JP2018157961A - Ultrasonic image processing device and method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To accurately measure the length of a thighbone of a fetus on a tomographic image.SOLUTION: A thighbone image is extracted by pre-processing (binarization, labelling, region expansion, etc.) to a tomographic image (S12-S16). The thighbone image is divided into two so as to form a first partial image and a second partial image (S18). A first main axis and a second main axis are specified by analyzing the first partial image and the second partial image, and a first end point and a second end point are detected on the first main axis and the second main axis (S20 and S22). A distance between the first end point and the second end point is measured as the length of the thighbone (FL) (S24).SELECTED DRAWING: Figure 4

Description

  The present invention relates to an ultrasonic image processing apparatus, and more particularly to processing of a long bone image.

  Ultrasonic image processing apparatuses are used in the medical field. The ultrasonic image processing apparatus includes an ultrasonic diagnostic apparatus, an information processing apparatus, and the like. In the following, an ultrasonic diagnostic apparatus will be described.

  The ultrasonic diagnostic apparatus is an apparatus that forms an ultrasonic image based on a reception signal obtained by transmitting and receiving ultrasonic waves to a subject. In obstetrics, in order to examine the growth and health of the fetus, ultrasound is regularly performed on pregnant women. In ultrasonic inspection, a plurality of measurements are usually performed. It includes measurement of femur length (FL). The femur length is defined as the distance between two end points in the femur (see, for example, Patent Document 1).

JP, 2015-171476, A

  When the femoral length is manually measured on a tomographic image, the objectivity is reduced and complicated, and therefore, it is desired to realize an automatic measurement of the femoral length. In the automatic measurement, it is conceivable to obtain the bone axis from the entire femur by analyzing the femur image, search for two end points on the bone axis, and measure the distance between them as the femur length. However, according to such a method, it becomes easy to set the end point away from the end point that has been manually specified. Further, when the femur is curved or deformed, the femur length cannot be obtained correctly. This problem can also be pointed out when measuring a long bone (long bone) other than the femur of the fetus.

  An object of the present invention is to appropriately set two end points for a long bone image in measurement of the long bone image. Alternatively, an object of the present invention is to enable two end points to be automatically and appropriately set for a long bone image in which bending or deformation is recognized.

  (1) An ultrasonic image processing apparatus according to an embodiment includes preprocessing means for extracting a long bone image having a first bone end portion and a second bone end portion from a tomographic image, and a plurality of portions from the long bone image Means for generating an image, the generating means for generating at least a first partial image having the first bone end and a second partial image having the second bone end; and First analysis means for identifying a first principal axis for the first partial image by analysis and detecting a first end point as an edge of the first bone end portion on the first principal axis; and analysis of the second partial image And a second detecting means for identifying a second principal axis for the second partial image and detecting a second end point as an edge of the second bone end portion on the second principal axis. In the measurement, the first end point and the second end point are used.

  If a single principal axis for end point detection is specified based on the entire long bone image in which bending or deformation is recognized, and each end point is detected on the main axis, each end point is likely to be set at an inappropriate position. On the other hand, according to the said structure, a 1st partial image and a 2nd partial image are produced | generated from a long bone image, and they are processed separately. That is, the first main axis for detecting the first end point is specified based on the first partial image, and the second main axis for detecting the first end point is specified based on the second partial image. Thereby, the specific precision of a 1st endpoint and a 2nd endpoint can be raised.

  In the embodiment, the generating unit includes a dividing unit that generates the first partial image and the second partial image by dividing the long bone image. According to this configuration, it is possible to generate a plurality of partial images quickly and easily by a very simple method of division on the assumption that the long bone image is displayed in a generally lying state. Generally, when creating a long bone image, the position and posture of the probe are adjusted by an inspector so that the long bone image does not stand vertically. In such a case, the above configuration functions effectively. Although the long bone image may be physically divided, the long bone image may be logically divided.

  In the embodiment, the dividing unit generates the first partial image and the second partial image by dividing the long bone image into two. In that case, the dividing line may be determined so as to pass through the representative points (centroid point, intermediate point, inspector-designated coordinates, etc.) of the long bone image.

