KR100884745B1 - Method and apparatus for measuring coarseness level in target organ from ultrasound visual image - Google Patents

Abstract

The present invention relates to a method and apparatus for quantitatively measuring the coarseness of a living organ from an ultrasound image, comprising: (a) obtaining an ultrasound region image of a living organ; (b) setting a target area for the ultrasound area image; (c) setting a comparison area distinct from the target area to obtain a representative roughness value quantified from the brightness value distribution of the pixels in the target area and the comparison area; And (d) calculating a degree of coarseness of the living organ, corresponding to the quantified representative roughness value.

Ultrasound Image, Roughness, Roughness

Description

Method and apparatus for measuring coarseness of target organs from ultrasound image level in target organ from ultrasound visual image}

1 is a schematic diagram showing a state in which an ultrasound image of a human is generally obtained.

2 is a flowchart illustrating a method of quantitatively measuring the roughness of a target organ from an ultrasound image according to the present invention.

3A, 3B and 3C are flow charts illustrating in more detail a method of obtaining a representative roughness value quantified in accordance with the present invention.

4 is a flowchart detailing another method of obtaining a representative roughness value quantified in accordance with the present invention.

5 is a flow chart showing in more detail the roughness calculation method according to the present invention.

6 is a block diagram schematically illustrating an apparatus for quantitatively measuring roughness of a target organ from an ultrasound image according to the present invention.

7 is a block diagram illustrating in more detail the area setting unit and the control operation unit according to an embodiment of the present invention.

8 is a block diagram illustrating in more detail the control operation unit according to an embodiment of the present invention.

9 is a block diagram illustrating in more detail the roughness calculator according to the present invention.

10 is an example of an image obtained by ultrasonic imaging.

11 is a graph showing the correlation between the roughness reading and the quantified representative roughness value according to the present invention.

The present invention relates to a method and apparatus for quantitatively measuring coarseness in a region image of a human body or other animals. In particular, the present invention relates to a method of measuring coarseness of a target organ by quantifying a representative roughness of a target organ from a coarseness level distribution of pixels of an ultrasound region image.

Organ tissue in the body, especially the liver, can cause various complications. Moreover, hardening by itself poses the risk of organ dysfunction. For example, cirrhosis of the liver may be accompanied by destruction of the liver cells in a state in which abnormal regenerative nodules between the liver cells prevent normal blood flow due to fibrosis of the liver parenchyma in the human body. In particular, the higher the alcohol consumption in the country, the higher the incidence of alcoholic cirrhosis, attention is focused on the development of a method for easily and cheaply measuring the degree of fibrosis of such organs.

Liver cirrhosis is known to be very closely related to fibrosis of tissues in the liver parenchyma. Thus, various methods have been proposed or are currently used to measure quantitative fibrosis. Nevertheless, the reality is that there is a continuous need for a method for measuring the degree of fibrosis of organs simply and inexpensively.

For example, computed tomography (CT) is a widely used method for obtaining area images of such human organs. In recent years, many studies have been conducted on CT because of its high image resolution and the ability to photograph the coarse pattern in the parenchyma of the liver in the image relatively unaffected by the imaging technique and the shooting conditions. However, CT imaging is expensive, and there is a problem such as stability because radiation exposure must be taken.

In addition, liver biopsy is performed to measure fibrosis of such organs. Liver biopsy is a method of removing organs, for example liver tissue, to analyze tissues and components. This method provides the most fundamental and accurate results for all liver disease diagnoses and tests. However, the procedure is complicated and invasive to the human body. In addition, liver biopsies are not performed when no signs of disease appear, so they are difficult to apply for preventive measures.

Ultrasonic imaging has been widely used for diagnosing an area image of an organ, for example, an image of each part of a living body including an abdominal area. Such ultrasonography is relatively inexpensive and simple to take. It is also a safe method and non-invasive because it does not use radiation fundamentally.

However, in the ultrasound imaging method, the effects of the photographing conditions and the imaging technique on the quality of the ultrasound image are absolute, and there are disadvantages in that the reflection characteristics and the resolution of each portion through which the ultrasound passes are not constant. Accordingly, when various diagnosis is performed using the photographed image, there is much room for the arbitrary interpretation of the diagnosis person, and as a result, the reliability of diagnosis is not completely guaranteed.

