitted.

[0092] In this example of blood vessel density estimation method, the skin region is first delineated ( Figure 16 Step S21). The skin area being measured can be the skin area containing pigmentation (the area containing pigmentation), or it can be a skin area where the location of pigmentation is unknown beforehand. For example, skin areas SA1 and SA2 (…) are the subjects of the measurement. Figure 17 , Figure 18 ).

[0093] Skin region SA1 is a normal area of ​​skin (not a pigmented area) and consists of epidermal EM1 and dermal DM1. The epidermal EM1 of skin region SA1 has a thickness (epidermal thickness) ET1. The epidermal thickness ET1 of the epidermal EM1 of skin region SA1 (normal area) corresponds to the baseline epidermal thickness described later. Furthermore, the blood vessels BV1 are densely distributed in the dermal DM1 of skin region SA1.

[0094] Skin region SA2 is the skin area of ​​the pigmented 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 (pigmented area) is thicker than the epidermal thickness ET1 (reference epidermal thickness) of skin region SA1 (normal area). Furthermore, in the dermis DM2 of skin region SA2, the blood vessels BV2 are denser. The density of blood vessels BV2 in skin region SA2 is higher than the density of blood vessels in the dermis DM1 of skin region SA1.

[0095] Next, the epidermal thicknesses ET1 and ET2 of the defined skin regions SA1 and SA2 were measured (see [reference]). Figure 16 Step S22 Figure 17 In this example of vessel density estimation method, the epidermal thicknesses ET1 and ET2 of skin regions SA1 and SA2 are measured through image analysis of each image of skin regions SA1 and SA2. It should be noted that in the image analysis of each image of skin regions SA1 and SA2, the methods for visualizing skin regions described above (using methods such as OCT) can be used (see...). Figure 3 ).

[0096] Furthermore, the images used for image analysis are the images of the epidermal EM1 and EM2 of skin regions SA1 and SA2. That is, the locations of skin regions SA1 and SA2 used to obtain the images for image analysis are the epidermal EM1 and EM2 of skin regions SA1 and SA2.

[0097] In the blood vessel density estimation method of this example, each image of skin regions SA1 and SA2 is an image (three-dimensional image IM3e) obtained from a depth (epidermal thickness EM1, EM2) of 50 μm or more and 600 μm or less from the surface of skin regions SA1 and SA2, and preferably an image (three-dimensional image IM3d) obtained from a depth of 50 μm or more and 300 μm or less, more preferably 50 μm or more and 200 μm or less (see [reference]). Figure 5 ).

[0098] That is, the range of 50 μm or more and 600 μm 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 or more and 400 μm roughly corresponds to the range including epidermal EM1 and EM2 and a part of dermal DM1 and DM2; and the range of 300 μm or more and 200 μm roughly corresponds to a part of dermal DM1 and DM2.

[0099] In this example of the vessel density estimation method, the ratio of the measured epidermal thickness to a predetermined baseline epidermal thickness is used as the epidermal thickness ratio for calculation (see [link]). Figure 16 Step S23 Figure 17 Here, the predetermined reference epidermal thickness refers to the epidermal thickness of a pre-defined normal area. Furthermore, when the predetermined reference epidermal thickness is set as ST and the measured epidermal thickness is set as MT, the epidermal thickness ratio is expressed as MT / ST.

[0100] When the epidermal thickness ratio (MT / ST) is greater than 1, it indicates that the epidermal thickness of the measured skin area is greater than that of the normal skin area. When the epidermal thickness ratio (MT / ST) is 1, it indicates that the epidermal thickness of the measured skin area is equal to that of the normal skin area.

[0101] In this example, for instance, the epidermal thickness EM1 of skin region SA1 (normal area) can be measured in advance, and the obtained epidermal thickness ET1 can be used as a predetermined baseline epidermal thickness. Additionally, the epidermal thickness ET2 of skin region SA2 (pigmented area) EM2 can be measured, and the obtained epidermal thickness ET2 can be used as the measured epidermal thickness.

[0102] In this example of vascular density estimation method, the vascular density of the skin region is further estimated based on the calculated epidermal thickness ratio MT / ST (see [link to relevant documentation]). Figure 16 Step S24 Figure 17 Here, vascular density refers to the degree of density of blood vessels within a skin area. Estimating vascular density based on the epidermal thickness ratio means estimating vascular density based on the epidermal thickness ratio.