  In an embodiment, the dividing means cuts the long bone image parallel to the vertical axis of the display coordinate system. According to this configuration, the amount of calculation can be reduced at the time of division, and two partial images can be generated more easily.

  In the embodiment, the first main axis is an axis that passes through the first representative coordinates in the first partial image and represents the longitudinal direction of the first partial image, and the second main axis is the second main axis. It is an axis that passes through the second representative coordinates in the partial image and represents the longitudinal direction of the second partial image. The representative coordinates are, for example, barycentric coordinates. The direction of the main axis can be specified using various methods for analyzing the longitudinal direction of the object.

  In the embodiment, the preprocessing unit binarizes the original tomographic image using an initial threshold value to generate a binarized image as the tomographic image, and the binarization Extracting means for extracting a temporary long bone image from an image, and applying a region expansion process to the temporary long bone image after setting a threshold value lower than the initial threshold value, whereby the temporary long bone image Region expansion processing means for generating the long bone image from the image.

According to the above configuration, a temporary long-axis image is obtained by binarization processing using a relatively high initial threshold value, and becomes a processing target by region expansion processing based on threshold processing using a relatively low threshold value. A long bone image can be generated. When threshold processing is performed using a low threshold from the beginning, there is an increased possibility of extracting a portion other than the long bone image, but according to the above configuration, an initial region that is likely to be a long bone image is detected. As a starting point, it is possible to search for a long bone image constituent pixel having a relatively low luminance existing around the starting point.

  In the embodiment, the region expansion processing unit applies a plurality of region expansion processing to the temporary long bone image while gradually reducing the threshold value within a range lower than the initial threshold value. According to this configuration, the long bone image grows step by step. As a result, it is possible to reduce the possibility that a pixel group that does not constitute a long bone image becomes a connection target.

  (2) The ultrasonic image processing apparatus according to the embodiment extracts a femur image having a first bone end portion and a second bone end portion from a tomographic image representing a fetus, and divides the femoral image. And generating a first partial image having the first bone end and a second partial image having the second bone end, and analyzing the first partial image with respect to the first partial image. Identifying a first principal axis, detecting a first end point as an edge of the first bone end on the first principal axis, and analyzing the second partial image to determine a second principal axis for the second partial image Identifying and detecting a second end point as an edge of the second bone end on the second main axis, and measuring a distance between the first end point and the second end point as a femoral length; ,including.

  The image processing method can be realized as a hardware function or a software function. In the latter case, a program for executing the image processing method is installed in an ultrasonic image processing apparatus (ultrasonic diagnostic apparatus, information processing apparatus, etc.) via a portable storage medium or via a network. Is done. Although it is desirable that all the processes are automatically executed, a process that is executed semi-automatically based on an inspector's designation, selection, or the like may be included therein. Moreover, you may comprise so that each result calculated automatically can be suitably corrected by the examiner.

  According to the present invention, in measuring a long bone image, two end points can be appropriately set for the long bone image. Alternatively, according to the present invention, two end points can be set automatically and appropriately even for a long bone image in which bending or deformation is recognized, so that the objectivity of measurement can be improved and the burden on the examiner is reduced. Multi-faceted advantages such as being able to improve measurement accuracy.

It is a block diagram which shows the structure of the ultrasonic diagnosing device which concerns on embodiment. It is a figure which shows the measurement of the femur image of a fetus. It is a figure which shows the tomographic image containing the femur image of a fetus. It is a flowchart which shows the ultrasonic image processing method which concerns on embodiment. It is a figure which shows the dividing line set with respect to the femur image. It is a figure which shows the process of a 1st partial image and a 2nd partial image. It is a figure which shows the measurement which concerns on embodiment, and the measurement which concerns on a comparative example. It is a figure which shows the modification of an endpoint detection method. It is a figure which shows the 1st example of a processing area setting method. FIG. 10 is a diagram showing endpoint detection in the first example shown in FIG. 9. It is a figure which shows the 2nd example of the processing area setting method. It is a figure which shows the 3rd example of a process area setting method. It is a flowchart which shows the specific example of pre-processing. It is a figure which shows an example of an area | region expansion process.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

  FIG. 1 is a block diagram showing the configuration of the ultrasonic diagnostic apparatus according to the embodiment. This ultrasonic diagnostic apparatus functions as an ultrasonic image processing apparatus. Specifically, the ultrasonic diagnostic apparatus is installed in a medical institution such as a hospital and forms an ultrasonic image by transmitting and receiving ultrasonic waves to a subject. It is. In the present embodiment, ultrasonic waves are transmitted / received to a pregnant woman, thereby forming a tomographic image including a femur image of a fetus.

