JP2015226599A - Apparatus for measuring chromaticity of living body - Google Patents

Abstract

PROBLEM TO BE SOLVED: To readily obtain values (chromaticity values) of color components except for glossy components (reflecting components) contained in a captured image with a compact and low-cost configuration.SOLUTION: An organ imaging apparatus includes an imaging unit and a calculating unit. The imaging unit takes an image of an imaging target included in a living body. The calculating unit calculates chromaticity values except for reflecting components contained in the image on the basis of frequency distribution of data on the image taken by the imaging unit.

Description

  The present invention relates to a living body chromaticity measuring apparatus that takes a shooting target included in a living body and calculates a chromaticity value.

  In oriental medicine, a diagnostic method (diagnosis, tongue examination) for diagnosing a health condition or medical condition by observing the state of a human face or tongue is known. For example, in the tongue examination, the physical condition and health level are diagnosed based on the state of the tongue and moss, and the diagnosis items include the color of the tongue, the thickness of the tongue, and the crack on the surface of the tongue.

  In a healthy state, the tongue is light red, but if there is a problem with the respiratory or circulatory system, the tongue will turn blue, and if there is fever or dehydration, the tongue will turn red. Further, when the metabolism of water is poor, the so-called swelled state is formed, and the tongue swells and becomes thick. Conversely, when the blood flow is insufficient or the water is insufficient, the tongue becomes thin and thin. Furthermore, when the immunity decreases due to lack of nutrition, poor blood flow, stress, etc., the regenerative power of the tongue cells decreases and the surface of the tongue cracks.

  Currently, these diagnoses are carried out by specialist doctors, but because they rely on experience and intuition, there are individual differences and poor objectivity.

  In order to solve these problems, a system has been proposed in which a subject is photographed using a digital camera, and the features of the photographed image are digitized and recorded and diagnosed. For example, in Patent Document 1, a tongue is photographed with a camera to extract a region of interest such as a tongue apex, a tongue, a tongue base, a tongue base, and the tongue quality (tongue color) and tongue tongue (moss color) of the region of interest. This makes it easy to diagnose the health status of individuals.

  In Patent Document 2, the ratio between the red (R) image data and the green (G) image data in each pixel of the captured image of the tongue is calculated, and by comparing this ratio with a threshold value, the color of the tongue and I try to judge the color of the moss. Also, polar coordinates with the center of gravity of the tongue as the origin are set, the variance of the radius (distance between the center of gravity of the tongue and the point on the contour line) is calculated, and the outer shape of the tongue (circular) is calculated based on the value of this variance Degree). Specifically, when the variance value is small, it is judged that the tongue is enlarged (in plan view) and close to a circle, and when the variance value is large, the tongue is thinned (in plan view). Judged to be close to a triangle. Furthermore, the number of connections in the vertical and horizontal directions of pixels on the base of the tongue (parts other than the nipple) is counted, and the number of connections in one direction (for example, the vertical direction) is equal to or greater than a predetermined value and the connection in the other direction (for example, the horizontal direction) A portion where the number is equal to or less than a predetermined value is determined as a crack in the tongue.

  By the way, when the amount of water in the body becomes excessive, the surface of the tongue gets wet and the gloss becomes strong. If the surface of the tongue is glossy, the illumination light is reflected and a so-called whiteout occurs in the image of the tongue. For this reason, it becomes impossible to accurately detect the chromaticity value based on the data of the captured image.

  Therefore, in Patent Document 3, for example, a diffusion light source device, a gloss light source device, and a camera are arranged in any of a plurality of openings formed in the integrating sphere, and the diffusion light source device and the gloss light source device are turned on. Is controlled so that the surface of the tongue is photographed with a camera. By diffusing the light emitted from the light source for diffusion with an integrating sphere, the gloss component on the tongue surface is sufficiently removed, and only the color image that does not include the gloss on the tongue surface is obtained, and the color component on the tongue surface is obtained. I am trying to measure stably. Thus, the structure which removes the gloss component of the tongue surface using an integrating sphere and measures the color component of the tongue surface is also disclosed in Patent Document 4.

Japanese Patent No. 3854966 (see claim 1, paragraphs [0004], [0009], [0022] to [0024], etc.) JP 2005-137756 A (refer to claim 3, paragraphs [0059] to [0063], [0071] to [0074], [0079] to [0081], FIG. 4 to FIG. 6, FIG. 8 etc.) JP 2011-239926 A (refer to claim 1, paragraphs [0016], [0017], [0028], FIG. 1, etc.) JP 2010-259487 A (refer to claim 1, paragraphs [0019], [0031], FIG. 1, etc.)

  However, in Patent Documents 3 and 4, since a large and special instrument called an integrating sphere is required to remove the gloss component, there arises a problem that the size of the entire apparatus increases and the cost also increases.

  The present invention has been made to solve the above-described problems, and an object of the present invention is to reduce the color component value (chromaticity value) excluding the glossy component (reflection component) included in the photographed image in a small size and low cost. An object of the present invention is to provide a biological chromaticity measuring device that can be easily obtained with the above-described configuration.

  The biological chromaticity measuring apparatus according to one aspect of the present invention uses an imaging unit that captures an image of a subject to be captured in a living body and acquires an image, and a frequency distribution of the data of the image acquired by the imaging unit, An arithmetic unit that calculates a chromaticity value excluding the reflection component included in the image.

  The calculation unit calculates a chromaticity value excluding the reflection component of the image, using the frequency distribution of the image data acquired by the imaging unit. For this reason, when removing the reflection component from the image, it is not necessary to use a large and special integrating sphere as in the prior art, and a chromaticity value excluding the reflection component can be easily obtained with a small and low-cost configuration.