[0103] It should be noted that after estimating the vascular density of skin regions SA1 and SA2, when estimating the vascular density of another skin region, the process from defining the skin region to estimating the vascular density is repeated (see [link to documentation]). Figure 16 (Steps S21 to S24).

[0104] Vascular density can be calculated, for example, as follows. First, by measuring the vascular density using the aforementioned pigmentation attribute determination method, the vascular density (reference vascular density) MD1 of skin region SA1 (normal area) with a predetermined reference epidermal thickness and the vascular density MD2 of skin region SA2, for which the epidermal thickness has been measured are determined. Then, the ratio of the vascular density MD2 of skin region SA2 to the predetermined reference vascular density MD1 (vascular density ratio MD2 / MD1) is calculated as the vascular density.

[0105] Figure 19 This is a graph showing the correlation between the epidermal thickness ratio (MT / ST) and vascular density in a skin region. The inventors discovered that: [The text abruptly shifts to a different topic] * There is a correlation between MD2 and MD1 and vascular density ratio. Figure 19 Here, the epidermal thickness ratio MT / ST(E) is used. * The N number for investigating the relationship between MD2 and MD1 vascular density ratios was 25. Figure 19 ).

[0106] In the vascular density estimation method of this example, the attributes (spot attributes) of pigmentation spots generated in the skin region can be determined based on the estimated vascular density. Here, determining the pigmentation attributes based on vascular density means distinguishing pigmentation attributes based on vascular density.

[0107] From a different perspective, it can be said that the vessel density estimation method in this example can be based on the calculated epidermal thickness ratio MT / ST(E). *The attributes of pigmentation spots are determined. Here, determining the attributes of pigmentation spots based on the epidermal thickness ratio means differentiating pigmentation spots based on the epidermal thickness ratio. In addition, it can be determined in advance that the skin area identified when calculating the epidermal thickness ratio is the pigmented part (the skin area containing pigmentation spots).

[0108] It should be noted that, in the blood vessel density estimation method of this example, there is no limitation on the method for identifying the pigmented spots contained within the pigmented area. In the blood vessel density estimation method of this example, the pigmented spots can be identified, for example, based on the brightness or color of the skin region SA2 containing the pigmented spots.

[0109] In the vascular density estimation method of this example, there are no limitations on the attributes of the pigmentation determined based on vascular density. For example, the effectiveness of laser treatment for pigmentation can be determined using the vascular density estimation method of this example, similar to the pigmentation attribute determination method described above.

[0110] Here, for skin area SA2, the same method as described above for determining pigmentation attributes can be used to measure the vascular density and melanin value Mb before laser treatment, and the melanin value Ma 3 months after laser treatment, and calculate the melanin value ratio Ma / Mb (M * (See also) Figure 7 Steps S11 to S14 Figure 8 , Figure 10 , Figure 18 It should be noted that skin region SA2 is equivalent to skin region (pigmented area) SA obtained by measuring the melanin value Mb using the aforementioned method for determining pigmentation attributes (see [reference]). Figure 2 ).

[0111] Then, the epidermal thickness ratio of skin region (pigmentation area) SA2 to E is calculated. * Compared to melanin value, M * Correlation coefficient between them Figure 7 Step S15). In this example, for 12 cases involving 11 subjects, the epidermal thickness ratio of skin area (pigmentation site) SA2 to E will be compared. * Compared to melanin value M * The data obtained from the measurements were plotted, and the correlation coefficient was 0.75 (see [link]). Figure 20 ).

[0112] Therefore, it can be determined that in the skin area (pigmented area), the epidermal thickness of SA2 is greater than that of E. * Compared to melanin value, M * There is a correlation between them. This correlation shows that if the epidermal thickness of SA2 in the skin area (pigmentation site) is greater than that of E... *If it is thicker, the melanin value is higher than M. * Higher, and if the epidermal thickness of the skin area is greater than E * Thinner, with a melanin value higher than M * Lower.

[0113] That is, there is a trend of higher blood vessel density in skin areas with thicker epidermis and lower blood vessel density in skin areas with thinner epidermis. Therefore, it can be predicted that laser treatment will be less effective in skin areas with thicker epidermis, while it will be more effective in skin areas with thinner epidermis. Figure 20 ).

