JP6535097B2 - Ultrasonic tissue detection device, ultrasonic tissue detection method, and ultrasonic tissue detection program - Google Patents

Description

  The present invention relates to a technique for detecting a measurement target site such as muscle tissue from an echo image captured by transmitting and receiving ultrasonic waves into the body such as the abdomen of a human body.

  In various medical diagnoses, an internal structure such as the abdomen may be imaged using an ultrasonic tissue detection apparatus. The ultrasonic tissue detection apparatus transmits ultrasonic waves from the surface of the human body into the body, receives ultrasonic waves reflected in the body, and generates an echo image obtained by imaging the body.

  The abdomen of the human body has a structure in which a plurality of tissues overlap in order from the body surface to the inside of the body, such as epidermis, subcutaneous tissue, fat tissue, muscle tissue, and viscera. In addition, tissues such as blood vessels and diaphragms also exist inside each tissue. Therefore, in the ultrasonic tissue detection apparatus, the ultrasonic waves transmitted into the body are reflected at the boundaries of these tissues, and an echo image is obtained in which a part of the boundaries of various tissues is captured as a linear or point-like image.

  Usually, the operator of the ultrasonic tissue detection apparatus visually recognizes a plurality of images on the echo image to estimate the boundary located on the body surface side of the internal tissue to be measured and the boundary located on the inner side of the body. Then, the operator operates the positions of the two cursors and the like displayed on the operation screen, and aligns the positions with the boundaries of the internal tissue on the echo image. Thereby, the space between the cursors on the operation screen corresponds to the thickness of the internal tissue. Therefore, the operator grasps the thickness of the internal tissue by reading a scale or a numerical value indicating the thickness of the internal tissue on the operation screen.

  However, in the conventional method in which the boundary of the internal tissue is determined visually by the operator, the accuracy may vary depending on the operator's level of learning, and the labor and time required for the operation may be a burden to the operator. Therefore, various techniques have been proposed for automatically detecting the boundary of the internal tissue from an echo image obtained by imaging the internal tissue (see, for example, Patent Documents 1 and 2).

  The technique disclosed in Patent Document 1 detects the boundary of internal tissue from various images on an echo image obtained by ultrasound based on the length of the image and the direction in which the image extends. The technique disclosed in Patent Document 2 searches and tracks a linear image from the vicinity of a position on an echo image specified by an operator, and detects the linear image as a boundary of internal tissue.

Patent 4464152 JP, 2004-181240, A

  However, none of the conventional techniques described above detect boundaries regardless of the type of internal tissue, and do not detect a specific internal tissue such as muscle tissue or a boundary of the internal tissue. Therefore, even if the prior art is used, it is necessary for the operator to visually judge the target internal tissue boundary, automatically detecting and displaying a specific internal tissue or its boundary, or a specific internal tissue It was not possible to automatically measure the thickness of.

  Therefore, an object of the present invention is to provide an ultrasonic tissue detection apparatus that automatically detects a specific internal tissue or its boundary with a certain precision or more from an echo image of a subject such as the abdomen or the like imaged using ultrasonic waves. It is to provide.

  An ultrasonic tissue detection apparatus according to the present invention comprises an image acquisition unit for acquiring an echo image based on an echo from the body of an ultrasonic wave transmitted into the body from the surface of a subject including a measurement target site; A transverse image detection unit that detects a plurality of transverse images that cross in a direction that intersects the transmission direction of the ultrasonic wave in the echo image acquired by the unit; and the plurality of traverses detected by the transverse image detection unit Of the images, two cross-sectional images corresponding to the measurement target region of the subject are selected based on the feature amount of the cross-sectional image, and the selected two cross images are used as the boundaries of the measurement target region. And a boundary estimation unit.

  According to this configuration, of the plurality of cross-sectional images detected by the cross-sectional image detection unit, the boundary estimation unit selects two cross-sectional images corresponding to the measurement target region of the object based on the feature amount of the cross-sectional image. Therefore, each boundary of the measurement target site can be automatically detected according to a specific measurement target site (for example, an internal tissue including muscle tissue).

  According to the present invention, it is possible to automatically detect the boundary of a specific internal tissue from an echo image of a subject such as the abdomen or the like imaged using ultrasonic waves with a certain accuracy or more.