  The imaging unit acquires the image for each of the different colors, and the calculation unit calculates the chromaticity value from the frequency distribution of the color for the narrowest frequency distribution of the image, and While the frequency distribution is used as a reference frequency distribution, for other colors, the similar distribution of the reference frequency distribution is fitted to the frequency distribution of the other colors, so that the frequency distribution of the other colors is converted to the similarity distribution. The chromaticity value may be calculated from the distribution of the chromaticity region by dividing the reflection region located on the higher luminance side than the region where the shapes overlap and the remaining chromaticity region.

  For the color having the narrowest frequency distribution width (for example, R), it can be considered that the frequency distribution hardly includes reflection components. Therefore, the calculation unit can obtain a chromaticity value that does not include a reflection component by calculating a chromaticity value from the frequency distribution (reference frequency distribution). On the other hand, for other colors (for example, B and G), the frequency distribution is wider than R, for example, and it is considered that the frequency distribution includes a reflection component. A region including the reflection component (reflection region) and another region (chromaticity region) are separated. That is, the arithmetic unit fits the similarity shape of the reference frequency distribution to the frequency distribution of the other colors, thereby converting the frequency distribution of the other colors into the reflection region on the higher luminance side than the region where the similarity shapes overlap. Divide into the remaining chromaticity areas. Then, the calculation unit can obtain chromaticity values excluding the reflection component for other colors by calculating the chromaticity values from the distribution of the chromaticity regions.

  The calculation unit enlarges or reduces the shape of the reference frequency distribution in accordance with the mode ratio with the frequency distribution of the other color, and forms the shape of the reference frequency distribution after the enlargement or reduction, Fitting may be performed so that the center of the half-value width of the mode value coincides with the center of the half-value width of the mode value of the frequency distribution of the other color.

  When fitting the similar shape of the standard frequency distribution expanded or reduced according to the ratio of the mode to the frequency distribution of other colors, the centers of the half-value widths of the mode are matched to each other. High fitting can be performed.

  The calculation unit enlarges or reduces the shape of the reference frequency distribution in accordance with the mode ratio with the frequency distribution of the other color, and forms the shape of the reference frequency distribution after the enlargement or reduction, Fitting may be performed so that the center of the half-value width of the mode value is shifted to the lower luminance side or the higher luminance side than the center of the half-value width of the mode value of the frequency distribution of the other color.

  In this way, when fitting the similarity of the standard frequency distribution expanded or reduced according to the mode ratio to the frequency distribution of other colors, the centers of the half-value widths of the mode are shifted from each other. Thus, the fitting method can be changed according to the shape of the frequency distribution of other colors. Accordingly, it is possible to calculate the chromaticity value by separating the reflection area and the chromaticity area according to the shape of the frequency distribution of other colors.

  The reference frequency distribution is preferably a red frequency distribution. The frequency distribution of the R image obtained by photographing the imaging target included in the living body has a shape close to a normal distribution and contains almost no reflection component. Therefore, the R chromaticity value can be easily obtained from the R frequency distribution. The data variation in the frequency distribution of R is considered to be substantially the same as the data variation in the frequency distribution of other colors (for example, B and G), except for the reflection component. Therefore, by using the frequency distribution of R as a reference and fitting the similar shape to the frequency distribution of other colors (for example, B and G), the variation in the data of the frequency distribution of R can also be applied to the frequency distribution of other colors. Reflecting this, the frequency distribution of other colors can be well separated into the reflection area and the chromaticity area.

  The calculation unit may calculate, as the chromaticity value, image data having a mode value in a region excluding the reflection component of the frequency distribution. In addition, the calculation unit may calculate, as the chromaticity value, image data at the center of the half-value width of the mode value in a region excluding the reflection component of the frequency distribution. Furthermore, the calculation unit may calculate, as the chromaticity value, image data having an intermediate frequency value in a region excluding the reflection component of the frequency distribution.

  The chromaticity of the image data that takes the mode value, the image data at the center of the half-value width of the mode value, or the image data of the intermediate value of the frequency in the area excluding the reflection component of the frequency distribution By calculating as a value, it is possible to reliably obtain a chromaticity value excluding the reflection component included in the image.

  The living body chromaticity measuring apparatus may further include a feature amount extracting unit that extracts a feature amount used for diagnosis of the health level of the living body based on the chromaticity value calculated by the calculating unit.

  Since the feature quantity extraction unit extracts the feature quantity used for the health degree diagnosis based on the calculated chromaticity value, the health degree diagnosis based on the detected feature quantity becomes possible. The health degree diagnosis may be performed by the biological chromaticity measurement device (for example, the feature amount extraction unit) from which the feature amount is obtained, or the feature amount is transmitted to an external device to be externally transmitted. It may be done at.

  The feature amount may be at least one of a tongue color, a moss color, and a moss shape. In this case, the health level can be diagnosed based on the color of the tongue, the color of the moss, and the shape of the moss.

  According to the present invention, a chromaticity value excluding a reflection component can be easily obtained with a small and low-cost configuration without using a large and special integrating sphere as in the prior art.