  In FIG. 1, a probe 10 is composed of a probe head, a cable, and a connector. The connector is detachably attached to the ultrasonic diagnostic apparatus main body. A probe head is contact | abutted on the abdominal surface of a pregnant woman, for example. The probe head has an array transducer composed of a plurality of vibration elements arranged one-dimensionally. An ultrasonic beam BE is formed by the array transducer and electronically scanned. As an electronic scanning method, an electronic sector scanning method, an electronic linear method, and the like are known. A beam scanning surface S is formed by electronic scanning. In FIG. 1, r indicates the depth direction, and φ indicates the electronic scanning direction. Instead of the 1D array transducer, a 2D array transducer may be provided to obtain volume data from a three-dimensional space in the living body. An intracorporeal probe can also be used.

  The transmission / reception circuit 12 is an electronic circuit that functions as a transmission beam former and a reception beam former. During transmission, a plurality of transmission signals are supplied in parallel from the transmission / reception circuit 12 to the array transducer. As a result, a transmission beam is formed. At the time of reception, the reflected wave from the living body is received by the array transducer. As a result, a plurality of reception signals are output in parallel from the array transducer to the transmission / reception circuit 12. The transmission / reception circuit 12 includes a plurality of amplifiers, a plurality of A / D converters, a plurality of delay circuits, an addition circuit, and the like. In the transmission / reception circuit 12, a plurality of reception signals are subjected to phasing addition (delay addition) to form beam data corresponding to the reception beam. Received frame data is composed of a plurality of beam data arranged in the electronic scanning direction. Each beam data is composed of a plurality of echo data arranged in the depth direction.

  The tomographic image forming unit 14 is an electronic circuit that generates tomographic image data based on received frame data. The electronic circuit includes one or more processors. The tomographic image forming unit 14 includes, for example, a detection circuit, a logarithmic conversion circuit, a frame correlation circuit, a digital scan converter (DSC), and the like. The tomographic image data is sent to the image processing unit 20 and the display processing unit 30.

  The image processing unit 20 functions as an image processing unit, and is a module that applies image processing to an ultrasonic image (a tomographic image including a femur image of a fetus in the embodiment). The image processing unit 20 is configured by an electronic circuit including one or a plurality of processors. In FIG. 1, a plurality of functions of the image processing unit 20 are expressed by a plurality of blocks. Specifically, the image processing unit 20 includes a preprocessing unit 22 that functions as a preprocessing unit, a dividing unit (generation unit) 24 that functions as a dividing unit or a generation unit, a detection unit 26 that functions as a detection unit, and measurement. It has a measuring unit 28 that functions as a means. Each individual block may be constituted by a dedicated processor. Those blocks may be realized as functions of a control unit (CPU, operation program) described later.

  The preprocessing unit 22 functions as an extraction unit, or functions as a filter processing unit, a binarization processing unit, a labeling processing unit, a region expansion processing unit, and the like. The preprocessing unit 22 processes the tomographic image (original image), and extracts a femur image as a binarized image from the tomographic image. In other words, a binarized image from which parts other than the femur image are removed is obtained. The femur image is sent to the dividing unit 24 and also sent to the display processing unit 30 as necessary.

The dividing unit 24 generates a plurality of partial images from the femur image. Specifically, the dividing unit 24 generates a first partial image and a second partial image by dividing the femoral image into two. . The first partial image and the second partial image are sent to the detection unit 26. It is also sent to the display processing unit 30 as necessary. Examples of the femoral image division method include physical division and logical division. In the former, for example, the substance or data of the femur image is divided, and in the latter, for example, a plurality of processing regions are defined for the femur image. Each processing area is independent, and only a partial image included therein is a processing target.