FIG. 1 is a block diagram of an ultrasonic tissue detection apparatus according to an embodiment of the present invention. FIG. 2 is a diagram showing a processing flow of the ultrasonic tissue detection apparatus according to the embodiment of the present invention. FIG. 3 is a view exemplifying a cross-sectional structure of an abdomen corresponding to an imaging target and an echo image obtained by imaging the abdomen. FIG. 4 is a diagram showing a processing flow of the transverse image detection processing according to the embodiment of the present invention. FIG. 5 is a view exemplifying an image converted by the transverse image detection process according to the embodiment of the present invention. FIG. 6 is a diagram showing a processing flow of boundary detection processing according to the embodiment of the present invention. FIG. 7 is a view schematically showing the image of FIG. FIG. 8 is a diagram showing an example of processing by the boundary processing unit according to the embodiment of the present invention.

  An ultrasonic tissue detection apparatus, an ultrasonic tissue detection method, and an ultrasonic tissue detection program according to a first embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram of an ultrasonic tissue detection apparatus according to a first embodiment of the present invention.

  An ultrasonic tissue detection apparatus 1 shown in FIG. 1 includes a probe 2 and an image processing apparatus 11. The probe 2 has, for example, a substantially columnar shape, and is configured to be grasped and moved by an operator. A cable is connected to the upper end of the probe 2, and the probe 2 is connected to the interface 10 of the image processing apparatus 11 via the cable.

  The probe 2 receives a transmission signal from the image processing apparatus 11. The lower end surface of the probe 2 is configured as a transmission / reception surface of ultrasonic waves, and when the transmission signal is input, the ultrasonic waves are transmitted from the lower end surface. Therefore, in the state where the lower end surface of the probe 2 is pressed against the subject (in this embodiment, the abdomen of the human body) 101 by the operator, the transmission signal is input from the image processing apparatus 11 to Ultrasonic waves are transmitted toward the inside of the abdomen 101. Further, the probe 2 receives the echo of the ultrasonic wave reflected in the body of the abdomen 101, and outputs a reception signal according to the reception level of the ultrasonic wave to the image processing device 11.

  The image processing apparatus 11 includes a transmission / reception processing unit 3, an image display unit 8, a control unit 9, and an interface (I / F) 10. The control unit 9 includes an image acquisition unit 4, a transverse image detection unit 5, a boundary estimation unit 6, and a boundary processing unit 7. The control unit 9 is configured of a CPU (computer) and a storage unit. The image acquisition unit 4, the transverse image detection unit 5, the boundary estimation unit 6, and the boundary processing unit 7 are executed as software by causing the CPU to execute an ultrasonic tissue detection program installed in the storage unit. FIG. 2 is a flowchart illustrating the general processing flow of the image processing apparatus 11.

  The transmission / reception processing unit 3 generates a transmission signal obtained by shaping a signal having a frequency in the ultrasonic region into a pulse waveform, and outputs the transmission signal to the probe 2 via the interface 10 (FIG. 2: S101). As a result, the probe 2 is driven, and ultrasonic waves are transmitted from the probe 2 to the abdomen 101. Further, the transmission / reception processing unit 3 receives the received signal output from the probe 2 and performs processing such as analog-to-digital conversion on the received signal (FIG. 2: S102). The transmission / reception processing unit 3 performs these processing flows at predetermined time intervals, repeatedly outputs a transmission signal, and repeatedly receives an input of a reception signal.

  The image acquisition unit 4 receives a reception signal that the transmission / reception processing unit 3 has performed processing such as analog-to-digital conversion. The image acquisition unit 4 generates a first image (echo image) 21 obtained by imaging an echo in the body of the abdomen 101 based on the received signal received (FIG. 2: S103, image acquisition step). The first image 21 is obtained by setting the luminance corresponding to the received signal strength of the echo in the pixel corresponding to the position where the echo received by the probe 2 is reflected by the abdomen 101. FIG. 3A is a view showing a schematic structure of the abdomen 101. FIG. 3B is a diagram illustrating a first image 21 obtained from the abdomen 101.