It is a perspective view which shows the external appearance of the organ imaging device which concerns on one Embodiment of this invention. It is a block diagram which shows the schematic structure of the said organ image imaging device. It is explanatory drawing which shows the positional relationship of the illumination part with respect to imaging | photography object, and an imaging part. It is explanatory drawing which shows an example of the picked-up image of a tongue, an edge extraction filter, and the outline of the tongue extracted using the said filter. It is explanatory drawing which shows each area | region which extracts the chromaticity value set based on the said outline. It is explanatory drawing which shows the picked-up image of a tongue with few glossiness, and the frequency distribution of RGB in the center area | region of the picked-up image. It is explanatory drawing which shows the picked-up image of a tongue with much glossiness, and the RGB frequency distribution in the lower area | region of the picked-up image. It is explanatory drawing which shows the picked-up image of a tongue with much glossiness, and the RGB frequency distribution in the center area | region of the picked-up image. It is a graph which shows the spectral distribution of a tongue and moss. It is explanatory drawing which shows each area | region of frequency distribution of B and G typically. It is a graph which shows an example of the frequency distribution of R. It is explanatory drawing which shows an example which expands or reduces the shape of the said R frequency distribution, and applies it to the G frequency distribution. It is explanatory drawing which shows an example which expands or reduces the shape of the frequency distribution of said R, and applies it to the frequency distribution of B. It is explanatory drawing which shows an example of the method of calculating the chromaticity value of G from the chromaticity area | region except the reflection area | region of the frequency distribution of G. It is explanatory drawing which shows the other example of the method of calculating G chromaticity value from the chromaticity area | region except the reflective area | region of G frequency distribution. It is explanatory drawing which shows the further another example of the method of calculating G chromaticity value from the chromaticity area | region except the reflection area of G frequency distribution. It is explanatory drawing which shows the other example which expands or reduces the shape of the frequency distribution of said R, and applies it to the frequency distribution of G. It is explanatory drawing which shows the further another example which expands or reduces the shape of the said R frequency distribution, and applies it to the G frequency distribution. It is a flowchart which shows the flow of operation | movement in the said organ imaging device. It is explanatory drawing which shows typically the image which observed the inner wall of the large intestine with the endoscope.

  An embodiment of the present invention will be described below with reference to the drawings. In the present specification, when the numerical range is expressed as A to B, the numerical value range includes the values of the lower limit A and the upper limit B.

[Overall configuration of organ imaging system]
FIG. 1 is a perspective view showing an external appearance of an organ image photographing apparatus 1 of the present embodiment, and FIG. 2 is a block diagram showing a schematic configuration of the organ image photographing apparatus 1. The organ image photographing apparatus 1 photographs a photographing target included in a living body and extracts information necessary for diagnosis of the photographing target. In particular, in the present embodiment, the organ image photographing device 1 photographs the above-described photographing object, and acquires chromaticity for obtaining a characteristic amount (for example, tongue color, moss color, moss shape) necessary for diagnosis. A biological chromaticity measuring apparatus for calculating a value is configured. The subject to be photographed includes a living organ (for example, tongue), a body tissue (for example, an inner wall of a digestive organ such as the stomach or the large intestine), an eyelid, and the like. Hereinafter, as an example, a case where the subject to be imaged is a living body tongue is shown.

  The organ imaging apparatus 1 includes an illumination unit 2, an imaging unit 3, a display unit 4, an operation unit 5, a communication unit 6, and an audio output unit 7. The illumination unit 2 is provided in the housing 21, and the configuration other than the illumination unit 2 (for example, the imaging unit 3, the display unit 4, the operation unit 5, the communication unit 6, and the audio output unit 7) is provided in the housing 22. ing. Although the housing | casing 21 and the housing | casing 22 are connected so that relative rotation is possible, rotation is not necessarily required and one side may be completely fixed to the other. In addition, said illumination part 2 grade | etc., May be provided in the single housing | casing.

  The illuminating unit 2 illuminates a living organ (here, tongue) that is a subject to be imaged, and includes an illuminator that illuminates the subject to be photographed from above. As the light source of the illuminating unit 2, in order to improve color reproducibility, a light source that emits a daylight color such as a xenon lamp is used. The brightness of the light source varies depending on the sensitivity of the imaging unit 3 and the distance to the shooting target, but as an example, it can be considered that the shooting target has an illuminance of 1000 to 10,000 lx. The illumination unit 2 includes a lighting circuit and a dimming circuit in addition to the above light source, and lighting / extinguishing and dimming are controlled by a command from the illumination control unit 11.

  The imaging unit 3 captures an image of an organ (here, a tongue) of a subject whose health is to be determined under illumination by the illumination unit 2, and acquires an image. An imaging lens and an area sensor (imaging element) Have. The aperture (brightness of the lens), shutter speed, and focal length of the imaging lens are set so that the entire range to be photographed is in focus. As an example, F number: 16, shutter speed: 1/120 seconds, focal length: 20 mm.

  The area sensor is composed of an image sensor such as CCD (Charge Coupled Device) or CMOS (Complementary Metal Oxide Semiconductor), and the sensitivity and resolution are set so that the color and shape of the subject can be detected sufficiently. Has been. As an example, sensitivity: 60 db, resolution: 10 million pixels.

  Imaging by the imaging unit 3 is controlled by the imaging control unit 12. In addition to the imaging lens and area sensor, the imaging unit 3 includes a focus mechanism (not shown), a diaphragm mechanism, a drive circuit, an A / D conversion circuit, and the like. Focus, aperture control, A / D conversion, and the like are controlled. In the imaging unit 3, for example, data of 0 to 255 in 8 bits is acquired for each of red (R), green (G), and blue (B) as captured image data. That is, the imaging unit 3 acquires images obtained by shooting the shooting target for each of different colors (RGB), and thereby acquires image data of each color.

