KR20150048297A - Apparatus and method for image diagnosis - Google Patents

Abstract

An image diagnosis apparatus comprises: an analysis unit generating a first histogram by performing color histogram analysis with regard to an input tongue image; a determination unit determining at least one second histogram matched with the first histogram among a plurality of reference histograms corresponding to a plurality of health condition information respectively; and a providing unit providing health condition information result corresponding to the second histogram.

Description

[0001] APPARATUS AND METHOD FOR IMAGE DIAGNOSIS [0002]

And more particularly to an apparatus and method for performing a color histogram analysis on an input tongue image to determine a matching histogram and providing corresponding resultant health state information.

Seoljin is to diagnose the health of the patient through the tongue in the diagnosis of Oriental medicine. That is, the health status of the patient can be diagnosed by using the information such as the type of the larynx, In recent years, an ointment system has been developed and is being supplied to assist such ointment.

The tongue tongue is a device for photographing a tongue image (photograph) of a patient and deriving diagnosis results according to a predetermined algorithm and / or providing the result to a medical person.

In coloring, tongue color information is needed. In other words, the health condition of the patient can be grasped through color information of the tongue. Since the color information of the tongue affects the results of coloring, it is necessary to quantitatively analyze the color information.

The image of the patient's tongue also needs a device to diagnose which specific disease the patient has.

According to an aspect of the present invention, there is provided an image processing apparatus comprising: an analyzer for performing a color histogram analysis on an input tongue image to generate a first histogram; A determination unit that determines at least one second histogram matching the first histogram among a plurality of reference histograms corresponding to each of the plurality of health state information; And a providing unit for providing result health state information corresponding to the at least one second histogram.

According to one embodiment, each of the first histogram and the plurality of reference histograms may have a section quantized into a range value.

According to another embodiment of the present invention, the input tongue image is at least one of a sagittal image, a slurping image, and a concept center image, and the plurality of reference histograms may correspond to the same kind of the input tongue image.

According to another embodiment of the present invention, the apparatus further includes a conversion unit for converting the color system of the input tongue image into a CIE color system, and the histogram analysis may be an analysis of at least one of brightness and saturation of the input tongue image.

According to an embodiment, when determining at least two of the second histograms, the determining unit may determine and provide at least two matched histograms in descending order of weight.

According to another embodiment, the determining unit may determine the second histogram by assigning a weight to the second histogram, and the weight may be proportional to the specific weight.

According to still another embodiment, the health state information may correspond to at least one of the abnormality in the stool and the thickness of the stool.

According to an embodiment, when the health state information is the thickness of the tongue, the plurality of reference histograms may include: a first reference histogram obtained from a plurality of tongue images having a predetermined range of tongue thicknesses; And a second reference histogram obtained from a plurality of tongue images having a predefined second range of tongue thickness.

According to another embodiment, the acquisition may be achieved using at least one of average contrast and average saturation for the plurality of tongue images.

According to yet another embodiment, the result health state information may include at least one of a germicidal index and a hypoxic index.

According to another aspect, the analyzing unit performs a color histogram analysis on an input tongue image to generate a first histogram; Determining at least one second histogram matching the first histogram among a plurality of reference histograms for which the determination unit corresponds to each of a plurality of health state information; And providing a resultant health state information corresponding to the at least one second histogram.

According to one embodiment, each of the first histogram and the plurality of reference histograms may be a histogram having a section quantized into a range value.

According to another embodiment of the present invention, the input tongue image is at least one of a sagittal image, a slurping image, and a concept center image, and the plurality of reference histograms may correspond to the same kind of the input tongue image.

According to another embodiment, the converting step may further include converting the color system of the input tongue image into a CIE color system, wherein the histogram analysis is performed on at least one of the contrast and the saturation of the input tongue image have.

According to an embodiment, when determining at least two of the second histograms, the step of determining the second histogram may determine and provide the second histogram in a higher order of matching the first histogram.

According to another embodiment, the step of determining the second histogram further comprises weighting the second histogram to determine the weight, the weight being proportional to the weight.