Currently, there are about four stages of clinically determining the fibrosis index of liver parenchyma using images by ultrasonography. It is often the case that the error between specialists with diagnostic experience is the first step, and the probability of such error is up to 20%.

Recently, an elastic modulus imaging method using elastic ultrasound has been developed. As a result, a study on the diagnosis of liver cirrhosis has been reported in the change of elastic modulus distribution which may be related to fibrosis. This method has the advantage of obtaining an image that reflects the change in the roughness of the tissue directly. However, the resolution degradation due to the local elastic coefficient derivation algorithm using the secondary displacement measurement has been a problem.

Therefore, there is a strong demand for the development of a method that can provide a quantified degree of fibrosis regardless of shooting conditions and the like while using a simple and inexpensive ultrasound imaging image.

Therefore, many attempts have been made to measure the coarseness using only the ultrasound image, but the coarseness of the image due to the fundamental problem of the ultrasonic imaging method, for example, the distortion of the image characteristic due to the roughness distribution and the irregularity of the resolution, has been reported. Difficulties in quantitative extraction and difficulty in securing reliability are currently difficult to overcome.

The technical problem to be achieved by the present invention is to quantify the roughness distribution characteristics of the target organs from the image including the target organs and the comparative organs obtained by ultrasound imaging to extract the representative roughness value and to measure the roughness quantitatively from the representative roughness values. There is a way to provide.

Another object of the present invention is to provide an apparatus required to provide a method for quantitatively measuring the roughness.

The present invention to achieve the above technical problem, (a) obtaining an ultrasound region image of a living organ; (b) setting a target area for the ultrasound area image; (c) obtaining a quantified representative roughness value of the target region from the distribution of brightness values of the pixels in the target region; And (d) calculating the degree of coarseness of the living organs corresponding to the quantified representative roughness values. The method of the present invention provides a method for quantitatively measuring the coarseness of living organisms from an ultrasound image. .

Preferably, obtaining the quantified representative roughness value of the target area from the distribution of brightness values of the pixels in the target area may include: (c1) obtaining a brightness distribution pattern of the target area; (c2) extracting a representative roughness value from the roughness values of the brightness distribution patterns; And (c3) quantifying the extracted representative roughness value.

More preferably, extracting a representative roughness value from the roughness values of the brightness distribution patterns comprises: obtaining a statistic representing a roughness value distribution of each pixel of the target area; And determining a statistical value representing roughness calculated from the statistics representing the roughness value distribution as a representative roughness value.

Preferably, the statistics are characterized in that the matrix value representing the roughness.

Preferably, the matrix value is a cooccurrence matrix representing roughness.

Preferably, the step (c3) of quantifying the extracted representative roughness value includes: (c31) setting a comparison area distinguished from the target area in the target area; (c32) obtaining a representative roughness value of the comparison area from the distribution of the roughness values of the pixels in the comparison area; And (c33) quantifying a representative roughness value of the target area by using the representative roughness values of the comparison area and the target area.

Preferably, the step (c33) of quantifying the representative roughness value of the target area by using the representative roughness values of the comparison area and the target area is a value relative to the representative roughness value of the comparison area. Representative roughness values are quantified.

Preferably, the step (c33) of quantifying the representative roughness value of the target area using the representative roughness values of the comparison area and the target area is performed by linear combination of numerical values relative to the representative roughness value of the comparison area. A representative roughness value of the target area is quantified.

Preferably, the step (c) of obtaining the quantified representative roughness value of the target area from the distribution of the brightness values of the pixels in the target area comprises: (c1) each of the target area including the target area already set in the ultrasound area image; Setting at least one target area and a comparison area; (c2) obtaining distribution patterns of quantified representative roughness values for each of the set one or more target areas and the comparison area; And (c3) extracting the quantified representative roughness value of the ultrasound region image from the distribution pattern.

More preferably, extracting the quantified representative roughness value is a value obtained by calculating an average of representative roughness values of the distribution pattern.

Also preferably, extracting the quantified representative roughness value may be a value obtained by calculating a median of representative roughness values of the distribution pattern.