[0114] The vascular density estimation method in this example is based on this consideration. It estimates the vascular density of a skin region solely by estimating the epidermal thickness ratio (the ratio of the measured epidermal thickness to a predetermined baseline epidermal thickness) at the defined skin region, thereby measuring the epidermal thickness of the skin region and thus identifying the nature and characteristics of the skin region (whether the skin region is a normal area or a pigmented area, etc.).

[0115] In this example of vascular density estimation method, by using the vascular density ratio (the ratio of the vascular density of a region to a predetermined baseline vascular density) as vascular density, the correlation between epidermal thickness and vascular density within the skin region can be derived. This allows for the acquisition of objective information regarding the vascular density of the skin region.

[0116] In this example of vascular density estimation method, the epidermal thickness is measured through image analysis of an image of the skin region, thereby obtaining more objective information about the vascular density of the skin region. Therefore, by calculating the epidermal thickness ratio based on the epidermal thickness measured through image analysis of the skin region, vascular density can be estimated with high accuracy.

[0117] In the vessel density estimation method of this example, image analysis of an image of the epidermis of the skin region allows for the acquisition of more objective information about the epidermal thickness of the skin region. Therefore, by calculating the epidermal thickness ratio based on the epidermal thickness determined through image analysis of the image of the skin region's epidermal thickness, vessel density can be estimated with even higher accuracy.

[0118] In the vascular density estimation method of this example, by using a three-dimensional image as the image of the skin region, further objective information about the epidermal thickness of the skin region can be obtained. Therefore, by calculating the epidermal thickness ratio based on the epidermal thickness determined through image analysis of the three-dimensional image of the skin region, vascular density can be estimated with even higher accuracy.

[0119] In the vascular density estimation method of this example, epidermal thickness is measured using image analysis of images obtained from a region of skin depth ranging from 50 μm to 600 μm from the surface of the skin area. This allows for the acquisition of comprehensive information about the epidermal thickness of the skin region. Therefore, by calculating the epidermal thickness ratio based on the epidermal thickness measured using image analysis of images of skin regions at depths within this range from the surface of the skin area, vascular density can be estimated with even higher accuracy.

[0120] In this example of vascular density estimation method, the attributes of pigmentation spots generated in a skin region are determined based on the estimated density. Therefore, by measuring the epidermal thickness of the skin region alone, it is possible to distinguish whether a skin region is normal or pigmented. This allows for the prediction of whether a skin region where it is unclear whether it is a pigmented area is normal or pigmented. Thus, according to this example of vascular density estimation method, it is possible to determine in advance whether a skin region is suitable for treatments such as phototherapy.

[0121] In this example of a vascular density estimation method, the skin area containing pigmentation is delineated, the epidermal thickness of the delineated area is measured, and the attributes of the pigmentation are determined based on the calculated epidermal thickness ratio, thereby identifying the nature and characteristics of the pigmentation site. This allows for the prediction of pigmentation attributes, such as whether treatment will be effective at the pigmentation site. Therefore, according to this example of a vascular density estimation method, pigmentation can be easily evaluated before treatments such as phototherapy.

[0122] In the vessel density estimation method of this example, pigmented areas are identified based on their brightness or color, thus enabling objective delineation of these areas. Therefore, by determining the attribute of the pigmented area based on the vessel density of the skin region where the pigmentation is determined in this way, the attribute of the pigmented area can be determined with high accuracy.

[0123] In the vascular density estimation method of this example, the effectiveness of laser treatment on pigmented spots is determined by using the pigmentation as an attribute. Therefore, by measuring the vascular density of the skin area alone, it is possible to predict in advance whether laser treatment will be effective on the pigmented area. Thus, according to this embodiment, pigmented spots can be more easily evaluated before undergoing treatments such as laser treatment on the pigmented area.

[0124] <Vessel Denseness Estimation System>

[0125] Figure 21This is a block diagram illustrating an embodiment of the vessel density estimation system according to the present invention. It should be noted that, in Figure 21 In China, sometimes it is aimed at... Figure 15 Common parts are given the same or corresponding symbols and their descriptions are omitted.

