JP4202966B2 - Ultrasonic diagnostic equipment - Google Patents

Description

  The present invention relates to an ultrasonic diagnostic apparatus that receives an ultrasonic reflected wave transmitted into a living body and obtains an ultrasonic tomographic image based on the reflected wave.

  An ultrasonic diagnostic apparatus that receives an ultrasonic reflected wave transmitted into a living body and obtains an ultrasonic tomographic image based on the reflected wave is known, and is used for various examinations and diagnoses because the burden on the subject is small. Yes. One of such tests and diagnoses is liver function diagnosis. As the function of the liver decreases, the homogeneity of the liver parenchyma, the smoothness of the liver surface, the sharpness of the liver margin, and the clarity of the intravascular image of the liver are lost. The examiner obtains these states from a tomographic image of the liver and makes a diagnosis in consideration of the results of other examinations. Note that the liver margin corresponds to the lower edge portion of a human when the human is standing.

  Patent Document 1 below discloses a diagnosis of liver function based on a change in the liver parenchyma of an ultrasonic tomographic image.

Japanese Patent Laid-Open No. 2001-23884

  Patent Document 1 discloses the examination and diagnosis of liver function based on changes in the liver parenchyma, but the diagnosis of liver function includes the uniformity of the liver parenchyma as described above, the smoothness of the liver surface, Considering the sharpness of the liver margin and the clarity of intravascular images, the examiner makes a diagnosis based on the experience. Patent Document 1 does not disclose any observation object other than the uniformity of the liver parenchyma.

  The present invention relates to an examination and diagnosis of liver function, and provides an ultrasonic diagnostic apparatus that supports evaluation by performing evaluations on characteristic items different from Patent Document 1.

  The ultrasonic diagnostic apparatus of the present invention includes a tomographic image acquisition unit that obtains an ultrasonic tomographic image of the liver, and an irregularity calculation unit that calculates a value indicating the degree of irregularities on the surface of the liver in the tomographic image. ing.

  The irregularity calculation means may calculate an approximate curve in a predetermined section of a contour line representing the liver surface, and calculate the degree of irregularity based on a difference between the approximate curve and the contour line.

  Further, the predetermined section of the contour line, which is a calculation target of the degree of irregularity, can be a partial section of the liver surface on the back side.

  In the image of the region including a part of the liver surface on the back side, the longest edge can be set as the predetermined section of the contour line.

  Further, in an image of a region including a part of the liver surface on the back side, an edge closest to the center of the liver among edges having a predetermined length or longer can be set as a predetermined section of the contour line.

  Further, the value indicating the degree of irregularity may be a value obtained by normalizing the area of the portion sandwiched between the approximate curve and the outline by dividing the length of the approximate curve.

  The approximate curve may be a quadratic curve.

  Further, it is possible to provide an image in which the approximate curve is drawn on the ultrasonic tomographic image and a color is given to a portion sandwiched between the approximate curve and the contour line.

  According to the present invention, an index for diagnosing liver function can be provided by quantifying the degree of irregularities on the surface of the liver.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a diagram showing a schematic appearance of a human liver 10. FIG. 1 is drawn in a direction when a human is standing, with the front side of the paper being the front of the human, the back of the paper being the back, and the top of the paper being the top. The portion of the lower edge of the liver (the edge indicated by reference numeral 12 in FIG. 1) is particularly called the liver margin. When obtaining an ultrasonic tomographic image, the image is obtained by bringing the ultrasonic probe into contact with the rib from below the rib slightly upward. In FIG. 1, a tomographic image is obtained by applying a probe from a slightly lower side toward the upper side on the front side of the drawing. A tomographic image taken along one-dot chain line 14 in FIG. 1 is shown in FIG. In FIG. 2, the position directly above is the position of the ultrasound probe, and therefore, the upper side of FIG. 2 is the front side surface of the human and the lower side is the back side surface. In addition, the aforementioned liver margin is the right end.