Preferably, (d) corresponding to the quantified representative roughness value, calculating the degree of roughness of the living organs, (d1) collecting samples of the ultrasound region image; (d2) obtaining quantified representative roughness values and associated roughness readings of the samples from each of the samples; (d3) obtaining a proportional function between the quantified representative roughness values and the coarseness reading; And (d4) calculating a roughness corresponding to the quantified representative roughness value of the target area by using the proportional function.

In addition, an apparatus for quantitatively measuring the coarseness of a living body organ for purposes of the present invention, the ultrasonic imaging unit for generating an image of the living body organ imaging; An area setting unit for setting a target area for the generated image; A memory unit for storing the generated image or the target area set by the target area setting unit as data; A control calculation unit for generating a roughness by calculating and quantifying a representative roughness value with the image data stored in the memory unit; And a display unit for displaying the calculation and generation results of the control operation unit or displaying an image of the living organ.

Preferably, the area setting unit comprises a target area setting unit for setting at least one target area; And a comparison area setting unit configured to set one or more comparison areas, wherein the control operation unit extracts a roughness value distribution pattern to obtain a distribution pattern of quantified representative roughness values for each of the one or more set target areas and the comparison areas. part; And a representative roughness value extracting unit for extracting a representative roughness value quantified from a distribution pattern of roughness values extracted by the roughness value distribution pattern extracting unit.

Preferably, the control operation unit comprises: a brightness distribution pattern extraction unit for obtaining a brightness distribution pattern of the target area; A roughness value extraction unit for extracting a representative roughness value from roughness values of the brightness distribution patterns obtained by the brightness distribution pattern extraction unit; And a representative roughness value quantifying unit for quantifying the representative roughness value extracted by the roughness value extracting unit.

More preferably, the control operation unit comprises an image sample collection unit for collecting the samples of the generated image; A representative roughness value extracting unit obtaining quantified representative roughness values of the samples from the samples of the image collected by the image sampling unit; And a coarseness reading unit for obtaining a coarseness reading related to the quantified representative roughness value of the samples from the samples of the image collected by the image collecting unit.

Preferably, the roughness value extracting unit includes a pixel statistics unit for obtaining a statistic indicating a roughness value distribution of each pixel of the target region, wherein the statistical value indicating roughness calculated and output from the pixel statistics unit is represented as a representative roughness value. Characterized in that.

More preferably, the pixel statistics unit may calculate a matrix value.

Preferably, the area setting unit comprises: a target area setting unit for setting at least one target area; And a comparison area setting unit for setting at least one comparison area, wherein the representative roughness value quantification unit comprises: a comparison area for obtaining a comparison area representative roughness value from a distribution of roughness values of pixels in the comparison area by the comparison area setting unit; A representative roughness value extracting unit; And a roughness value comparison quantization unit for comparing the representative roughness value obtained by the roughness value extracting unit with the representative roughness value of the comparison region obtained by the comparison region representative roughness value extracting unit to quantify a representative roughness value. It features.

Hereinafter, a method and apparatus for quantitatively measuring the coarseness of a living organ from an ultrasound image according to the present invention will be described in detail with reference to the accompanying drawings. In the following description of the present invention, detailed descriptions of related well-known technologies or configurations will be omitted if it is determined that the detailed description of the present invention may unnecessarily obscure the subject matter of the present invention. In addition, terms to be described below are terms defined in consideration of functions in the present invention, which may vary according to a client's or operator's intention or custom. Therefore, definitions should be made based on the contents throughout the specification. Like reference numerals refer to like elements throughout.

1 is a schematic diagram 100 generally illustrating the appearance of an ultrasound image of a person.

In general, there is shown an ultrasound device 120 that can be encountered a lot in a hospital or the like. The inside of the abdomen and the like of the human body 120 to be examined is inspected by the ultrasonic tester provided in the ultrasonic equipment 120. The ultrasound image generated through the inspection may be viewed through a display device provided in the ultrasound apparatus 120.

The present invention proposes a method of indexing the change in the reflection refraction characteristic of the ultrasound which appears as the degree of coarseness increases in the human organs, that is, the target organ using an ultrasound image, and proposes a method for estimating the degree of coarseness from such changes. .