  FIG. 3 is an explanatory diagram showing a positional relationship between the illumination unit 2 and the imaging unit 3 with respect to a photographing target (tongue or face). As shown in the figure, the imaging unit 3 is arranged to face the subject to be photographed. The illumination unit 2 is arranged so as to illuminate the imaging target at an angle A of, for example, 0 ° to 45 ° with respect to the imaging optical axis X of the imaging unit 3 passing through the imaging target. The imaging optical axis X refers to the optical axis of the imaging lens that the imaging unit 3 has.

  When the angle A at the time of illumination is large, the measurement accuracy of the surface unevenness is improved, but the range in which the tongue can be photographed becomes small due to the shadow of the upper lip. On the contrary, if the angle A is small, the photographing range is enlarged, but the measurement accuracy is lowered, and the color jump due to the regular reflection of the illumination light is increased. Considering the above, a preferable range of the angle A at the time of illumination is 15 ° to 30 °.

  The display unit 4 includes a liquid crystal panel, a backlight, a lighting circuit, and a control circuit (not shown). The display unit 4 includes an image acquired by photographing with the imaging unit 3, an arithmetic unit 15 and a feature amount extraction unit 16 described later. The information calculated and output is displayed. The display unit 4 can also display information acquired from the outside via the communication unit 6 (for example, a result of diagnosis by transmitting information to an external medical institution). Display of various types of information on the display unit 4 is controlled by the display control unit 13.

  The operation unit 5 is an input unit for instructing imaging by the imaging unit 3, and includes an OK button (imaging execution button) 5a and a CANCEL button 5b. In the present embodiment, the display unit 4 and the operation unit 5 are configured by a common touch panel display device 41, and the display area of the display unit 4 and the display area of the operation unit 5 in the touch panel display device 41 are separated. The display of the operation unit 5 on the touch panel display device 41 is controlled by the operation control unit 14. The operation unit 5 may be configured by an input unit other than the touch panel display device 41 (the operation unit 5 may be provided at a position outside the display area of the touch panel display device 41).

  The communication unit 6 transmits the image data acquired by the imaging unit 3 and the information calculated by the calculation unit 15 and the feature amount extraction unit 16 to the outside via the communication line, or receives information from the outside. It is an interface for receiving. Transmission / reception of information in the communication unit 6 is controlled by the communication control unit 18.

  The audio output unit 7 outputs various kinds of information as audio, and is constituted by, for example, a speaker. The information output by voice includes information calculated by the calculation unit 15 and the feature amount extraction unit 16 and information (diagnosis result) transmitted to the organ imaging apparatus 1 after being diagnosed by an external device. . The sound output in the sound output unit 7 is controlled by the sound output control unit 19.

  In addition, the organ imaging apparatus 1 further includes an illumination control unit 11, an imaging control unit 12, a display control unit 13, an operation control unit 14, a calculation unit 15, a feature amount extraction unit 16, a storage unit 17, a communication control unit 18, An audio output control unit 19 and an overall control unit 20 for controlling these units are provided. As described above, the illumination control unit 11, the imaging control unit 12, the display control unit 13, the operation control unit 14, the communication control unit 18, and the audio output control unit 19 are the illumination unit 2, the imaging unit 3, the display unit 4, and the operation. The unit 5, the communication unit 6, and the audio output unit 7 are controlled. The overall control unit 20 is composed of, for example, a CPU (Central Processing Unit). The illumination control unit 11, the imaging control unit 12, the display control unit 13, the operation control unit 14, the communication control unit 18, the audio output control unit 19, and the overall control unit 20 are integrated (for example, with one CPU). ) May be configured.

  The calculation unit 15 performs a process of extracting an organ outline from the image acquired by the imaging unit 3. In the present embodiment, the calculation unit 15 extracts the contour line of the tongue as an organ based on the luminance edge of the photographed image (the part where the brightness is rapidly changed in the image).

  Luminance edge extraction can be performed using an edge extraction filter as shown in FIG. 4, for example. The edge extraction filter is a filter that weights pixels in the vicinity of the target pixel when performing first-order differentiation (when obtaining a difference in image data between adjacent pixels). Using such an edge extraction filter, for example, for the G image data of each pixel of the captured image, the difference between the image data is calculated between the target pixel and the neighboring pixel, and the pixel whose difference value exceeds a predetermined threshold is extracted. Thus, a pixel that becomes a luminance edge can be extracted. Since there is a luminance difference due to the shadow around the tongue, the contour line of the tongue can be extracted by extracting pixels that become luminance edges as described above. Here, although G image data having the greatest influence on the luminance is used for the calculation, R or B image data may be used.

  Further, the calculation unit 15 uses the frequency distribution of the image data acquired by the imaging unit 3 to remove chromaticity values (for example, RGB image data values (pixel values)) from which reflection components included in the captured image are removed. It also functions as a chromaticity value calculation unit for calculating. Details of the calculation of the chromaticity value by the calculation unit 15 will be described later.

  The storage unit 17 stores image data acquired by the imaging unit 3, data acquired by the calculation unit 15, data calculated by the feature amount extraction unit 16, information received from the outside, and the like. It is a memory which memorize | stores the program for operating a control part.

  The feature amount extraction unit 16 extracts a feature amount used for diagnosis of the health level of the living body based on the chromaticity value excluding the reflection component calculated by the calculation unit 15. The feature amount includes a tongue color, a moss color, a moss shape (thickness), and the like. These feature amounts can be extracted (detected) as follows, for example.