  The detection unit 26 functions as a spindle specifying unit, a search range setting unit, and an edge detection unit. Specifically, for each individual partial image, the center of gravity and the principal axis passing therethrough are calculated based on the partial image. Subsequently, end points are specified by performing edge detection on the main axis. In that case, an edge is preferably searched for in the direction far from the side close to the center of gravity. The edge detected first is specified as an end point (first end point, second end point). The coordinate information of the two end points is sent to the measuring unit 28. The coordinate information is also sent to the display processing unit 30 as necessary. The measurement unit 28 calculates the distance between the first end point and the second end point, and outputs it to the display processing unit 30 as the femur length (FL). Other measurements based on the two end points may be performed.

  The display processing unit 30 is an electronic circuit having an image composition function, a color calculation function, and the like. The electronic circuit has one or more processors. A graphic image may be generated in the display processing unit 30. On the display screen of the display 32, a tomographic image (original image) including a femur image is displayed. On the tomographic image, graphic elements such as markers and lines are superimposed and displayed as necessary. Further, the femur length is displayed as a numerical value on or near the original image. Two partial images in the middle of processing or two partial images after processing may be displayed together with a graphic element group indicating the principal axis and end points. The display 32 is configured by an LCD or an organic EL device.

  The control unit 34 functions as a control unit that controls each component illustrated in FIG. 1, and is configured by a CPU and an operation program. An operation panel 36 is connected to the control unit 34. The operation panel 36 has input devices such as a trackball, a switch, and a keyboard.

  Hereinafter, the ultrasonic image processing method according to the embodiment will be described in detail with reference to FIGS.

  FIG. 2 illustrates the measurement of the femur length. When manually measuring the femoral length, an end point 42 is defined on the contour of one end of the femur image 40 and an end point 44 is defined on the other end of the femur image 40. Thereafter, a distance 48 between the two end points 42 and 44 is automatically calculated on the path 46 passing through the two end points 42 and 44. The distance is taken as the femur length. Depending on the position where the end points 42 and 44 are defined, the femur length as a measurement result varies. In general, each end point is set at an intermediate position of each end (or the bottom of a depression included in each end).

  FIG. 3 shows a tomographic image 50 of the fetus. The tomographic image 50 includes a femur image 52 having a relatively high luminance. However, there are some portions with relatively high luminance around the femur image. Therefore, in order to selectively extract the femur image 52, preprocessing described later is applied to the tomographic image. Note that an ROI (region of interest) 54 that defines an image processing range is set as necessary. Instead of such an ROI 54, the entire tomographic image may be subject to image processing.

  FIG. 4 shows a flowchart of the ultrasonic image processing method according to the present embodiment. The ultrasonic image processing method is executed in the image processing unit shown in FIG.

  In S12, the input tomographic image (original image) is binarized. Prior to that, the tomographic image may be filtered. In the binarization process, a threshold value (an initial threshold value in relation to a threshold value set in an area expansion process described later) is used. For example, pixel values greater than or equal to the threshold are converted to 1, and pixel values less than the threshold are converted to 0. A relatively high value is set as the threshold value at this stage. A histogram of the original image may be generated and a threshold value may be determined from the histogram. Filter processing may be applied to the image after binarization processing. In that case, a median filter may be used.

  In S14, a labeling process is applied to the binarized image, that is, a plurality of isolated regions are specified. Each area is a set of pixels having a pixel value of 1. Among them, a temporary femur image is extracted (temporary extraction) as a region that satisfies certain conditions regarding area, form, position, and the like. For example, after specifying a region having the largest area and a region having the second largest area, any one may be selected according to the morphological condition or the positional condition.