An ultrasound image, for example, an ultrasound image of an abdomen region of a human body is a reconstruction of a refraction characteristic of ultrasound of each biological tissue into a coarseness level and then reconstructed into a 2D image. The main constituents of biological tissues are fat and moisture. Ultrasound shows different reflection characteristics depending on the distribution of fat and moisture. As a result, the roughness of the region is rough in the tissue in the captured image. Therefore, the roughness or roughness of each pixel of the taken abdominal region image is proportional to the content of fibrous tissue in the biological tissue region corresponding to the pixel region.

However, such roughness or roughness of the pixel itself is hardly considered to directly represent the roughness of the target organ. This is because the roughness itself may be relatively different depending on the ultrasound abdominal region photographing condition and the photographing technique. That is, two ultrasound abdominal region images captured at the same position with respect to the same object may have different coarseness or pixel coarseness at the same position. As a result, it is difficult to directly use the roughness of the ultrasound image as a roughness index.

In order to overcome this, an embodiment of the present invention proposes a method of estimating roughness by extracting and quantifying a representative roughness value of a target organ region. That is, it is possible to use the representative roughness value as an index value for roughness by eliminating the influence of the condition in which the ultrasound region image is acquired, for example, a shooting condition, on the quantified representative roughness value.

In order to more accurately determine the coarseness at the time of the ultrasound abdominal region imaging, it is important to maintain the homeostasis of the shooting conditions. For example, it may be desirable to adjust the photographing angle and the degree of adhesion during ultrasound abdominal imaging so that the roughness distribution in the image is as constant as possible. In addition, it is preferable to include an organ, for example, a liver parenchyma, a spleen, and the like to be photographed in the image.

2 is a flowchart illustrating a method of quantitatively measuring the roughness of a target organ from an ultrasound image according to the present invention.

First, in step S210, an ultrasound image is obtained using an ultrasound imaging apparatus. Usually this ultrasound image will be an abdominal region ultrasound image. In operation S220, a target region for the ultrasound region image obtained above is set.

The next step is to obtain the quantified representative coarseness level of the liver, for example, from the brightness values of the pixels in the target region set above (S230).

Finally, the degree of roughness of the living organs corresponding to the quantified representative roughness value is calculated (S240).

Of course, each step further includes a detailed step, and a more detailed step-by-step description of the steps presented above will be described below.

3A, 3B and 3C are flow charts illustrating in more detail a method of obtaining a representative roughness value quantified in accordance with the present invention.

First, FIG. 3A is an embodiment illustrating in more detail the step S230 of obtaining the quantified representative roughness value shown in FIG. 2. Obtaining the quantified representative roughness value of step S230 may be further divided into three steps. 3A, obtaining a brightness distribution pattern of the target area for the ultrasound region image (S231), extracting a representative roughness value from the roughness values of the obtained brightness distribution patterns (S233), and quantifying the extracted representative roughness value (S235) is it.

Step S233 may be further divided into two steps as shown in FIG. 3B. That is, the step (S2331) of obtaining a statistic indicating the roughness value distribution of each pixel of the target region and the step of determining the statistical value indicating the roughness calculated from the statistic indicating the roughness value distribution as the representative roughness value (S2332) will be.

In one embodiment according to the present invention, the statistic obtained in step S2331 may be composed of a matrix value representing roughness, and the matrix value may be a cooccurrence matrix representing roughness.

Quantifying the representative roughness value in step S235 may be further divided into three steps as shown in FIG. 3C. That is, a comparison area distinguished from the target area is set in the target area (S2351), and then a representative roughness value of the comparison area is obtained from the distribution of the roughness values of the pixels in the comparison area (S2352), and finally the comparison is performed. Representative roughness values of the target region are quantified using the representative roughness values of the region and the target region (S2353).

In a preferred embodiment of the present invention, the step S2353 is characterized by quantifying the representative roughness value of the target area by a value relative to the representative roughness value of the comparison area.

In another embodiment, in operation S2353, the representative roughness value of the target area may be quantified by linear combination of a value relative to the representative roughness value of the comparison area.