According to another aspect, there is provided a computer-readable recording medium on which a program for performing the above method is recorded.

1 is a block diagram showing a configuration of an image diagnostic apparatus according to an embodiment.
2 is a flowchart illustrating a procedure of an image diagnostic method according to an exemplary embodiment.
Figure 3 is an exemplary diagram detailing the first histogram generation step of Figure 2, in accordance with one embodiment.
Figure 4 is an exemplary diagram detailing the second histogram determination step of Figure 2, according to one embodiment.
FIG. 5 is an exemplary diagram showing an image in which a tongue color is reproduced according to an embodiment, in accordance with saturation and contrast distribution. FIG.
FIG. 6 is an exemplary diagram showing quantization of a color distribution for an image in which a tongue color is reproduced according to an embodiment.
FIG. 7 is an exemplary view showing a color distribution for an image reproducing colors of a sallow area and a losing area according to an embodiment.

In the following, some embodiments will be described in detail with reference to the accompanying drawings. However, it is not limited or limited by these embodiments. Like reference symbols in the drawings denote like elements.

Although the terms used in the following description have selected the general terms that are widely used in the present invention while considering the functions of the present invention, they may vary depending on the intention or custom of the artisan, the emergence of new technology, and the like.

Also, in certain cases, there may be terms chosen arbitrarily by the applicant for the sake of understanding and / or convenience of explanation, and in this case the meaning of the detailed description in the corresponding description section. Therefore, the term used in the following description should be understood based on the meaning of the term, not the name of a simple term, and the contents throughout the specification.

1 is a block diagram showing a configuration of an image diagnostic apparatus according to an embodiment.

The image diagnostic apparatus may include an analyzing unit, a determining unit, a providing unit, and a converting unit.

According to one embodiment, the image diagnostic apparatus can perform a color histogram analysis on an image reproduced from a tongue color to diagnose a health condition of a patient (or a subject).

According to an exemplary embodiment, the color value may be different depending on the position of the pixel in the tongue color reproduced image. The color value may include at least one of saturation and lightness values.

According to one embodiment, different ranges of color values may lead to different results in diagnosing a patient ' s health condition. The image diagnostic apparatus can quantitatively analyze the saturation value and the lightness value distribution.

According to one embodiment, the imaging apparatus may generate a first histogram for the received image, and determine a second histogram matching the first histogram in a plurality of reference histograms. The imaging device may provide result health state information corresponding to the second histogram.

According to an embodiment, the analyzing unit may perform a color histogram analysis on the input tongue image to generate a first histogram. The tongue image may correspond to at least one of a sagittal image, a lolita image, and a laryngeal image. The tongue image may include an image of the entire tongue.

According to an embodiment, the analyzing unit may perform a histogram analysis for analyzing at least one of the contrast and the saturation of the input tongue image.

According to one embodiment, the determination unit may determine at least one second histogram matching the first histogram using a plurality of reference histograms. The reference histogram may include a plurality of health state information.

According to one embodiment, the health state information may include A disease, B disease, and the like. It may also contain healthy information that does not correspond to any pathology.

According to one embodiment, one of the methods of matching the first histogram to the reference histogram may be to compare the first histogram and the reference histogram by area (or range) and compares the first histogram and the reference histogram.

According to another embodiment, each of the first histogram and the plurality of reference histograms may be a histogram having a section quantized into a range value. By comparing the sections to be quantized, it is possible to check whether the first histogram and the plurality of reference histograms match each other.

According to yet another embodiment, the input tongue image may be for a root image. When the sagittal image is input to the image diagnostic apparatus, the histogram analysis can be performed only for the sagittal image. The reference histogram may be generated using at least one of the image of the whole image, the image of the saury, the image of the snow, the image of the snow, and the image of the snow edge. The determining unit may determine whether the reference histogram generated using the same kind of image is selected and mapped in response to the input of the root image.

According to an embodiment, when determining at least two second histograms, the determining unit may determine and provide at least two matched histograms in descending order of weight.