  As shown in FIG. 3A, the abdomen 101 has a structure in which the epidermis, the subcutaneous tissue, the fat tissue, the muscle tissue, and the viscera are arranged in order from the body surface side to the inside of the body. Such ultrasound transmitted from the probe 2 to the abdomen 101 is reflected at the epidermis, the subcutaneous tissue, the boundary between the subcutaneous tissue and the fat tissue, the boundary between the fat tissue and the muscle tissue, the boundary between the muscle tissue and the viscera, etc. Do. For this reason, in the first image 21 shown in FIG. 3 (B), a plurality of linear images of high brightness (white display) appear at the position where the ultrasonic wave is reflected from the epidermis side to the inside of the body. . In addition, a diaphragm, a blood vessel, etc. exist inside the various tissues mentioned above, and since an ultrasonic wave is reflected also by them, in addition to the image corresponding to the above-mentioned boundary on the 1st image 21, There are also images of shorter linear and dotted images.

  The transverse image detection unit 5 shown in FIG. 1 performs image conversion processing etc. on such a first image 21 to detect a plurality of transverse images appearing on the first image 21 (FIG. 2: S104, Transverse image detection step). Here, the cross-sectional image is a direction intersecting the transmission direction of the ultrasonic waves, that is, the direction from the epidermis to the inside of the body (the lower direction in the drawing) on the first image 21 shown in FIG. It is defined as a line-like image that extends to cross the first image 21. In addition, the boundary estimation unit 6 compares each of the plurality of cross-sectional images detected by the cross-sectional image detection unit 5 and estimates from among them the one corresponding to the boundary of any internal tissue (FIG. 2: S105, Boundary estimation step). More specifically, the boundary estimation unit 6 determines the measurement target portion (the present embodiment) of the object based on the feature amount (for example, the positional relationship as described below, the intensity of the echo, etc., described below) of each transverse image. Then, select the two cross-sectional images corresponding to the internal tissue (including internal tissue) as the selected two cross-sectional images as the boundaries of the internal tissue. In addition, the boundary processing unit 7 highlights the cross-sectional image or displays the interval based on the cross-sectional image estimated by the boundary estimation unit 6 as the boundary of the internal tissue, that is, a predetermined display such as highlighting or thickness display of some internal tissue. A process is performed (FIG. 2: S106, predetermined processing step). Further, the image display unit 8 includes, for example, a display for displaying an echo image or the like, and performs highlighting display and thickness display of a cross-sectional image which has been subjected to predetermined processing by the boundary processing unit 7.

  By such processing, the ultrasonic tissue detection apparatus 1 according to the present embodiment can automatically estimate the boundary of internal tissue such as muscle tissue from an image such as the abdomen imaged using ultrasonic waves. .

  Hereinafter, the specific processing flow of each of the transverse image detection unit 5, the boundary estimation unit 6, and the boundary processing unit 7 will be described using the case where the first image 21 shown in FIG. 3B is an example. Do. In addition, the process flow illustrated below is an illustration to the last, and an appropriate | suitable change and adjustment may be performed in an actual process flow.

  FIG. 4 is a diagram illustrating a detailed processing flow of image conversion processing in the cross-sectional image detection unit 5. FIG. 5 is a view exemplifying an image subjected to the image conversion processing in the cross-sectional image detection unit 5. FIG. 7 is a view schematically showing an image subjected to the image conversion processing in the cross-sectional image detection unit 5.

  The cross-sectional image detection unit 5 first divides the first image 21 into a second image 22 on the subcutaneous tissue side and a third image 23 on the muscle tissue side (FIG. 4: S111, FIG. 5 (A )reference). For example, the transverse image detection unit 5 first removes the extracorporeal portion from the first image 21. Then, from the first image 21 from which the extracorporeal portion has been removed, the cross-sectional image detection unit 5 adds the luminances of the pixel rows in the transverse direction, and generates a luminance profile in which the summed luminances are arranged in the depth direction. Then, in the luminance profile, among the positions where the luminance is minimized, the position closest to the epidermis is divided into the second image 22 on the subcutaneous tissue side and the third image 23 on the muscle tissue side as division positions.

  Next, the cross-sectional image detection unit 5 detects the first cross-sectional image 31 corresponding to the boundary of the subcutaneous tissue from the second image 22 on the subcutaneous tissue side (FIG. 4: S112, FIG. 5 (A), FIG. 7 (A)).