  FIG. 5 shows a photographed image of the tongue. In the following, in the photographed image of the tongue, when the region formed in the vertical direction at the center in the left-right direction of the tongue is divided into three regions arranged in the vertical direction, each region is divided into an upper region A1, a central region. These are referred to as A2 and lower region A3. These areas are defined by the size shown in FIG. 5 based on the width W in the horizontal direction and the length H in the vertical direction of the area surrounded by the contour line of the tongue detected by the calculation unit 15. The illustrated size is an example, and the present invention is not limited to this.

  Since the color of the tongue reflects the color of blood, the R component or the B component mainly increases or decreases in the RGB image data. Therefore, by obtaining the ratio of R (R / (R + G + B)) or the ratio of B (B / (R + G + B)) in the lower tongue region A3, the color of the tongue can be quantified and extracted. As the RGB data, an average value of R image data, an average value of G image data, and an average value of B image data among a plurality of pixels constituting the lower region A3 can be used.

  The reason why the image data of the lower tongue area A3 is used when extracting the color of the tongue is as follows. That is, in tongue examination used in Kampo medicine, the color of the tongue is generally diagnosed at the left and right ends of the tongue without moss or at the lower center of the tongue, but the left and right ends of the tongue are This is because the way in which the illumination light strikes changes due to the unevenness, so that shading tends to occur, and the image data tends to fluctuate from a value indicating the original tongue color.

  The moss color changes from white to brown depending on the amount of keratinized tissue, and therefore the G component or (G + R) component mainly increases or decreases in the RGB image data. Therefore, it is possible to quantify and extract the color of moss by determining the ratio of G (G / (R + G + B)) or the ratio of (G + R) ((G + R) / (R + G + B)) in the upper region A1 of the tongue. it can. As the RGB data, an average value of R image data, an average value of G image data, and an average value of B image data among a plurality of pixels constituting the upper region A1 can be used.

  When extracting the color of the moss, the image data of the upper region A1 of the tongue is used because the moss is a keratinized papillary tissue of the lingual mucosa and exists from the upper part of the tongue to the center. This is because there are many at the top.

  As the moss becomes thicker, the red color of the tongue changes to the white color of the moss, so that the R component or G component mainly increases or decreases in the RGB image data. For this reason, the thickness of moss can be quantified and extracted by obtaining the ratio of R (R / (R + G + B)) or the ratio of G (G / (R + G + B)) in the central region A2 of the tongue. As the RGB data, an average value of R image data, an average value of G image data, and an average value of B image data among a plurality of pixels constituting the central region A2 can be used.

  Note that the image data of the tongue central area A2 is used when extracting the thickness of the moss because the moss exists at the upper part of the tongue as described above, and the color of the central area A2 is the upper area A1. This is because the thickness of the moss can be determined by whether or not the color of the moss is close. By determining the ratio of the color difference (for example, chromaticity difference) between the upper region A1 and the central region A2 of the tongue and the color difference (for example, chromaticity difference) between the central region A2 and the lower region A3, Thickness can also be quantified.

[Calculation of chromaticity value]
Next, a method for calculating chromaticity values by the above-described calculation unit 15 will be described.

(If the tongue has less gloss)
FIG. 6 shows the photographed image of the tongue with less gloss and the RGB frequency distribution (the relationship between the pixel value (image data) and the frequency) in the center area A2 of the photographed image. In the photographed image shown in FIG. 6, there is no glossy whiteout area on the surface of the tongue except for the side of the tongue, and the shape of the RGB frequency distribution in the central area A2 is relatively wide for each color. The shape is close to a narrow normal distribution. Therefore, the calculation unit 15 determines whether the width of the frequency distribution is small or not by comparing the difference between the maximum value and the minimum value of the image data and the threshold value. Assuming that there is little, the average value of the image data is obtained for each of RGB, and this is used as the chromaticity value. Thereby, the feature quantity extraction unit 16 can obtain the predetermined ratio from the RGB chromaticity values (image data) as described above, and quantify and extract the feature quantity (for example, the thickness of the moss).

  6 shows the frequency distribution in the central area A2 of the tongue. However, if the photographed image does not have overexposure due to gloss, both the RGB in the upper area A1 and the lower area A3 are normal distributions as in FIG. A frequency distribution close to is obtained. Therefore, the calculation unit 15 obtains an average value of the image data for each of RGB from the RGB frequency distribution obtained in the upper region A1 and the lower region A3, and uses this as a chromaticity value, thereby obtaining a feature amount extraction unit. No. 16 can obtain a predetermined ratio from RGB chromaticity values and quantify and extract feature amounts (for example, tongue color, moss color).

(If the tongue is glossy)
FIG. 7 shows a photographed image of a glossy tongue and RGB frequency distribution in the lower area A3 of the photographed image. FIG. 8 shows a photographed image of a glossy tongue and RGB frequency distribution in the central area A2 of the photographed image. In the captured images of FIGS. 7 and 8, the entire tongue is moistened, so that the glossy whiteout occurs on the surface of the tongue. In particular, there are many overexposures in the portion of the tongue where the illumination light reflects. Hereinafter, an area where overexposure occurs in a photographed image of the tongue is referred to as an area L. A region where moss is generated is defined as a region M. Note that the region M is included in the central region A2 (see FIG. 8), but is not included in the lower region A3 (see FIG. 7).