FIG. 4 shows another embodiment showing in more detail the step S230 of obtaining the quantified representative roughness value shown in FIG. 2. That is, S230 may be subdivided into three steps as another embodiment different from the embodiment shown in FIG. 3.

First, as shown in S232, one or more target areas and a comparison area are respectively set including the target areas already set in the ultrasound area image. In S234, a distribution pattern of quantified representative roughness values for one or more target areas and a comparison area, respectively, is set. Finally, in S236, the quantified representative roughness value of the ultrasound region image is extracted from the previously obtained distribution pattern.

Extracting the representative roughness value quantified in step S236 may appear in various aspects. For example, the average of representative roughness values of the distribution pattern obtained above may be calculated to obtain a quantified representative roughness value. As another example, the representative roughness value quantified may be determined by calculating a median of the representative roughness values of the distribution pattern obtained above.

2, when the representative roughness value quantified in S230 is obtained, the roughness is calculated in the last S240. FIG. 5 shows the step S240 in more detail. That is, Figure 5 is a flow chart showing in more detail the roughness calculation method according to the present invention.

Roughness calculation includes a first step (S242) of first collecting samples of the ultrasound region image. Obtaining the quantified representative roughness values and associated roughness readings of the samples from each of these collected specimens will be the second step (S244), and the third proportional function between the quantified representative roughness values and the roughness readings In step S246, a roughness corresponding to the quantified representative roughness value of the target area is calculated using the proportional function obtained last, and the roughness calculation is completed.

In this case, the ultrasound region image may be the abdominal region image of the human body in most cases, and the target image may be an image including the liver of the human body.

6 is a block diagram schematically illustrating an apparatus for quantitatively measuring roughness of a target organ from an ultrasound image according to the present invention.

The ultrasound image capturing unit 610 may be formed of general ultrasound image capturing equipment. The image data obtained by the ultrasound image capturing unit 610 is stored in the memory unit 630. The image data stored in the memory unit 630 is calculated, determined and quantified by the control calculator 640 to estimate the roughness therefrom.

If the ultrasound abdominal region image thus obtained is composed of pixels having brightness values between 0 and 255, the number of pixels may vary from one embodiment to another, and may be set to approximately 500 × 700. The data may be processed by the control operation unit 640, and the output of the image may be performed by a display device such as a monitor of the input / output unit 650.

The control operation unit 640 is a part that is responsible for the overall control of the device for quantitatively measuring the coarseness of the living organs according to the present invention. In general, the control operator 640 controls the peripheral apparatuses such as the ultrasound image capturing unit 610, the memory unit 630, the region setting unit 620, and the input / output unit 650. In addition, operations of the above steps, such as obtaining statistics, or performing mathematical operations such as matrix calculation, average calculation, and median calculation, are also performed.

7 is a block diagram illustrating in more detail the area setting unit 620 and the control operation unit 640 according to an embodiment of the present invention.

The area setting unit 620 is a device for setting a target area and a comparison area, in particular, in an apparatus for obtaining an ultrasound image. In this embodiment, the area setting unit 620 is divided into a target area setting unit 622 and a comparison area setting unit 624. The representative roughness value obtained in each area setting unit is sent to the control operation unit 640 for comparison.

The control operation unit 640 performs an operation in the roughness value distribution pattern extraction unit 641 to obtain a distribution pattern with the representative roughness values obtained by the target area setting unit 622 and the comparison area setting unit 624. The representative roughness value extractor 642 is responsible for extracting the quantified representative roughness value of the ultrasound region image from the obtained distribution pattern.

8 is a block diagram illustrating in more detail the control operation unit 640 according to an embodiment of the present invention.

The control operation unit 640 shown in FIG. 8 includes a brightness distribution pattern extraction unit 643, a roughness value extracting unit 644, a representative roughness value quantifying unit 645, and a roughness calculating unit 646.

The brightness distribution pattern extractor 643 obtains a brightness distribution pattern from an ultrasound image part set as a target area. The ultrasound abdominal region image is formed of pixels having a brightness value between 0 and 255 according to an embodiment. The number of pixels can be set to approximately 500 x 700. The roughness value extractor 644 extracts a representative roughness value from the roughness values of the brightness distribution patterns. The extracted representative roughness value is again quantified by the representative roughness value quantification unit 645.