  Specifically, the cross-sectional image detection unit 5 first applies the Dijkstra method to the second image 22 on the subcutaneous tissue side, and detects the first cross-sectional image 31 with the strongest echo in this region. Although the details of the Dijkstra method are omitted, the outline of the Dijkstra method is three-dimensional in which the cost axis in which the luminance is converted to a lower luminance is added to the cost axis is added to the depth direction axis and the transverse axis of the image. It is a type of optimization algorithm that generates a cost map and searches for the shortest path with the lowest cost along the transverse axis in the cost map.

  In the second image 22 on the subcutaneous tissue side, there is a possibility that many unnecessary echoes may be present near the boundary of the subcutaneous tissue. Therefore, after the second image 22 on the subcutaneous tissue side is subjected to edge conversion processing, Dijkstra The law may be applied. Although details of the edge conversion processing are omitted, the edge conversion processing extracts an end portion in the depth direction of the region from the region where high luminance is distributed side by side in the depth direction (see FIG. 5B). It is a kind of processing algorithm. By detecting the transverse image from the image subjected to the edge conversion processing, it is possible to estimate the boundary of the internal tissue with high accuracy, excluding the influence of unnecessary echoes present around the transverse image.

  Next, the cross-sectional image detection unit 5 detects the second cross-sectional image 32 from the third image 23 on the muscle tissue side (see FIG. 4: S113, FIG. 5 (A), and FIG. 7 (A)). Again, the second echo image 32 with the strongest echo in this area is detected, for example using the Dijkstra method. Alternatively, the Dijkstra method may be applied after performing edge conversion processing on the third image 23 on the muscle tissue side.

  Next, the cross-sectional image detection unit 5 further divides the third image 23 on the muscle tissue side at the boundary of the second cross-sectional image 32 detected earlier, and then the fourth image 24 on the body surface side (FIG. 5). (C), FIG. 7 (B)) and the 5th image 25 (refer FIG. 5 (D), FIG. 7 (B)) of the body inside are produced | generated (FIG. 4: S114).

  Next, the cross-sectional image detection unit 5 detects the third cross-sectional image 33 from the fourth image 24 on the body surface side (see FIG. 4: S115, FIG. 5 (C), and FIG. 7 (B)). Here again, the third echo image 33 with the strongest echo in this area is detected, for example, using the Dijkstra method. Alternatively, the Dijkstra method may be applied after performing edge conversion processing on the fourth image 24 on the body surface side.

  In addition, the cross-sectional image detection unit 5 detects the fourth cross-sectional image 34 from the fifth image 25 inside the body (see FIG. 4: S116, FIG. 5 (D), and FIG. 7 (B)). Again, the fourth echo 34 with the strongest echo in this area is detected, for example, using the Dijkstra method. Alternatively, the Dijkstra method may be applied after performing edge conversion processing on the fifth image 25 inside the body.

  According to the above processing flow, the cross-sectional image detection unit 5 detects the first to fourth cross-sectional images 31 to 34. Of the first to fourth transverse images 31 to 34, the first transverse image 31 is a transverse image corresponding to the boundary of the subcutaneous tissue. Then, any two of the first to fourth transverse images 31 to 34 are transverse images corresponding to the boundary on the body surface side of the muscle tissue and the boundary on the inside of the body of the muscle tissue. Therefore, from the first to fourth cross-sectional images 31 to 34 detected by the cross-sectional image detection unit 5, respective cross-sectional images corresponding to two boundaries of muscle tissue are estimated as follows.

  FIG. 6 is a diagram illustrating a detailed processing flow of the boundary estimation process in the boundary estimation unit 6.

  The boundary estimation unit 6 compares each of the four cross-sectional images detected by the cross-sectional image detection unit 5 and performs a process of estimating which one of the internal tissue boundaries these correspond to.

  Specifically, first, the boundary estimation unit 6 first detects the second transverse image 32 (see FIGS. 5A, 7A, and 7B) previously detected by the transverse image detection unit 5; Consider the first boundary of muscle tissue (FIG. 6: S121). This is because, in the case where there is no bone tissue within the range where ultrasonic waves are transmitted and received, as in the abdomen 101, usually, the region inside the body in the first image 21 than the subcutaneous tissue, This is because, in the image 23 of 3 (see FIG. 5A and FIG. 7A), the echo is maximized at the boundary on the body surface side of the muscle tissue or at the boundary inside the body.