[0126] The vascular density estimation system 100 according to this embodiment includes an information input unit 110, an image forming unit 120, an epidermal thickness measuring unit 131, an epidermal thickness ratio calculation unit 132, a vascular density estimation unit 133, a pigmentation 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 a vascular density estimation system according to the present invention, and can perform the vascular density estimation method according to the present invention.

[0127] The information input unit 110 can input various information about the subject (such as identification number, gender, age, location of normal body parts, location of pigmentation sites, etc.). Figure 21 ).

[0128] Image forming unit 120 forms images (images IM2, IM3, etc.) of skin region SA1 (normal area) and skin region SA2 (pigmented area) (see...). Figure 2 , Figure 4 , Figure 5 Specifically, a portion of the aforementioned blood vessel density estimation method is performed in the image forming unit 120. Figure 16 Step S21), and determine each skin region (skin region SA1, SA2).

[0129] The image forming unit 120 is communicatively connected to the information input unit 110, the epidermal thickness measuring 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 170. It should be noted that the image forming unit 120 is an example of an image forming unit that constitutes part of the color spot attribute determination system according to the present invention.

[0130] The epidermal thickness measuring unit 131 measures the epidermal thickness ET1 and ET2 of each skin region (skin region SA1, SA2) based on the image IM3. Figures 16-18 Specifically, a portion of the above-described method for determining pigmentation attributes is performed in the epidermal thickness measuring unit 131. Figure 16 Step S22).

[0131] The epidermal thickness measuring 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 measuring unit 131 is controlled by the central processing unit (CPU) 160, and the epidermal thickness information obtained by the epidermal thickness measuring unit 131 can be stored in the memory 70. It should be noted that the epidermal thickness measuring unit 131 is an example of an epidermal thickness measuring unit that constitutes part of the vascular density estimation system according to the present invention.

[0132] 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 (a predetermined reference epidermal thickness) of skin region SA1 (epidermal thickness ratio ET2 / ET1). * )) Perform calculations ( Figures 16-18 Specifically, a portion of the aforementioned blood vessel density estimation method is performed in the epidermal thickness ratio calculation unit 132. Figure 16 Step S23).

[0133] The skin thickness ratio calculation unit 132 is communicatively connected to the skin thickness measurement unit 131, the blood vessel density estimation unit 133, the central processing unit (CPU) 160, and the memory 170. Figure 21 The skin thickness ratio calculation unit 132 is controlled by the central processing unit (CPU) 160, and the skin thickness ratio information obtained by the skin thickness ratio calculation unit 132 can be stored in the memory 170. The skin thickness ratio calculation unit 132 is an example of a skin thickness ratio calculation unit that constitutes part of the vascular density estimation system according to the present invention.

[0134] The vessel density estimation unit 133 estimates the calculated epidermal thickness ratio ET2 / ET1 based on the calculated ET2 / ET1 (E * Estimate the vascular density of SA2 in the skin region. Figures 16-18 Specifically, a portion of the aforementioned blood vessel density estimation method is performed in the blood vessel density estimation unit 133. Figure 16 Step S24).

[0135] The blood vessel density estimation unit 133 is communicatively connected to the epidermal thickness ratio calculation unit 132, the pigmentation attribute determination unit 140, the central processing unit (CPU) 160, and the memory 170. Figure 21The 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. The vascular density estimation unit 133 is an example of a vascular density estimation unit that constitutes part of the vascular density estimation system according to the present invention.

[0136] The pigmentation attribute determination unit 140 determines the attribute of pigmentation spots generated in skin region SA2 based on the estimated blood vessel density (see [reference]). Figures 17-19 Specifically, a portion of the aforementioned pigmentation attribute determination method (determination of pigmentation attributes generated in the skin area) is performed in the pigmentation attribute determination unit 140.

[0137] The pigmentation attribute determination unit 140 is communicatively connected to the blood vessel density estimation unit 133, the central processing unit (CPU) 160, and the memory 170. Figure 21 The stain attribute determination unit 140 is controlled by the central processing unit (CPU) 160, and the information of the determination result obtained by the stain attribute determination unit 140 can be stored in the memory 170. The stain attribute determination unit 140 is an example of a stain attribute determination unit that constitutes part of the blood vessel density estimation system according to the present invention.