  7 and 8, the frequency distribution of R has a shape close to a narrow normal distribution, but the frequency distribution of B and G is higher on the higher luminance side than the center of the normal distribution and further on the higher luminance side. It has an elongated shape. This is because B and G are more susceptible to regular reflection due to gloss on the tongue surface than R, and high brightness data (reflection component) is generated due to regular reflection. Therefore, in the frequency distribution of B and G, the original tongue color appears on the low luminance side, but the chromaticity values (for example, average values) of B and G are determined based on the data of the high-reflectance portion that is regularly reflected. It is measured to be larger than the value indicating the color. Note that the data of the high luminance part changes according to the degree of specular reflection, and the degree of specular reflection changes depending on the wetness of the tongue surface, the way the tongue is put out, and the angle. In this way, if the tongue surface is glossy, the original tongue color (chromaticity value) cannot be measured accurately, so that feature amount extraction and diagnosis based on RGB chromaticity values can be performed accurately. Can not be.

  FIG. 9 shows the spectral distribution of the tongue and moss. For both the tongue and moss, the reflectance of R (wavelength 600 to 700 nm) is high, and there is almost no difference in reflectance between the tongue and moss. On the other hand, the reflectance of B (wavelength 400 to 500 nm) and G (wavelength 500 to 600 nm) is low for both the tongue and moss, and there is a difference in reflectance between the tongue and moss. That is, the reflectance of B and G is higher for moss than for the tongue. For this reason, the moss is observed to be whiter than the tongue, and as shown in FIG. 10, in the frequency distribution of B and G, a distribution showing the moss color appears on the higher luminance side than the distribution showing the tongue color. In the frequency distribution of B and G, a higher luminance distribution (reflection component) due to the above-described regular reflection appears on the higher luminance side than the distribution indicating the moss color. It can be seen that the frequency distribution of B and G shown in FIG. 8 has a shape similar to the distribution shown in FIG. Further, the frequency distribution of B and G in FIG. 7 is the frequency distribution of the lower region A3, and the lower region A3 does not include the moss region M. Therefore, among the distributions shown in FIG. It has a shape similar to the part excluding the distribution showing.

  In view of the above points, in the present embodiment, the calculation unit 15 obtains RGB chromaticity values as follows.

  First, as shown in FIG. 11, the calculation unit 15 has the frequency distribution of R (the difference between the maximum value and the minimum value of the image data) having the narrowest width and the influence of regular reflection on the tongue surface ( The reference frequency distribution is used as a standard frequency distribution, and the maximum height (mode) H1 and the half-value width W1 of the frequency distribution of R are obtained and applied to the normal distribution to form the shape R1 of the frequency distribution of R (broken line) Request). The frequency distribution R1 is determined by the color distribution of the imaging target, the color distribution of the illumination light, and the sensitivity distribution of the imaging unit 3. However, since the imaging target, the illumination unit 2, and the imaging unit 3 are common, each of the RGB colors is used. The degree of variation in the image data may be considered to be the same. That is, the variation in the B and G image data can be considered to be the same as the variation in the R image data.

  Therefore, as shown in FIG. 12 and FIG. 13, the calculation unit 15 enlarges or reduces the shape R1 of the R frequency distribution to create a similar shape of the shape R1, and converts the similar shape into the frequency distribution of B and G. Each fitting is performed to obtain B and G chromaticity values. Hereinafter, as an example, a method for obtaining the chromaticity value of G will be specifically described, but the same applies to the case of obtaining the chromaticity value of B.

  The calculation unit 15 obtains the maximum height (mode) H2 of the G frequency distribution shown in FIG. 12, and obtains the ratio (H2 / H1) to the mode H1 of the R frequency distribution. Then, the calculation unit 15 multiplies the above-mentioned ratio by the half-value width W1 of the R frequency distribution to obtain the half-value width W2 of the mode H2 of the G frequency distribution, and the calculated half-value width W2 Center (intermediate value between left end and right end of half width W2) G0 and center of half width W1 of enlarged or reduced frequency distribution shape R1 (intermediate value between left end and right end of half width W1) The shape R1 is applied to the frequency distribution of G so that R0 matches. Then, the calculation unit 15 cuts the frequency distribution of G into a reflection area on the higher luminance side than the distribution of the fitted shape R1 and a chromaticity area other than the reflection area. That is, this chromaticity region corresponds to a region obtained by removing the reflection region from the G frequency distribution. When a distribution indicating moss does not originally exist in the frequency distribution of G, this chromaticity region is a distribution indicating only the tongue color.

  The computing unit 15 calculates a G chromaticity value from the chromaticity region that has been cut as described above. At this time, the calculation unit 15 may calculate a pixel value taking the mode value of the chromaticity region as shown in FIG. 14 as the G chromaticity value, or as shown in FIG. The pixel value at the center of the half-value width of the mode of the region may be calculated as the G chromaticity value, or as shown in FIG. 16, the intermediate value of the frequency in the chromaticity region (for example, the chromaticity region If there are 100 frequencies (pixels), the 50th frequency pixel value from the low luminance side) may be used as the G chromaticity value.

  On the other hand, for R having the narrowest frequency distribution width, in the entire region of the frequency distribution of R, the pixel value taking the mode value, the pixel value at the center of the half value width of the mode value, and the pixel value in the middle of the frequency Any of these can be used as the R chromaticity value.

  In the above, when fitting the shape R1 of the R frequency distribution to the G frequency distribution, the center G0 of the half width W2 of the G frequency distribution and the half width W1 of the expanded or reduced R frequency distribution Although the fitting is performed so that the center R0 of the G coincides with the distribution of G, if the distribution of the tongue color and the distribution of the moss color exist in the G frequency distribution, the center of the half-value width W2 of the G frequency distribution The fitting may be performed so that G0 and the center R0 of the half-value width W1 of the expanded or reduced frequency distribution of R are shifted.