For example, the determination unit may match the first histogram with the plurality of reference histograms. Among them, the A reference histogram, the B reference histogram, and the C reference histogram can be matched with the first histogram by 70% or more. The determining unit may determine a reference histogram matching the first histogram by 70% or more as a second histogram. The reference histogram, the B reference histogram, and the C reference histogram may be determined in a descending order of the weight to be matched with the first histogram.

According to another embodiment, when the determination section determines at least two second histograms, each of the second histograms may be weighted and provided.

For example, the second histogram may be referred to as the A reference histogram, the B reference histogram, and the C histogram, as in the above example. The probability that the reference histogram A is the first histogram is 60%. The probability that the B reference histogram is the first histogram is 30%. Also, the probability that the C reference histogram is the first histogram is 10%.

The providing unit may provide result health state information corresponding to the at least one second histogram.

According to one embodiment, the health state information may be at least one of the abnormality in the stool and the thickness of the stool.

According to one embodiment, when the health status information is abnormal in the feces, the reference histograms may include a reference histogram of a person who has no abnormality in the feces and a reference histogram of a person who has an abnormality in the feces . The reference histogram may generate a reference histogram for a population of a small number of samples, but may also generate a reference histogram for a population of more than 500 individuals of the same class, for example. The reference histogram may be a histogram calculated as an average rather than a histogram for each of the members of the population.

According to an embodiment, the analyzing unit may generate a first histogram from the input tongue image. The determination unit may map the first histogram to a reference histogram obtained from a group having no abnormality in the feces. Further, the determination unit may map the first histogram to a reference histogram obtained from a group having an abnormality in the feces.

According to another embodiment, when the health state information is a gesture thickness, the plurality of reference histograms may include a first reference histogram obtained from a plurality of tongue images having a predetermined range of gesture thicknesses . In addition, the plurality of reference histograms may include a second reference histogram obtained from a plurality of tongue images having a predefined second range of tongue thickness.

According to one embodiment, the first reference histogram may be obtained using at least one of average contrast and average saturation for a plurality of tongue images. Similarly, the second reference histogram can also be obtained using at least one of average contrast and average saturation for a plurality of tongue images.

According to one embodiment, the resulting health state information may include at least one of a germicidal index and a left ventricular index.

The conversion unit may convert the color system of the input tongue image into a CIE (Commission Internationale de l'Eclairage) color system. The conversion unit may provide information on saturation and contrast by the CIE color system. That is, it is possible to provide the range in which the saturation and the contrast are distributed.

2 is a flowchart illustrating a procedure of an image diagnostic method according to an exemplary embodiment.

Step 210 is a step in which the analyzer performs a color histogram analysis on the input tongue image to generate a first histogram. Step 210 will be described later in the detailed description of FIG.

According to one embodiment, the transforming unit may convert the color system of the input tongue image into a CIE color system. The histogram analysis may be for at least one of brightness and saturation of the input tongue image.

Step 220 is a step of determining at least one second histogram matching the first histogram among a plurality of reference histograms in which the determining unit corresponds to each of a plurality of health state information. Step 220 is described below in the detailed description of FIG.

Step 230 is a step in which the providing unit provides the resulting health status information corresponding to the at least one second histogram.

Figure 3 is an exemplary diagram detailing the first histogram generation step of Figure 2, in accordance with one embodiment.

Step 310 is a step in which the image diagnostic apparatus receives the tongue image. The tongue image may be an image of the entire tongue. In addition, the tongue image may correspond to at least one of a sagittal image, a lolita image, and a laryngeal image.

Step 320 is a step of converting the color system for the received tongue image into a CIE color system.

Step 330 is an analysis of at least one of the contrast and the saturation for the CIE color system conversion. Through the analysis, a first histogram can be generated.

Figure 4 is an exemplary diagram detailing the second histogram determination step of Figure 2, according to one embodiment.

Step 410 is a step of matching the first histogram to the reference histogram. The reference histogram may correspond to each of a plurality of health state information. The reference histogram may correspond to the same kind of input tongue image.

For example, if the first histogram is generated through image analysis for the entire tongue, a plurality of reference histograms may also be generated through image analysis for the entire tongue.