[0138] In addition to outputting the determination results from the pigmentation attribute determination unit 40, the information output unit 150 can also output various information about the subject, image data of skin areas (normal areas, pigmentation areas), baseline epidermal thickness, and baseline vascular density.

[0139] 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 pigmentation 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 blood vessel density estimation unit 133, the pigmentation attribute determination unit 140, the information output unit 150, and the memory 70. Figure 21 ).

[0140] The memory 170 stores various information (various information about the subject, location of normal areas, location of pigmented areas, image data of skin regions (normal areas and pigmented areas), 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, etc.). Figure 21 ).

[0141] 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 blood vessel density estimation unit 133, the pigmentation attribute determination unit 140, the information output unit 150, and the central processing unit (CPU) 160. Figure 21 It should be noted that, in Figure 15 In the example shown, although the memory 170 is arranged independently of the central processing unit (CPU) 160, this embodiment is not limited to this configuration, and the memory 170 may also be arranged inside the central processing unit (CPU) 160.

[0142] 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, in the vascular density estimation system 100 according to this embodiment, a skin region can be delineated, the epidermal thickness of the delineated skin region can be measured, an epidermal thickness ratio (the ratio of the measured epidermal thickness to a predetermined reference epidermal thickness) can be calculated, and the vascular density of the skin region can be estimated based on the calculated epidermal thickness ratio (see [link to relevant documentation]). Figures 16-18 ).

[0143] Therefore, in this embodiment, the nature and characteristics of the skin area (whether the skin area is a normal area or a pigmented area, etc.) can be identified simply by measuring the epidermal thickness of the skin area.

[0144] Furthermore, in the blood vessel density estimation system 100 according to this embodiment, the attributes of pigmentation spots generated in the skin area can be determined based on the estimated blood vessel density (see [link to documentation]). Figures 16-20 ).

[0145] Therefore, it is possible to predict whether a skin area with unclear pigmentation is a normal area or a pigmented area. Thus, based on the vascular density estimation method in this example, it is possible to determine in advance whether a skin area is suitable for treatments such as phototherapy.

[0146] While the embodiments of the present invention have been described above, the present invention is not limited to the specific embodiments, and various modifications and alterations can be made within the scope of the invention as described in the claims.

[0147] This application is based on International Application PCT / JP2019 / 044031, filed on November 8, 2019, and the entire contents of that application are incorporated herein by reference.

[0148] Symbol Explanation

[0149] 1. Pigmentation attribute determination system;

[0150] 10. Information Input Department;

[0151] 20 Image forming unit;

[0152] 30. Vascular density measurement unit;

[0153] 40. Color Spot Attribute Determination Section;

[0154] 50. Information Output Department;

[0155] 60. Central Processing Unit (CPU);

[0156] 70. Memory;

[0157] 100-vessel density estimation system;

[0158] 110 Information Input Department;

[0159] 120 Image forming unit;

[0160] 131. Skin thickness measuring unit;

[0161] 132 Skin thickness ratio calculation section;

[0162] 133. Vascular density estimation unit;

[0163] 140. Color Spot Attribute Determination Department;

[0164] 150 Information Output Department;

[0165] 160 Central Processing Unit (CPU);

[0166] 170. Memory;

[0167] SS pigmentation;

[0168] SA skin area;

[0169] SA1 Skin area (normal area);

[0170] SA2 Skin area (pigmented area);

[0171] EM epidermis;

[0172] EM1 epidermis (normal area);

[0173] EM2 epidermis (pigmented areas);

[0174] DM genuine leather;

[0175] DM1 Dermal (normal area);

[0176] DM2 dermis (area with pigmentation);

[0177] BV blood vessels;

[0178] BV1 blood vessels (normal site);

[0179] BV2 blood vessels (area of ​​pigmentation);

[0180] IM2 Two-dimensional image;

[0181] IM3 3D image;

[0182] Three-dimensional image of the IM3e epidermis;

[0183] IM3D: 3D image of genuine leather;

[0184] BF blood flow images;

[0185] ET1 epidermal thickness (normal area);

[0186] ET2 epidermal thickness (pigmented areas).