  In S16, the region expansion process is applied to the temporary femur image. Specifically, region growing processing is applied. This will be described in detail later with reference to FIG. 13 and FIG. 14, but in this area expansion process, the search process (concatenation process) is usually repeatedly executed a plurality of times. In the process, the search range is expanded stepwise in the longitudinal direction (long axis direction, principal axis direction) of the femur image. At the same time, the threshold is lowered step by step. In each search process, one or a plurality of femur image constituent pixels that are in contact with or continuous with a previously obtained region (seed region) are searched, and these are incorporated as a part of the femur image. As a result of the processing in S16, a high-quality femur image is extracted or generated. The femur image is a binarized image. Note that a method of searching for an area from a point designated on the tomographic image by the examiner may be used.

  In S18, a plurality of partial images are generated from the femur image. In the present embodiment, a division line serving as a division reference is determined for the femur image, and the femur image is divided into two with the division line as a boundary. As a result, a first partial image having a first end (for example, the left end) and a second partial image having a second end (for example, the right end) are generated. This will be described in a specific example later with reference to FIG.

  S20A and S20B can be executed in parallel. Similarly, S22A and S22B executed after them can also be executed in parallel. S20A and S22A can be collectively referred to as a first detection step corresponding to the first detection means. S20B and S22B can be collectively referred to as a second detection step corresponding to the second detection means.

  In S20A, the first partial image is analyzed, and the first centroid and the first principal axis passing through the first centroid are specified. The first center of gravity is the center of gravity of the first partial image, and the first principal axis is an axis facing the longitudinal direction of the first partial image, which corresponds to the bone axis. In the analysis of the first partial image, in the present embodiment, as described in detail later, a second moment around the center of gravity of the first partial image is calculated, and the angle of the first main axis is specified from the calculation result. Instead of such a method, a principal component analysis method or other methods can be used. In S22A, an edge search is executed from the center of gravity of the first partial image on the first principal axis, and the detected first edge is specified as the first end point. As will be described later, the search range may be limited.

  In S20B, the second partial image is analyzed. That is, as in S20A, the second center of gravity and the second main axis passing through the second center of gravity are specified. In S22A, an edge search is executed from the center of gravity of the second partial image on the second principal axis, and the detected first edge is specified as the second end point. Specific contents of S20A, S22A, S20B, and S22B will be described later with reference to FIG. The calculation formulas used in S20A and S22A will be described later. In S24, the femoral length is calculated as the distance between the first end point and the second end point. Other measurements may be performed.

  FIG. 5 illustrates an image dividing method. In the illustrated example, the center of gravity 58 of the femur image 56 is calculated. In general, the barycentric coordinate in the X-axis direction and the barycentric coordinate in the Y-axis direction are calculated separately. Here, the X axis and the Y axis are the horizontal axis and the vertical axis of the display coordinate system. In the illustrated example, a dividing line 60 is defined as a vertical line passing through the center of gravity 58. The femur image 56 is divided into two with the dividing line 60 as a boundary. As a result, a first partial image 62 and a second partial image 64 are generated. A point other than the center of gravity 58 may be used as a representative point that defines the position of the dividing line 60. For example, the minimum coordinate in the X direction and the maximum coordinate in the X direction may be specified from the coordinates of the pixel group constituting the femur image, and a dividing line may be set as the intermediate coordinate between them. When the above ROI is determined, the dividing line may be determined based on the ROI.

  By defining the dividing line 60 parallel to the Y axis, data division or region division is facilitated. If there is a possibility that the femur image is not in a horizontal posture or a posture equivalent thereto, for example, in addition to the center of gravity 58 of the femur image 56, the main axis passing through the center of gravity 58 is calculated, and the direction passing through the center of gravity 58 and perpendicular to the main axis A dividing line may be defined. The dividing line may be defined simply as a direction that passes through the center of gravity 58 and is orthogonal to the edge existing in the vicinity of the center of gravity. As described above, the femur image 56 can be divided physically or logically.

  FIG. 6 illustrates an end point detection method (end point specifying method). A first partial image 62 is shown in the upper part of FIG. 6, and a second partial image 64 is shown in the lower part of FIG. 6. For the first partial image 62, the center of gravity 66 and the first main axis 68 passing therethrough are calculated. In that case, for example, the second moment of the image shown in the following formulas (1) to (3) is calculated.