  Next, the boundary estimation unit 6 sets the third crosswise image 33 (see FIGS. 5C and 7B) and the fourth crosswise image 34 (see FIGS. 5D and 7B). The first feature quantity is detected from each of the above (FIG. 6: S122). The first feature quantity relates, for example, to the echo intensity in the cross-sectional image. The boundary estimation unit 6 may calculate the value of the sum or average of the pixels on the cross-sectional images 33 and 34 as a first feature amount related to the echo intensity. At this time, it is preferable to obtain the above value by extracting a pixel of about 70% of the width of the image because an unclear echo is likely to appear near the end of the image. This makes it possible to detect the echo intensity with higher accuracy. Note that the above processing may be repeated while moving the position at which a pixel of about 70% of the width of the image is extracted in the width direction of the image.

  Next, the boundary estimation unit 6 detects a second feature value from each of the third cross image 33 and the fourth cross image 34 (FIG. 6: S123). The second feature value relates, for example, to the height of linearity in the cross-sectional image. The boundary estimation unit 6 obtains an approximate straight line along each of the cross images 33 and 34, and the value of the reciprocal of the sum of squares of the deviation (error) of each pixel on the cross images 33 and 34 with respect to those approximate straight lines calculate. Also in this case, it is preferable to obtain the above value by extracting a pixel of about 70% of the width of the image because an unclear echo is likely to appear near the edge of the image. Thereby, the height of linearity can be detected with higher accuracy. Note that the above processing may be repeated while moving the position at which a pixel of about 70% of the width of the image is extracted in the width direction of the image.

  Next, the boundary estimation unit 6 evaluates the first and second feature quantities obtained for the third and fourth cross-sectional images 33 and 34 as terms. Using the function, an evaluation point for the third transverse image 33 and an evaluation point for the fourth transverse image 34 are calculated (FIG. 6: S124).

  Next, when the fourth transverse image 34 has a higher evaluation point than the third transverse image 33, the boundary estimation unit 6 regards the fourth transverse image 34 as the second boundary of the muscle tissue (see FIG. 6). Figure 6: S125).

  On the other hand, when the third crosswise image 33 has a higher evaluation point than the fourth crosswise image 34, the boundary estimation unit 6 generates the third crosswise image 33 and the first crosswise image 31 (FIG. 5 (A And FIG. 7 (B)), detection and determination of the second boundary of the muscle tissue is performed. This is because the thickness of the fat layer on the body surface of the abdomen 101 is much larger than that of the individual on the body side, and sometimes there is almost no fat tissue between the subcutaneous tissue and the muscle tissue. This is because the cross-sectional image 31 of 1 may almost coincide with the second boundary of the muscle tissue.

  Specifically, the boundary estimation unit 6 first detects a third feature amount from the first cross image 31 and the third cross image 33 (FIG. 6: S126). For example, the third feature value relates to the distance between the first transverse image 31 and the third transverse image 33. Then, the boundary estimation unit 6 determines that the third cross image 33 is present if the distance is smaller than the threshold, that is, if the distance between the first cross image 31 and the third cross image 33 is extremely narrow. The first cross-sectional image 31 is regarded as the second boundary of the muscle tissue (FIG. 6: S127) because there is a high risk of being a cross-sectional image obtained by copying the unintended boundary.

  On the other hand, when the distance between the first transverse image 31 and the third transverse image 33 is larger than the threshold value, the boundary estimation unit 6 sets the first transverse image 31 and the third transverse image 33 The four feature quantities are detected (FIG. 6: S128). The fourth feature amount relates to, for example, the echo intensity in the cross-sectional image.

  Then, the boundary estimation unit 6 regards one of the first transverse image 31 and the third transverse image 33 with the larger fourth feature value as the second boundary of the muscle tissue (FIG. 6: S129). ).

  According to the above processing flow, the boundary estimation unit 6 regards the second transverse image 32 as the first boundary of the muscle tissue, and, among the first, third or fourth transverse images 31, 33, 34. Consider one of the second boundaries of muscle tissue.