According to one embodiment, the reference histogram may be a plurality of histograms corresponding to the same kind as the input tongue image.

According to one embodiment, the analyzing unit may analyze the image of the entire image to generate a first histogram. The determining unit may match the first histogram using the reference histogram generated from the lexical image.

According to another embodiment, the analysis unit may analyze the dull muscle image to generate the first histogram. In this case, the determination unit can match the first histogram using the reference histogram generated from the root image.

Step 420 is a step of determining how many reference histograms among the plurality of reference histograms match the first histogram.

According to one embodiment, how many reference histograms match the first histogram may be predetermined in advance by the user of the imaging diagnostic apparatus. For example, if the user matches the first histogram to the image diagnostic apparatus by 75% or more, the user may designate the second histogram as a matching histogram.

According to an embodiment, when the second histogram matching the first histogram is determined as one, one reference histogram may be determined as the second histogram.

According to another embodiment, when the second histogram matching the first histogram is determined to be at least two, it is possible to determine and provide the matched weighting order. Also, weighting may be given in proportion to the order of gravity.

Step 430 is a step in which, when the second histogram is at least two or more, the determining unit determines and provides the order of matching with the first histogram in descending order of weight.

For example, when the A reference histogram is 80%, the B reference histogram is 87%, and the C reference histogram is 83%, the determination unit determines that the reference histogram 2 histogram) can be determined and provided.

Step 440 is a step of weighting and providing a weight if the second histogram is at least two or more.

For example, the probability of matching the first histogram of the three reference histograms may be as follows. A reference histogram may be 80%, B reference histogram 15%, and C reference histogram 5%. Therefore, it can be determined that a ratio of A: B: C = 16: 3: 1 can be obtained.

Step 450 is a step of determining the second histogram to be matched with the first histogram. When the second histogram is at least two or more, the determining unit may provide the weighted matched weight to the first histogram in a descending order, and may provide weighted values. The weight may be proportional to the matching weight.

FIG. 5 is an exemplary diagram showing an image in which a tongue color is reproduced according to an embodiment, in accordance with saturation and contrast distribution. FIG.

510 is an exemplary view showing a human tongue. The tongue can be divided into several regions. For example, it can be divided into a sowing area, a losing area, a snowing area, and a snowing area.

According to an exemplary embodiment, a first histogram may be generated by analyzing an image of the entire image. In addition, it is possible to generate a plurality of reference histograms by analyzing the images of the entire image.

According to another embodiment, a first histogram may be generated by analyzing an image corresponding to at least one of a sallow region, a laryngeal region, and a design center region. In addition, a plurality of reference histograms of the same type of image corresponding to the analyzed image can be generated.

FIG. 520 is an exemplary diagram showing an image in which a tongue color is reproduced in accordance with a color distribution. FIG. The color distribution can be represented by a system based on the CIE color system.

According to one embodiment, saturation and contrast values may be distributed differently depending on the position of a pixel in an image in which a tongue color is reproduced. The saturation and the contrast distribution can be represented using a color histogram. The histogram can be used to quantitatively analyze the saturation and intensity distribution.

FIG. 6 is an exemplary diagram showing quantization of a color distribution for an image in which a tongue color is reproduced according to an embodiment.

FIG. 610 is a diagram showing a color histogram by analyzing an image reproduced from tongue color with respect to saturation and contrast. The horizontal axis represents saturation, and the vertical axis represents lightness and darkness. The distribution of each unit area is shown in color. In the case of FIG. 610, there are many distributions in the vicinity of saturation 18 and darkness 40.

In FIG. 620, a range is divided into a grid shape on the histogram of FIG. 610 to quantize the interval. A plurality of unit areas may be combined into one range and compared with the corresponding part of the histogram of the comparison object. A range includes a plurality of unit areas, and the numerical values thereof may be the same or different.

According to one embodiment, a saturation of 5 units and a darkness of 4 units can be set in one range. The one area has unit areas having different numerical values, but can be represented by one numerical value.

According to one embodiment, the range can be divided into a 4 X 4 grid shape as shown in FIG. 620. Each of the numerical values in the unit area may be different, but one range can be compared as having one numerical value. The comparison can be made by matching the portions where the two histograms correspond to each other.