  The normal liver surface is smooth with no irregularities, but irregularities occur as the fibrosis that hardens and breaks down the hepatocytes progresses. The degree of irregularities of the irregularities correlates with the degree of fibrosis and indicates the degree of progression of symptoms. This unevenness also appears on the ultrasonic tomographic image, and in particular, the unevenness appears well on the back side, in the lower side in the image shown in FIG. In the image of FIG. 2, it can be seen that relatively large irregularities appear on the lower side. An approximate curve of a curve representing the predetermined section of this side, that is, the liver surface 16 on the back side is obtained, and a value representing the degree of unevenness is calculated based on a portion sandwiched between these curves. For example, a value obtained by dividing the area of the portion sandwiched between the curves by the length of the approximate curve can be used. Such a value makes it possible to estimate the progress of the liver lesion.

  Hereinafter, the method of digitizing the unevenness of the liver surface will be described in detail. FIG. 3 is a functional block diagram related to the quantification processing of the unevenness of the liver surface. The ultrasonic probe 20 is controlled by the transmission / reception unit 22 to transmit an ultrasonic wave into the living body, receives a reflected wave thereof, and converts it into a reception signal. The received signal is sent from the transmission / reception unit to the digital scan converter (DSC) 24, where an ultrasonic tomographic image is formed. An irregularity indicating the degree of irregularities on the surface of the liver is calculated by the irregularity calculation means 27 based on the ultrasonic tomographic image. The formed ultrasonic tomographic image is, for example, an image as shown in FIG. The region-of-interest setting unit 28 sets the region of interest 26 on the surface 16 on the back side where unevenness appears well in the tomographic image of the liver. It is preferable that the region of interest 26 is set so as to include a portion where unevenness due to a lesion appears well, and exclude a portion where it does not appear so much. As described above, in the ultrasonic tomographic image, since unevenness appears well on the surface on the back side, it is preferable to set the region of interest 26 here, and as shown in FIG. It is more preferable to set the portion slightly closer to the liver margin 12. The region of interest can be set by the examiner directly inputting the position and size of the region of interest 26 on the screen numerically or by inputting with a pointing device or the like. The examiner can display the displayed tomographic image. Look and adjust the area.

  Next, each process is performed to extract the surface. First, the median processing unit 30 performs median processing to eliminate noise. Specifically, a process is performed in which the central value of the luminance values of the 3 × 3 pixels is set to the luminance value of the central pixel. Thereby, a big peak etc. can be removed suddenly. Next, the binarization processing unit 32 binarizes the image. As the threshold value for binarization, a predetermined value may be used, or the examiner may arbitrarily set the threshold value while viewing the image, for example. The noise removing unit 34 removes noise generated in the binarization process.

  The edge detection unit 36 detects an edge, that is, a binary boundary line, from the binarized image from which noise has been removed. Edge detection can be performed using a known filter. In FIG. 4, the detected edges in the region of interest 26 are shown. Among the detected edges, the longest edge is selected as the surface 16 on the back side of the liver. The detection of the longest edge will be described with reference to a more detailed functional block diagram relating to the contour detection unit 38 shown in FIG. FIG. 6 is a diagram for explaining processing related to the edge detected by the edge detection unit 36, and illustrates how an image changes in each processing stage by giving an example of an edge.

  As shown in FIG. 5, all the edges detected by the edge detection unit 36 are stored in the edge frame memory 40 of the contour line detection unit 38. This state is shown in FIG. Next, the labeling processing unit 42 performs a labeling process to separate each edge. Then, the labeled state is stored in the label image frame memory 44. FIG. 6B shows a state where the labeling process has been performed. Next, the number detection unit 46 for each label detects the number of each label, and the contour line and end point coordinate detection unit 48 detects the most label edge, that is, the longest edge as the contour line of the liver cross section, The end point coordinates are detected. In FIG. 6B, the edge with the label “10” is the longest, and this edge is stored in the contour frame memory 50 as shown in FIG.

  When the contour line of the liver is detected, the rotation processing unit 52 rotates the contour line so that the Y coordinates of the two end points of the contour line are equal. FIG. 6D shows this state. Although the shape of the edge (contour line) in FIG. 6D seems to be different from the shape of the edge in FIG. 6C, the third pixel and the sixth pixel from the bottom in FIG. 6 are reflected in the shape in which the third and sixth pixels from the left protrude upward and downward in FIG. 6D, and the same image is projected. Can understand.