  After these processes, the boundary processing unit 7 processes highlighting, thickness, etc. of the cross-sectional image determined to be the first boundary of the muscle tissue by the boundary estimation unit 6 and the cross-sectional image determined to be the second boundary. Perform predetermined processing such as measurement or display of In addition, since the portion excluding the subcutaneous tissue and the muscle tissue corresponds to the fat tissue, the boundary processing unit 7 performs predetermined processing such as highlighting of the fat tissue, measurement or display of the thickness of the fat tissue, etc. You may

  For example, as shown in FIG. 8 (A), when the fourth transverse image 34 is regarded as the second boundary, the boundary processing unit 7 combines the second transverse image 32 and the fourth transverse image 34. The portion shown is visually displayed on the image display unit 8 as muscle tissue. Further, as shown in FIG. 8B, when the third transverse image 33 is regarded as the second boundary, the boundary processing unit 7 determines whether the second transverse image 32 and the third transverse image 33 are used. The portion shown is visually displayed on the image display unit 8 as muscle tissue. Furthermore, as shown in FIG. 8C, in the case of measuring and displaying the thickness and the like of the internal tissue such as muscle tissue, the boundary processing unit 7 measures bars, which indicate the respective transverse images 31 to 34. A display object such as an arrow or a number indicating a measurement range or a distance between images is displayed on the image display unit 8. As a result, the operator can easily grasp visually the condition of each internal tissue via the image display unit 8.

  By the above processing, the ultrasonic tissue detection apparatus 1 measures a location where no bone tissue is detected, such as the abdomen 101, using ultrasonic waves, and a plurality of cross-sectional images 31 detected by the cross-sectional image detection unit 5 Since the two cross images corresponding to the measurement target region of the subject are selected by the boundary estimation unit 6 based on the feature amounts of the cross images 31 to 34 among 34 to 34, a specific measurement target region (for example, muscle tissue) The boundary of the measurement target region can be automatically detected in accordance with the internal tissues such as fat tissue and the like. Therefore, even when an operator with a low skill level performs measurement, it is possible to detect the thickness of muscle tissue or the like with a certain accuracy or more.

  In the above embodiment, although the Dijkstra method is used to detect the cross image from the echo image, other known shortest path search algorithm or approximation algorithm is used to detect the cross image from the echo image. May be used. Similarly, various known image processing algorithms may be used for image processing algorithms such as edge detection processing.

  In the above embodiment, an example of boundary estimation processing of internal tissue including abdominal muscle tissue as a measurement target region is shown, but the same boundary estimation processing is performed on other tissues of other regions. It goes without saying that you can.

  Finally, the description of the above embodiments is to be considered in all respects as illustrative and not restrictive. The scope of the present invention is indicated not by the embodiments described above but by the claims. Further, the scope of the present invention is intended to include all modifications within the scope and meaning equivalent to the claims.

DESCRIPTION OF SYMBOLS 1 ... Ultrasonic tissue detection apparatus 2 ... Probe 3 ... Transmission / reception processing part 4 ... Image acquisition part 5 ... Crossing image detection part 6 ... Boundary estimation part 7 ... Boundary processing part 8 ... Image display part 9 ... Control part 10 ... Interface 11 ... Image processing apparatuses 21, 22, 23, 24, 25 ... first to fifth images 31, 32, 33, 34 ... first to fourth cross-sectional images 101 ... subject (abdomen)

Claims (10)