M _2,0 shown in the equation (1) is a second moment about the x-axis (X-axis in FIG. 6), which corresponds to dispersion in the x-axis direction. M _0,2 shown in the equation (2) is a second moment about the y-axis (Y-axis in FIG. 6), which corresponds to dispersion in the y-axis direction. M _1,1 shown in the equation (3) is a second moment about the x-axis and the y-axis, which corresponds to the covariance about the x-axis and the y-axis. Each element in the formulas (1) to (3) is defined as follows.

  Expression (4) indicates that each pixel B (x, y) constituting the binarized image (pixel distribution) has a value of 1 or 0. Formula (5) is a calculation formula for obtaining the x coordinate of the center of gravity. Equation (6) is a calculation equation for obtaining the y coordinate of the center of gravity. Then, the angle θ of the main shaft is calculated according to the following equation (7).

  The position and direction of the main axis are specified from the angle θ of the main axis and the coordinates of the center of gravity. The above calculation formula is an example. The main axis may be specified by another calculation formula or another method. For example, a principal component analysis method may be used.

  In the first partial image 62 shown in FIG. 6, the first main axis 68 is specified according to the above calculation formula. After that, an edge is searched along the first main axis 68, and the first end point 72 is specified as the first detected edge. In the edge search, the center of gravity may be used as the search start point. If the partial image to be processed is the left image after division, the search direction is the left side. If the partial image to be processed is the right image after division, the search direction is the right side.

Also in the second partial image 64 shown in FIG. 6, first, the second centroid 74 and the second main axis 76 passing through the second centroid 74 are specified according to the above calculation formula. Subsequently, an edge is searched along the second main axis 76. The first detected edge is set as the second end point 80.

  According to the present embodiment, since the edge detection main axes 68 and 76 can be specified based on the divided partial images 62 and 64, respectively, even if the femur image is bent or deformed, the comparison is performed. Therefore, the two end points 72 and 80 can be specified accurately. That is, since the curvature or deformation in each of the partial images 62 and 64 is smaller than the curvature or deformation in the entire femur image (for example, the amount is ½), each of the main axes 68 and 76 is a partial image. The possibility of passing through the intermediate position of the epiphysis at 62 and 64 or in the vicinity thereof is increased. According to our research, in many cases, each endpoint can be determined fairly accurately, and at the same time, each doctor can determine each endpoint at a position similar to the position where the skilled doctor manually defines each endpoint. , Has been confirmed.

  FIG. 7 shows an example of measuring the femur length. In the femur image 56, the first end point 72 and the second end point 80 are specified by the above method. On the straight line 82 passing through them, the distance 84 between the two end points 72, 80 is measured and output as the femur length (FL). Incidentally, when the main axis is obtained from the entire femur image 56 shown in FIG. 7, the main axis is specified as a straight line 82A due to the influence of the curvature of the femoral image 56. When the two end points 72A and 80A are specified on the straight line 82A and the distance 84A thereof is obtained, the distance 84A is considerably different from the distance 84. For the curved femur image 56, it can be confirmed that the above dividing method is effective in ensuring the measurement accuracy.

  Next, some variations will be described with reference to FIGS. As shown in FIG. 8, when the main axis 68 passing through the center of gravity 66 is specified in the first partial image 62, a search range is defined on the first main axis 68 based on, for example, the number of weeks of pregnancy. May be. Two lines 86 and 88 indicate both ends of the search range. Edge detection is performed within the search range, whereby the first end point 72 is obtained. According to this, search accuracy can be improved and search time can be shortened.

  FIG. 9 shows a first example of the region setting method. Instead of dividing the image by dividing lines, a plurality of processing areas are set, and the above processing is applied to partial images of the respective processing areas. In the first example shown in FIG. 9, the main axis 94 that passes through the center of gravity 92 is specified in the femur image 90, and two processing regions 96 and 98 that are separated from each other are set on the basis of the main axis 94. In this case, the processing area 96 may be set so as to include the minimum coordinate in the X-axis direction, and the processing area 98 may be set so as to include the maximum coordinate in the X-axis direction. In the illustrated example, the processing areas 96 and 98 are determined so that the center lines of the processing areas 96 and 98 coincide with the main axis 94. Such an area setting makes it less susceptible to the bending of the femur image 90.