  The edge (contour line) subjected to the rotation processing is stored in the contour line graph memory 54. An example of this contour line is shown in FIG. 7A as a curve A (hereinafter referred to as contour line A). FIGS. 7B and 7C subsequent to this are views obtained by performing a predetermined process on the contour line A. FIG. The contour line A is subjected to median processing by the median processing unit 56 to remove a steep peak and store it in the contour line graph memory 58. An example of the contour line B subjected to the median process is shown in FIG. The contour line graph memory 58 may be the same as the previous contour line graph memory 54, and the contour line B after the median processing may be overwritten on the contour line A. Then, a quadratic curve C having an axis parallel to the Y-axis that approximates the median-processed contour line B is obtained by the least-square method in the approximate curve creation unit 60 and stored in the approximate curve graph memory 62. To do. This quadratic curve C is shown in FIG.

  Next, the difference (absolute value) of the Y coordinate between the contour line B and the quadratic curve C having the same X coordinate is integrated from one end of the contour line B to the other end. This integrated value corresponds to the area of the portion (the portion indicated by the slanted line in the figure) sandwiched between the contour line B and the quadratic curve C. A small integral value indicates that the contour line B is close to a quadratic curve that is an approximate curve and is smooth, and conversely, a large value indicates that the surface has large irregularities. Therefore, based on this integrated value, the properties of the liver surface can be evaluated. In addition, if the length of the cut outline A or B changes, the integration interval changes. Therefore, if this is normalized, it becomes easier to evaluate. Therefore, a normalization unit 66 is provided, and normalization is performed by dividing by the length of the contour line B or the quadratic curve C. This normalized value can be used as an irregularity indicating the degree of unevenness on the liver surface.

  Further, an image superimposed on the ultrasonic tomographic image shown in FIG. 2 is provided for the portion sandwiched between the secondary curve C and the contour line B. For example, as shown in FIG. 8, the portion sandwiched in the ultrasonic tomographic image is displayed on a display screen such as a CRT so that it can be distinguished from other portions. For example, since the ultrasonic tomographic image is a black and white image, it is possible to clearly show the unevenness by coloring this portion.

  In the embodiment described above, the contour line detection unit 38 extracts the longest edge as the contour line of the liver cross section, but other methods may be employed. For example, a predetermined number of relatively long edges can be selected in the order of a predetermined value or more or the length, and the most substantial edge of the liver can be used as the contour line. The liver parenchyma is a relatively homogeneous tissue, and long edges rarely appear here. Therefore, a relatively long edge and the one closest to the center of the liver can be estimated as a contour line representing the surface of the liver. In order to determine the edge on the substantial side, for example, the center of gravity of the image of each edge is obtained, and the distance between this center of gravity and the center of the ultrasonic image shown in FIG. Alternatively, the examiner may input a point as the center of the liver while looking at the ultrasonic tomographic image, and obtain it based on the center-of-gravity distance between the input center and the edge.

  Alternatively, a predetermined number of relatively long edges may be displayed on the screen in the order of the predetermined value or more, or the length may be selected by the examiner to determine the contour line. In this case, the contour line detection unit 38 receives an input from the examiner and sets an edge according to this input as a contour line.

  In the above-described embodiment, a quadratic curve is used as the approximate curve, but it is also possible to approximate with a higher order curve such as a third order. For example, as shown in FIG. 9A, when the region of interest 26 is set near the liver margin 12, it can be well approximated by a quadratic curve, but as shown in FIG. When the region of interest 26 ′ is set at a site relatively far from the edge 12, it is preferable to approximate with a cubic curve. For example, the examiner can input and set the order of the N-order curve.

  Further, the approximate curve may be obtained by performing smoothing processing and median processing on a relatively large window. For example, a smoothing process or a median process can be performed by setting a larger window with respect to the contour line B subjected to the median process illustrated in FIG. FIG. 10 shows a curve D obtained by performing a smoothing process by setting a window of 61 points before and after the curve B having 250 points. In this case, the approximate curve creation unit 60 is a circuit that executes a smoothing process or a median process. In the smoothing or median processing, the curve A shown in FIG.

  In the case of obtaining an approximate curve by such smoothing or the like, for example, the unevenness can be evaluated over the entire outline of the outline of the liver cross section. In FIG. 11, an approximate curve is obtained by smoothing the entire circumference of the liver cross section, and the portion sandwiched between the approximate curve and the outline of the liver is colored in the same manner as in FIG. It is a figure which shows the example of a display distinguished from the circumference | surroundings.