An image acquisition unit for acquiring an echo image based on an echo from the body of an ultrasonic wave transmitted into the body from the surface of a subject including a measurement target site including muscle tissue ;
A cross-sectional image detection unit that detects a plurality of cross-sectional images that cross in a direction intersecting the transmission direction of the ultrasonic wave in the echo image acquired by the image acquisition unit;
Of the plurality of cross-sectional images detected by the cross-sectional image detection unit, two cross-sectional images corresponding to the measurement target region of the subject are selected based on the feature amount of the cross-sectional image, and the selected two A boundary estimation unit that sets one transverse image to each boundary of the measurement target region;
Equipped with
The transverse image detection unit
Detecting a first transverse image on the body surface side of the transverse images in the echo image;
Detecting a second cross-sectional image having the strongest echo in a region inside the body of the cross-sectional image in the echo image than the first cross-sectional image,
In the echo image, a third cross-sectional image having the strongest echo is detected among the cross-sectional images that cross the region inside the body than the first cross-sectional image and the body surface side of the second cross-sectional image,
Among the transverse images in the echo image which cross the region inside the body than the second transverse image, a fourth transverse image having the strongest echo is detected
The boundary estimation unit
The second transverse image is used as a candidate for the first boundary of the measurement target region, and one of the first, third, and fourth transverse images is selected. As candidates for the second boundary of the measurement target site based on feature quantities, candidates for the two transverse images are selected.
Ultrasonic tissue detector. The ultrasonic tissue detection apparatus according to claim 1, wherein
The boundary estimation unit selects two cross-sectional images corresponding to the measurement target region of the subject based on at least the positions of the respective cross-sectional images or the echo intensities of the respective cross-sectional images.
Ultrasonic tissue detector. The ultrasonic tissue detection apparatus according to claim 1 , wherein
The boundary estimation unit selects, as a candidate of the second boundary, one of the third and fourth cross-sectional images in which the echo in the cross-sectional image is stronger.
Ultrasonic tissue detector. The ultrasonic tissue detection apparatus according to claim 1 , wherein
The boundary estimation unit selects, as a candidate of the second boundary, one of the third transverse image and the fourth transverse image that is higher in linearity in the transverse image.
Ultrasonic tissue detector. The ultrasonic tissue detection apparatus according to claim 1 , wherein
The boundary estimation unit is configured such that, among the third and fourth cross-sectional images, evaluation points by an evaluation function having terms of the echo intensity in the cross-sectional image and the height of linearity in the cross-sectional image are Select the larger one as a candidate for the second boundary,
Ultrasonic tissue detector. An ultrasonic tissue detecting device according to any one of claims 1 to 5,
The boundary estimation unit selects a first transverse image as a candidate of the second boundary, when the distance interval between the third transverse image and the first transverse image is narrower than a threshold.
Ultrasonic tissue detector. An ultrasonic tissue detecting device according to any one of claims 1 to 5,
The boundary estimation unit is configured to, when the distance between the third transverse image and the first transverse image is smaller than a threshold, select one of the third transverse image and the first transverse image. Selecting the one with a stronger echo at the second boundary as a candidate for the second boundary,
Ultrasonic tissue detector. The ultrasonic tissue detection apparatus according to any one of claims 1 to 7 , wherein
A boundary processing unit that performs predetermined processing on each of the boundaries selected by the boundary estimation unit;
Ultrasonic tissue detector. Acquiring an echo image based on an echo from the body of an ultrasonic wave transmitted from the surface of the subject including the measurement target site including the muscle tissue into the body;
In the echo image acquired in the image acquisition step, a plurality of transverse images crossing in a direction intersecting with the transmission direction of the ultrasonic wave is detected, and among the transverse images in the echo image, the first one on the body surface side And detecting a second cross-sectional image having the strongest echo in a region inside the body than the first cross-sectional image among the cross-sectional images in the echo image, and Detecting a third cross-sectional image having the strongest echo among the cross-sectional images that cross the region inside the body than the first cross-sectional image and the body surface side of the second cross-sectional image; A transverse image detection step of detecting a fourth transverse image having the strongest echo among transverse images traversing an area inside the body rather than the second transverse image ;
Of the plurality of transverse images detected in the transverse image detecting step, the second transverse image is used as a candidate for the first boundary of the measurement target region, and the first transverse image and the third transverse image Estimating one of the second and fourth cross-sectional images as candidates for the second boundary of the measurement target region based on the feature quantities of the respective cross-sectional images ;
Ultrasonic tissue detection method to perform. Acquiring an echo image based on an echo from the body of an ultrasonic wave transmitted from the surface of the subject including the measurement target site including the muscle tissue into the body;
In the echo image acquired in the image acquisition step, a plurality of transverse images crossing in a direction intersecting with the transmission direction of the ultrasonic wave is detected, and among the transverse images in the echo image, the first one on the body surface side And detecting a second cross-sectional image having the strongest echo in a region inside the body than the first cross-sectional image among the cross-sectional images in the echo image, and Detecting a third cross-sectional image having the strongest echo among the cross-sectional images that cross the region inside the body than the first cross-sectional image and the body surface side of the second cross-sectional image; A transverse image detection step of detecting a fourth transverse image having the strongest echo among transverse images traversing an area inside the body rather than the second transverse image ;
Of the plurality of transverse images detected in the transverse image detecting step, the second transverse image is used as a candidate for the first boundary of the measurement target region, and the first transverse image and the third transverse image Estimating one of the second and fourth cross-sectional images as candidates for the second boundary of the measurement target region based on the feature quantities of the respective cross-sectional images ;
An ultrasonic tissue detection program that causes a computer to execute.

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