The representative roughness value quantifier 645 may be configured with a comparison area representative roughness value extractor 6645 and a roughness value comparison quantifier 6645.

According to an embodiment of the present invention, in order to extract a representative roughness value of a target area, for example, a liver parenchyma, from an ultrasound abdominal area image, a region of interest is first set in the ultrasound area image. 10 will be described with reference to FIG. 8 to describe in detail. The liver parenchyma in the target region in the ultrasound abdominal region image is set as the target region 1010. The comparison area 1030 is set together to quantify the representative roughness value extracted for the target area 1010.

In order to quantify liver parenchyma, which is the target region 1010, as a comparison target, an organ in the human body having a texture characteristic regardless of the progression of the lesion of interest may be selected. In the embodiment of the present invention, the spleen region 1030 is set as a comparison region.

First, when the set area is the comparison area 1030 rather than the liver parenchyma, the representative roughness of the comparison area 1030 is used as a reference for quantifying the roughness value of the target area to be extracted from the target area 1010 of the liver parenchyma. Extract the value. The representative roughness value of the comparison area obtains roughness distributions of the pixels in the comparison area 1030, and then determines the representative roughness value of the comparison area.

After setting the spleen area, a texture parameter value reflecting the roughness on the corresponding area is extracted and set as the representative roughness value of the comparison area 1030 which is the spleen area.

Since the spleen area shows a constant texture echo characteristic irrespective of the progression of cirrhosis of the liver, extracting a portion having a uniform roughness distribution to reflect this is appropriate as a comparison target for quantifying representative roughness values of the target area.

The setting and the calculation of the area for the extraction of the roughness value are performed by the control operation unit 640, for example, a microprocessor.

8, when the roughness distribution of the pixels described above is obtained, the roughness distribution is sent to the comparison area representative roughness value extractor 6645 to determine the representative roughness value of the comparison area. This comparison area representative roughness value and the representative roughness value sent from the roughness value extracting unit 644 are compared and quantified by the roughness value comparison quantifying unit 6452.

The representative roughness value for the comparison area 1030 which is such a spleen area may be determined to be approximately 75.6 by the extraction method as described above.

As described above, after the representative roughness value of the comparison area 1030 to be used as a comparison target of the representative roughness values to be extracted for the target area 1010 is obtained, the representative roughness value of the target area 1010 is obtained.

Since the liver material, which is the target region 1010 as shown in FIG. 10, is known to have a relatively uniform texture, the representative roughness value may be extracted by analyzing roughness values of the pixels in the setting region 1010. .

After obtaining the roughness value distribution of the pixels in the setting area 1010 in the target area, the representative roughness value is determined from the roughness distribution. In this case, the representative roughness value for the liver parenchymal target area 1010 may be extracted as, for example, 53.6.

The next step, quantification, may be performed by quantifying a representative roughness value of the target region 1010 with respect to other regions of the ultrasound abdominal region image including the target region 1010.

For example, the representative roughness value of the target area 1010 is quantified or normalized to a relative value with respect to the representative roughness value of the comparison area 1030 which is the spleen area whose texture characteristics are invariant. That is, if the representative roughness value of the quantified target region 1010 is C ', the representative roughness value of the target region 1010 is C, and the representative roughness value of the comparison region 1030 is A, C' is f (C; Can be calculated and quantified. In other words, by setting f (C; A) = (C-A), C ′, which is a representative roughness value of the quantified target region 1010, may be calculated to obtain a quantified value.

Such a function f (C; A, B) may be any function as long as it is a function monotonously increasing as well as a function given as described above.

The representative roughness value of the target area 1010 calculated and quantified by the function as described above is obtained as (53.6-75.6) = -22.0. Since the representative roughness value of the quantified target region 1010 is quantified as a value relative to the comparison region, it is possible to compare the numerical value obtained from another ultrasound region image.

When the quantified representative roughness value for the target region is obtained as described above, the texture parameter is obtained by the representative roughness values of the comparison region. In the spleen area, a target area having a relatively uniform texture characteristic is selected to calculate texture parameters to obtain a quantified representative roughness value of the target image.