  FIG. 12 is a diagram illustrating an example in which the approximate curve creation unit 60 and the approximate curve graph memory 62 illustrated in FIG. 3 are replaced with other configurations. The same components as those in FIG. 3 are denoted by the same reference numerals and description thereof is omitted. This configuration example is an example in which the examiner can select a case where the approximate curve is an Nth-order curve and a case where the approximate curve is obtained by smoothing. Based on the contour line, the Nth-order curve creation unit 160A obtains an approximate curve with a curve of a predetermined order, and stores this in the Nth-order curve graph memory 162A. On the other hand, the graph smoothing unit 160B also smoothes the contour line to obtain an approximate curve, and stores it in the smoothing graph memory 162B. The examiner selects which of the two memories 162A and 162B uses the approximate curve, and the selection unit 163 sends one approximate curve to the difference addition unit 64 in accordance with this selection.

  In the above-described embodiment, the difference between the contour line and the approximate curve is used to evaluate the unevenness of the liver surface, but the difference value is squared in a positive or negative state (that is, before taking the absolute value), and this is expressed as a curve. You may make it integrate over a full length.

It is a figure which shows the external appearance of a human liver. It is a figure which shows the example of the ultrasonic tomographic image of a liver. It is a functional block diagram which concerns on the quantification process of the unevenness | corrugation of the surface of a liver. It is a figure which shows a mode that the edge part of the image was extracted from the ultrasonic tomographic image of FIG. It is a figure which shows a part of functional block diagram of FIG. 3 in detail. It is a figure for demonstrating the process of the outline detection part 38 and the rotation process part 52. FIG. It is a figure which shows an example of the outline of a liver cross section, and the approximate curve of this outline. It is a figure which shows the example which performed the display which shows an unevenness | corrugation clearly on the ultrasonic tomographic image of a liver. It is a figure which shows the example from which the suitable degree of an approximated curve changes with the difference of the region of interest set. It is a figure which shows an example of the approximated curve obtained by the smoothing of an outline. It is a figure which shows the example which performed the display which shows an unevenness | corrugation clearly over the perimeter of a liver cross section. It is a figure which shows the block which replaces a part of functional block diagram of FIG.

Explanation of symbols

  10 liver, 12 liver margin, 16 dorsal liver surface, 26 region of interest, 27 irregularity calculator.

Claims (6)

A tomographic image acquisition means for obtaining an ultrasonic tomographic image of the liver;
In the tomographic image, an imperfection degree calculating means for calculating an approximate curve in a predetermined section of a contour line representing the liver surface and calculating a value indicating the degree of irregularity of the liver surface based on a difference between the approximate curve and the contour line. When,
Have
The ultrasonic diagnostic apparatus according to claim 1, wherein the value indicating the degree of irregularity is a value obtained by dividing an area of a portion sandwiched between the approximate curve and the outline by a length of the approximate curve . A tomographic image acquisition means for obtaining an ultrasonic tomographic image of the liver;
In the tomographic image, an imperfection degree calculating means for calculating an approximate curve in a predetermined section of a contour line representing the liver surface and calculating a value indicating the degree of irregularity of the liver surface based on a difference between the approximate curve and the contour line. When,
Have
An ultrasonic diagnostic apparatus characterized by providing an image in which the approximate curve is drawn on the ultrasonic tomographic image and a color is given to a portion sandwiched between the approximate curve and an outline . The ultrasonic diagnostic apparatus according to claim 1, wherein the predetermined section of the contour that is a calculation target of the degree of irregularity is a partial section of the liver surface on the back side. .   The ultrasonic diagnostic apparatus according to claim 3, wherein a longest edge is set as a predetermined section of the contour line in an image of a region including a part of a liver surface on the back side.   The ultrasonic diagnostic apparatus according to claim 3, wherein, in an image of a region including a part of the liver surface on the back side, an edge closest to the center of the liver among edges having a predetermined length or longer is defined as a predetermined section of the contour line. An ultrasonic diagnostic apparatus. The ultrasonic diagnostic apparatus according to claim 1, wherein the approximate curve is a quadratic curve.

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