  For example, as shown in FIG. 17, the fitting may be performed so that the center R0 of the half width W1 is shifted to the lower luminance side than the center G0 of the half width W2. In this case, it is possible to calculate a chromaticity value that emphasizes the tongue color rather than the moss color, and the detection accuracy of the tongue color is improved. That is, in this case, the contribution of the tongue color increases, and the chromaticity value moves to the low luminance side. On the contrary, as shown in FIG. 18, when fitting is performed such that the center R0 of the half width W1 is shifted to the higher luminance side than the center G0 of the half width W2, the emphasis is on the moss color rather than the tongue color. The chromaticity value can be calculated, and the moss color detection accuracy is improved. In other words, in this case, the contribution of the moss color increases, and the chromaticity value moves to the high luminance side.

[Control flow]
FIG. 19 is a flowchart showing an operation flow in the organ image photographing apparatus 1 of the present embodiment. When the organ image photographing apparatus 1 receives a photographing instruction from the operation unit 5 or an unillustrated input unit, the illumination control unit 11 turns on the illumination unit 2 (S1) and sets photographing conditions such as illuminance (S1). S2). When the setting of the shooting conditions is completed, the imaging control unit 12 controls the imaging unit 3 to shoot the tongue that is the shooting target (S3). Thereby, a photographed image of the tongue is obtained.

  When the photographing is finished, the calculation unit 15 extracts the contour line of the tongue from the photographed image of the tongue (S4), detects the upper and lower ends and the left and right ends of the tongue from the extracted contour line, and the feature amount (tongue color, Regions (upper region A1, central region A2, lower region A3) for detecting moss color and moss thickness are set (S5).

  Next, the computing unit 15 creates an RGB frequency distribution for at least one of the regions set in S5 (S6), removes the reflection component from the frequency distribution by the above-described method, and then performs the remaining regions (colors). RGB chromaticity values are calculated from the distribution of the degree region) (S7). After that, the feature amount extraction unit 16 calculates a predetermined ratio using the RGB chromaticity values calculated by the calculation unit 15 and extracts feature amounts (tongue color, moss color, moss thickness). In step S9, the feature amount is digitized in, for example, three levels. A value obtained by quantifying the feature value is output from the feature value extraction unit 16 and displayed on the display unit 4 and is also stored in the storage unit 17, but is output from the voice output unit 7 as a voice as necessary. Or recorded by an output device (not shown) or transferred to the outside via the communication unit 6 (S10). Further, the organ image capturing apparatus 1 may determine the health level of the subject based on the value obtained by quantifying the feature amount, and the organ image capturing apparatus 1 displays the result determined by the external device. You may do it.

  As described above, in the present embodiment, the calculation unit 15 uses the frequency distribution of the image data acquired by the imaging unit 3 to calculate the chromaticity value excluding the reflection component included in the image. When removing the reflection component from the lens, it is not necessary to use a large and special integrating sphere as in the prior art, and it is possible to easily obtain a chromaticity value excluding the reflection component while having a small and low-cost configuration.

  The computing unit 15 calculates a chromaticity value from the frequency distribution of R (reference frequency distribution) for R having the narrowest frequency distribution width of the captured image. Since R is hardly affected by regular reflection, the chromaticity value excluding the reflection component can be obtained by calculating the chromaticity value from the frequency distribution of R. On the other hand, the calculation unit 15 fits the similar shape of the frequency distribution of R to the frequency distribution of B or G, thereby dividing the frequency distribution of B or G into the reflection region and the chromaticity region, and the distribution of the chromaticity region. To calculate B and G chromaticity values. For G and B, since the chromaticity value is calculated from the area (chromaticity area) excluding the reflection component of the frequency distribution, the B and G chromaticity values can be obtained accurately.

  Further, the calculation unit 15 expands or contracts the shape of the R frequency distribution serving as a reference in accordance with the mode ratio with the frequency distribution of B or G, and shapes the R frequency distribution after expansion or reduction. Are fitted so that the center of the half-value width of the mode value coincides with the center of the half-value width of the mode value of the frequency distribution of B or G, so that fitting with high accuracy can be performed. Also, the dispersion of image data in the frequency distribution of R is appropriately reflected in the frequency distribution of B and G, and the frequency distribution of B and G is favorably applied to the reflection region including the reflection component and the remaining chromaticity region. Can be carved.

  On the other hand, as described above, the arithmetic unit 15 performs fitting so that the center of the half-value width of the R frequency distribution is shifted to the higher luminance side or the lower luminance side than the center of the half value width of the B or G frequency distribution. May be performed. In this case, the fitting method can be changed according to the shape of the frequency distribution of B or G. Accordingly, it is possible to calculate the chromaticity value by separating the reflection region and the chromaticity region according to the shape of the frequency distribution of B or G.

  Further, the calculation unit 15 is configured to obtain image data having a mode value in the chromaticity region excluding the reflection component of the frequency distribution, image data at the center of the half value width of the mode value in the chromaticity region, or the color. Since the intermediate value image data of the frequency in the degree region is calculated as the chromaticity value, the chromaticity value excluding the reflection component included in the image can be obtained with certainty. In particular, when the image data at the center of the half-value width of the mode value or the image data of the intermediate value of the frequency in the chromaticity region is set as the chromaticity value, the image data taking the mode value is set as the chromaticity value. Compared to the case, it is strong against noise with a small frequency distribution (not easily influenced by noise), and chromaticity values can be stably obtained (chromaticity values that are not easily changed by noise can be obtained).