The roughness calculating unit 646 calculates the actual roughness from the representative roughness value of the quantified target region 1010 thus obtained.

A more detailed function of the roughness calculator 646 is shown in FIG. 9.

The roughness calculator 646 includes an image sample data collector 6441, a roughness reader 6463, a proportional function 6465, and a roughness calculator 6467.

First, the image sample data collecting unit 6641 collects data about samples of the ultrasound region image from the ultrasound image. Thereafter, the target area for each of the samples, i.e., the fibrosis indexes for the liver parenchyma and the quantified representative roughness value are obtained. The representative roughness values quantified at this time can be obtained as described above.

Thereafter, a correlation between the quantified representative roughness values of the samples and the fiberization indices is obtained. For example, as shown in FIG. 11, a quantified representative roughness value for the fiberization index, that is, a distribution of values that can be regarded as a fiberization index (coarse) since it is a quantified value.

Thereafter, the proportional function unit 6465 obtains a proportional function between the fiberization index and the quantified representative roughness values by, for example, the least square method.

In the roughness calculation unit 6467, a corresponding fibrosis index can be obtained by substituting the quantitative representative roughness value of the target region into the correlation function. Therefore, the quantified representative roughness value obtained in the embodiment of the present invention may be directly estimated or considered as a fiberization index.

11 is a graph showing the correlation between the roughness reading and the quantified representative roughness value according to the present invention. This graph gives the correlation between the quantified representative roughness values of the samples and the fiberization indices as described above.

The fibrosis index used at this time uses the result of the determination of the coarseness of the liver parenchyma of a real clinical specialist.

As described above, the present invention has been described based on the preferred embodiments, but these embodiments are intended to illustrate the present invention, not to limit the present invention, so that those skilled in the art to which the present invention pertains can practice the above without departing from the technical spirit of the present invention. Various changes, modifications or adjustments to the example will be possible. Therefore, the protection scope of this invention will be limited only by the appended claims, and should be construed as including all changes, modifications or adjustments.

As described above, according to the present invention, by linearly adjusting the roughness distribution of the pixels constituting the ultrasound region image, it is possible to reduce the influence of the ultrasound image by the imaging condition and the imaging technique, and to represent the representative roughness of the target region of the organ of interest. You can get the value. By quantifying the representative roughness value, comparison between ultrasound images is possible. As a result, the degree of coarseness of the organ of interest can be quantitatively measured, and the degree of fibrosis of the organ can be easily and inexpensively measured.

Claims (19)