According to an exemplary embodiment, as shown in FIG. 620, the first to seventh ranges are used as a reference, and the comparison histogram can be matched and confirmed to what degree.

FIG. 7 is an exemplary view showing a color distribution for an image reproducing colors of a sallow area and a losing area according to an embodiment.

Figure 710 is an example of an image of the tongue divided into a laryngeal region and a sarcomere region; The image diagnostic apparatus can receive only the lingual area and analyze it as a histogram. In addition, it is possible to receive only the sallow area and analyze it as a histogram.

Figure 720 is an exemplary diagram showing a histogram for the sallow root region.

According to one embodiment, the histogram may have intervals that are quantized into range values.

730 is an exemplary diagram showing a histogram for the losing area.

According to another embodiment, the histogram may have a section that is quantized into a range value. When the histograms shown in Figs. 720 and 730 are compared, it can be seen that the distribution of saturation and contrast is different.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA) A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions.

The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software.

For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded.

The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software.

Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like.

Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like.

The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, the scope of the present invention should not be limited to the described embodiments, but should be determined by the equivalents of the claims, as well as the claims.

Claims (17)

An analysis unit for performing a color histogram analysis on an input tongue image to generate a first histogram;
A determination unit that determines at least one second histogram matching the first histogram among a plurality of reference histograms corresponding to each of the plurality of health state information; And
And providing result health state information corresponding to the at least one second histogram,
And an imaging device. The method according to claim 1,
Wherein each of the first histogram and the plurality of reference histograms is a histogram having a section quantized into a range value. The method according to claim 1,
The input tongue image,
Wherein the plurality of reference histograms correspond to the same type as the input tongue image. The method according to claim 1,
Further comprising a conversion unit for converting a color system of the input tongue image into a CIE color system,
Wherein the histogram analysis is an analysis of at least one of brightness and saturation of the input tongue image. The method according to claim 1,
Wherein,
And determines and provides at least two or more of the second histograms in descending order of the weight to be matched with the first histogram. 6. The method of claim 5,
Wherein the determination unit determines a weight by assigning a weight to the second histogram, and the weight is proportional to the specific gravity. The method according to claim 1,
Wherein the health state information comprises:
An abnormality in the feces, and a thickness of the fascia. The method according to claim 1,
If the health state information is a gender thickness,
Wherein the plurality of reference histograms include:
A first reference histogram obtained from a plurality of tongue images having a predefined range of tongue thickness; And
A second reference histogram obtained from a plurality of tongue images having a predefined second range of tongue thickness
And an imaging device. 9. The method of claim 8,
The acquisition includes:
Wherein the at least one tongue image is acquired using at least one of average contrast and average saturation for the plurality of tongue images. The method according to claim 1,
The result health state information may include:
A gyro index and a false index. Generating a first histogram by performing a color histogram analysis on an input tongue image;
Determining at least one second histogram matching the first histogram among a plurality of reference histograms for which the determination unit corresponds to each of a plurality of health state information; And
Providing a result health state information corresponding to the at least one second histogram
/ RTI > 12. The method of claim 11,
Wherein each of the first histogram and the plurality of reference histograms is a histogram having a section quantized into a range value. 12. The method of claim 11,
The input tongue image,
Wherein the at least one reference histogram corresponds to the same type of the input tongue image. 12. The method of claim 11,
Wherein the converting unit further comprises converting the color system of the input tongue image into a CIE color system,
Wherein the histogram analysis is an analysis of at least one of brightness and saturation of the input tongue image. 12. The method of claim 11,
Wherein determining the second histogram comprises:
Wherein when determining at least two or more of the second histograms, the second histogram is determined in the order of a higher weight to be matched with the first histogram. 16. The method of claim 15,
Wherein determining the second histogram comprises:
Further comprising assigning a weight to the second histogram to determine the weight, wherein the weight is proportional to the weight. A computer-readable recording medium having recorded thereon a program for performing the method according to any one of claims 11 to 16.

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