  Since the feature quantity extraction unit 16 extracts the feature quantity used for the diagnosis of health based on the chromaticity value calculated by the calculation unit 15, the feature quantity extraction based on the chromaticity value can be easily and accurately extracted. Can be done. As a result, it is possible to accurately diagnose the health level based on the feature amount.

  In addition, if the subject is a tongue, the feature amount based on the chromaticity value can be accurately extracted even when the tongue is wet, even when the subject hits the subject at an angle at which illumination light easily reflects. The accuracy of diagnosis based on this is improved. Furthermore, since the organ image capturing apparatus 1 of the present embodiment has a small size and a low cost configuration as described above, it is suitable as an apparatus used for home nursing or health management in an office.

  The above feature amount is at least one of tongue color, moss color, and moss shape, so it is possible to diagnose the health based on these feature amounts, and to make a diagnosis according to the Oriental medicine diagnosis method It becomes.

[Others]
In the above, the case where the subject to be photographed is a human tongue has been described. However, as long as it is a living body (living object), it may not be a human but may be an animal other than a human. For example, even for the tongue of an animal such as a pet or livestock, the diagnostic item can be accurately detected by applying the method of the present embodiment. In this case, it is possible to quickly and accurately determine the poor physical condition of an animal that cannot communicate its intention.

  In the present embodiment, the case where the subject to be imaged is a tongue has been described as an example. However, for example, a part such as an eyelid that appears to be swollen due to the quality of moisture metabolism and causes specular reflection depending on how the illumination light hits is taken. It can also be targeted. Even in this case, it is also possible to extract the feature amount by calculating the chromaticity value excluding the reflection component from the frequency distribution of the RGB image data obtained from the photographed image.

  FIG. 20 schematically shows an image obtained by observing the inner wall 31 of the large intestine, which is a human body tissue, with an endoscope. Since the digestive juice exists in the digestive organ, the surface of the inner wall 31 is wet. Since an endoscope has a thin tip and a light source and a lens (imaging unit) are arranged close to each other, an angle formed between the light source and the imaging unit with respect to a subject to be photographed is small. As a result, regular reflection tends to occur in the digestive organs. In FIG. 20, a protrusion 32 due to an ulcer appears on the inner wall 31 of the large intestine, but regular reflection tends to occur at the protrusion 32 portion. Even in such a case, by applying the method for obtaining the chromaticity value from the RGB frequency distribution of the photographed image described in the present embodiment, the chromaticity value excluding the reflection component can be obtained accurately, and the examination is performed. Accuracy can be improved.

  INDUSTRIAL APPLICABILITY The present invention can be used for an apparatus that captures an imaging target included in a living body and calculates a chromaticity value.

1. Organ image photographing device (biological chromaticity measuring device)
3 imaging unit 15 calculation unit 16 feature amount extraction unit

Claims (10)

An imaging unit that captures an image of an imaging target included in a living body and acquires an image;
A biological chromaticity measurement comprising: an arithmetic unit that calculates a chromaticity value excluding a reflection component included in the image using a frequency distribution of the image data acquired by the imaging unit apparatus. The imaging unit acquires the image for each of different colors,
For the color with the narrowest frequency distribution of the image, the calculation unit calculates the chromaticity value from the frequency distribution of the color, and sets the frequency distribution as a reference frequency distribution, while regarding other colors By fitting the similar shape of the reference frequency distribution to the frequency distribution of the other color, so that the frequency distribution of the other color is reflected on a higher brightness side than the region where the similar shape overlaps; and The living body chromaticity measurement apparatus according to claim 1, wherein the chromaticity value is calculated from a distribution of the chromaticity region by dividing into a remaining chromaticity region.   The calculation unit enlarges or reduces the shape of the reference frequency distribution in accordance with the mode ratio with the frequency distribution of the other color, and forms the shape of the reference frequency distribution after the enlargement or reduction, The biochromaticity measurement according to claim 2, wherein fitting is performed so that the center of the half-value width of the mode value coincides with the center of the half-value width of the mode value of the other color. apparatus.   The calculation unit enlarges or reduces the shape of the reference frequency distribution in accordance with the mode ratio with the frequency distribution of the other color, and forms the shape of the reference frequency distribution after the enlargement or reduction, The center of the half value width of the mode value is fitted so as to be shifted to the lower luminance side or the higher luminance side than the center of the half value width of the mode value of the frequency distribution of the other color. 2. The biological chromaticity measuring device according to 2.   5. The biological chromaticity measuring apparatus according to claim 2, wherein the reference frequency distribution is a red frequency distribution.   The living body according to claim 1, wherein the calculation unit calculates, as the chromaticity value, image data having a mode value in a region excluding the reflection component of the frequency distribution. Chromaticity measuring device.   6. The calculation unit according to claim 1, wherein the arithmetic unit calculates, as the chromaticity value, image data at a center of a half value width of a mode value in a region excluding the reflection component of the frequency distribution. The biological chromaticity measuring device according to claim 1.   6. The biological color according to claim 1, wherein the calculation unit calculates, as the chromaticity value, image data of an intermediate frequency value in a region excluding the reflection component of the frequency distribution. Degree measuring device.   9. The method according to claim 1, further comprising a feature amount extraction unit that extracts a feature amount used for diagnosis of a health level of a living body based on the chromaticity value calculated by the calculation unit. The biological chromaticity measuring device according to claim 1.   The biological chromaticity measuring apparatus according to claim 9, wherein the feature amount is at least one of a tongue color, a moss color, and a moss shape.

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