(a) obtaining an ultrasound region image of a living organ; (b) setting a target area for the ultrasound area image; (c) setting a comparison area distinct from the target area to obtain a representative roughness value quantified from roughness values of brightness distribution patterns of pixels in the target area and the comparison area; And and (d) calculating the degree of coarseness of the living body organ corresponding to the quantified representative roughness value. The method of claim 1, wherein step (c) comprises: (c1) obtaining a brightness distribution pattern of the target area; (c2) extracting a representative roughness value from the roughness values of the brightness distribution patterns; And (c3) quantifying the extracted representative roughness value; quantitatively measuring coarseness of a living organ from an ultrasound image. The method of claim 2, wherein step (c2) comprises: Obtaining a statistic representing a roughness value distribution of each pixel in the target area; And And determining a statistical value representing a roughness calculated from the statistics representing the distribution of roughness values as a representative roughness value; quantitatively measuring roughness of a living organ from an ultrasound image. 4. The method of claim 3, wherein the statistic is a matrix value representing roughness. The method of claim 4, wherein the matrix value is a cooccurrence matrix representing roughness. 6. The method of claim 2, wherein step (c3), (c31) obtaining a representative roughness value of the comparison area from the distribution of roughness values of the pixels in the comparison area; And (c32) quantifying a representative roughness value of the target area by using the representative roughness values of the comparison area and the target area. The method according to claim 6, wherein the step (c32) quantifies the roughness of the living organs from the ultrasound image quantitatively by quantifying the representative roughness value of the target area by a value relative to the representative roughness value of the comparison area. How to measure. 7. The coarseness of the living body according to claim 6, wherein the step (c32) quantifies the representative roughness value of the target area by linear combination of a numerical value relative to the representative roughness value of the comparison area. How to quantitatively measure The method of claim 1, wherein step (c) comprises: (c1) setting one or more target areas and the comparison area, respectively, including target areas already set in the ultrasound area image; (c2) obtaining distribution patterns of quantified representative roughness values for each of the set one or more target areas and the comparison area; And (c3) extracting a quantified representative roughness value of the ultrasound region image from the distribution pattern; quantitatively measuring the coarseness of the living organ from the ultrasound image. 10. The method of claim 9, wherein extracting the quantified representative roughness value is a value obtained by calculating an average of representative roughness values of the distribution pattern. . 10. The method of claim 9, wherein extracting the quantified representative roughness value is calculated by calculating a median of representative roughness values of the distribution pattern. . The method of claim 1, wherein step (d) (d1) collecting samples of the ultrasound region image; (d2) obtaining quantified representative roughness values and associated roughness readings of the samples from each of the samples; (d3) obtaining a proportional function between the quantified representative roughness values and the coarseness reading; And (d4) a method of calculating the coarseness corresponding to the quantified representative roughness value of the target region by using the proportional function; and quantitatively measuring the coarseness of the living organs from the ultrasound image. An apparatus for quantitatively measuring the coarseness of a living organ, An ultrasound imaging unit configured to photograph an organ and generate an image of the organ; An area setting unit for setting at least one target area and a comparison area with respect to the generated image; A memory unit for storing the generated image or the target area and the comparison area as data by the target area setting unit; A control calculator configured to calculate a representative roughness value from roughness values of brightness distribution patterns of the image data stored in the memory unit, and to generate a roughness by quantifying the representative roughness value; And And a display unit configured to display a result of the calculation and generation of the control operation unit or to display an image of the biological organ. The method of claim 13, wherein the control operation unit A roughness value distribution pattern extracting unit obtaining a distribution pattern of quantified representative roughness values for each of the one or more set target areas and the comparison area; And Representative roughness value extraction unit for extracting the representative roughness value quantified from the distribution pattern of the roughness values extracted by the roughness value distribution pattern extraction unit; Apparatus for quantitatively measuring the coarseness of the living organs comprising a . The method of claim 13, wherein the control operation unit A brightness distribution pattern extracting unit obtaining a brightness distribution pattern of the target area; A roughness value extraction unit for extracting a representative roughness value from roughness values of the brightness distribution patterns obtained by the brightness distribution pattern extraction unit; And Representative roughness value quantifier for quantifying the representative roughness value extracted by the roughness value extractor; Apparatus for quantitatively measuring the roughness of the living organs comprising a. The method of claim 14 or 15, wherein the control operation unit An image sampling unit collecting the samples of the generated image; A representative roughness value extracting unit obtaining quantified representative roughness values of the samples from the samples of the image collected by the image sampling unit; And A coarseness reading unit for obtaining coarseness readings related to the quantified representative roughness values of the samples from the samples of the image collected by the image collecting unit; Device for The roughness extracting unit of claim 15, wherein the roughness value extracting unit includes a pixel statistics unit for obtaining a statistic representing a distribution of roughness values of each pixel of the target area, and the representative roughness represents a statistical value indicating roughness calculated and output from the pixel statistics unit. A device for quantitatively measuring the coarseness of a living organ, characterized by the value. 18. The apparatus of claim 17, wherein the pixel statistics unit can calculate a matrix value. The method of claim 15, wherein the area setting unit A target area setting unit for setting at least one target area; And A comparison area setting unit for setting one or more comparison areas; The representative roughness value quantification unit A comparison area representative roughness value extracting unit which obtains a comparison area representative roughness value from the distribution of roughness values of the pixels in the comparison area by the comparison area setting unit; And And a roughness value comparison quantifying unit for quantifying a representative roughness value by comparing the representative roughness value obtained by the roughness value extracting unit and the comparison region representative roughness value obtained by the comparison region representative roughness value extracting unit. An apparatus for quantitatively measuring the coarseness of a living organ.

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