Claims

1. A method for determining the attributes of pigmentation spots. Delineate the areas of skin containing pigmentation. The blood vessel density in the region was measured. The properties of the pigmentation are determined based on the blood vessel density. The properties of the pigmentation indicate the effectiveness of laser treatment for the pigmentation. As a property of the pigmentation, it is predicted that areas with higher blood vessel density will have a lower effect of laser treatment, while areas with lower blood vessel density will have a higher effect of laser treatment.

2. The method for determining the attributes of pigmentation spots according to claim 1, wherein, The blood vessel density is determined by image analysis of the image of the region.

3. The method for determining the attributes of pigmentation spots according to claim 2, wherein, The image is a blood flow image of the region.

4. The method for determining the attributes of pigmentation spots according to claim 3, wherein, The image is a three-dimensional image.

5. The method for determining the attributes of pigmentation spots according to claim 3 or 4, wherein, The image is obtained from a depth of 50 μm or more and 600 μm or less from the surface of the region.

6. The method for determining the attributes of pigmentation spots according to any one of claims 1 to 4, wherein, The color spot is determined based on the brightness or color of the area.

7. A system for determining the attributes of pigmentation spots, comprising: The image forming unit forms an image of the skin region containing pigmentation. A vascular density measurement unit measures the vascular density of the region based on the image; and The pigmentation attribute determination unit determines the attributes of the pigmentation based on the blood vessel density. The properties of the pigmentation indicate the effectiveness of laser treatment for the pigmentation. In the pigmentation attribute determination unit, based on the attribute of the pigmentation, it is predicted that the area with higher blood vessel density will have a lower effect of laser treatment, while the area with lower blood vessel density will have a higher effect of laser treatment.

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

9. A method for determining the attributes of pigmentation spots. Delineate the areas of skin containing pigmentation. The epidermal thickness of the defined area was measured. The ratio of the measured epidermal thickness to a predetermined reference epidermal thickness is used to calculate the epidermal thickness ratio. The properties of the pigmentation are determined based on the calculated epidermal thickness ratio. The properties of the pigmentation indicate the effectiveness of laser treatment for the pigmentation. As a property of the pigmentation, it is predicted that areas with thicker epidermis will have a lower effect of laser treatment, while areas with thinner epidermis will have a higher effect of laser treatment.

10. A method for estimating blood vessel density. Define the area of ​​the skin. The epidermal thickness of the defined area was measured. The ratio of the measured epidermal thickness to a predetermined reference epidermal thickness is used to calculate the epidermal thickness ratio. The blood vessel density in the region is estimated based on the calculated epidermal thickness ratio. The properties of the pigmentation spots generated in the region are determined based on the estimated blood vessel density. The properties of the pigmentation indicate the effectiveness of laser treatment for the pigmentation. As a property of the pigmentation, it is predicted that areas with higher vascular density will have lower efficacy with laser treatment, while areas with lower vascular density will have higher efficacy with laser treatment.

11. The method for estimating blood vessel density according to claim 10, wherein, The vascular density is the ratio of the vascular density of the region to a predetermined baseline vascular density.

12. The method for estimating blood vessel density according to claim 10 or 11, wherein, The thickness of the epidermis is determined by image analysis of the image of the region.

13. The method for estimating blood vessel density according to claim 12, wherein, The image is an image of the epidermis of the region.

14. The method for estimating blood vessel density according to claim 12, wherein, The image is a three-dimensional image.

15. The method for estimating blood vessel density according to claim 12, wherein, The image is obtained from a depth of 50 μm or more and 600 μm or less from the surface of the region.

16. The method for estimating blood vessel density according to claim 10 or 11, wherein, The color spot is determined based on the brightness or color of the area.

17. A blood vessel density estimation system, comprising: Image forming section, forming an image of the skin region; The epidermal thickness measuring unit measures the epidermal thickness of the region based on the image. The epidermal thickness ratio calculation unit calculates the epidermal thickness ratio by taking the ratio of the measured epidermal thickness to a predetermined reference epidermal thickness. The vessel density estimation unit estimates the vessel density of the region based on the calculated epidermal thickness ratio; and The pigmentation attribute determination unit determines the attributes of the pigmentation spots generated in the area based on the estimated blood vessel density. The properties of the pigmentation indicate the effectiveness of laser treatment for the pigmentation. As a property of the pigmentation, it is predicted that areas with higher vascular density will have lower efficacy with laser treatment, while areas with lower vascular density will have higher efficacy with laser treatment.

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