  FIG. 10 shows processing for two partial images 100 and 106 in the two processing regions 96 and 98 set as described above. A main axis 102 is specified based on the partial image 100, and an end point 104 is specified on the main axis 102. Similarly, the main axis 108 is specified based on the partial image 106, and the end point 110 is specified on the main axis 108.

  FIG. 11 shows a second example of the region setting method. Two processing regions 112 and 114 are set to the femur image 90 with a partially overlapping relationship. Reference numeral 115 indicates the overlapping portion. When setting the two processing areas 112 and 114, the setting method employed in the first example can be used, and other methods can also be used. The main axis 116 is specified based on the partial image in the processing area 112, and the main axis 118 is specified based on the partial image in the processing area 114.

  FIG. 12 shows a third example of the region setting method. As illustrated, three or more processing regions arranged along the curved bone axis may be set for the femur image 90 based on some criterion. In the illustrated example, four processing areas 120, 122, 124, and 126 are set.

  Next, a specific example of the area expansion process shown in FIG. 2 will be described with reference to FIGS. In the region expansion process, a region growing method is executed. To be precise, a method developed or modified from the region growing method is executed as follows.

In S30, the target length is calculated based on the average length (average value of femur length) and standard deviation (variation of femur length) determined according to the number of gestational weeks. More specifically, from the standard deviation, a variable amount in the major axis direction (a range extending before and after the average value) is determined for the search range described later, and the target length is set as the maximum value that allows for the variable amount. The However, such a setting is merely an example. S32 and S34 correspond to the search area setting step, and S35 and S36 correspond to the threshold setting step.

  Specifically, in S32, the barycentric coordinates, major axis angle, major axis length, and minor axis length are calculated based on the seed region. The seed region is a region constituting the temporary femur image extracted by the binarization process and the labeling process in the first stage, and the region constituting the femur image that has undergone the previous connection process in the later stage. It is. The major axis is the main axis, and the minor axis is an axis orthogonal to the main axis. When specifying the long axis, the second moment of the image may be calculated as described above, or another method such as principal component analysis may be used. The major axis length can be determined, for example, by multiplying the target length by a coefficient. It is desirable to change (increase) the coefficient in accordance with an increase in the number of times of connection processing. The short axis length can be determined, for example, by multiplying the target length by another coefficient. The short axis length is a constant value regardless of the number of connection processes. Of course, the short axis length may be varied.

  In S34, a search range having an elliptical shape is set based on the barycentric coordinates, major axis angle, major axis length, and minor axis length obtained as described above. The search range has a size that extends beyond the seed region in the long axis direction. A specific example will be described later with reference to FIG. The shape of the search range defines the region expansion direction.

  In S35, based on the tomographic image (original image), all pixel values in the seed region are referred to, and the minimum value and the maximum value are specified from among them. In S36, a search luminance value range on the luminance axis is determined based on the specified maximum value and minimum value, and based on the number of connections. In the present embodiment, the upper limit of the search luminance range is set to the maximum luminance. In this case, the entity of S36 is in adaptive determination of a threshold value that is the lower limit. Of course, the search luminance range may be determined based on the average luminance. In the present embodiment, basically, the threshold value is lowered stepwise in accordance with the number of times of connection processing. The above method relating to the setting of the search luminance value range is merely an example, and other methods can be used. It is desirable to perform a filtering process on the tomographic image to be referred to in advance. In that case, a Gaussian filter or the like can be used.

  In S38, a connection process is executed. That is, based on a tomographic image (preferably a tomographic image after filtering), within the currently set search range, the pixel value is equal to or greater than the currently set threshold and is in contact with the seed region. Alternatively, continuous pixel groups are specified, and the pixel groups are connected to the seed region. That is, the pixel group is additionally incorporated as a part of the femur image.

  In S40, it is determined whether or not a predetermined end condition is satisfied. If not, in S42, the region after the connection process, that is, the expanded region is set as a new seed region, and the steps after S32 and S35 are performed. Repeatedly executed. A plurality of conditions may be defined as the end conditions. For example, this process may be terminated when the connection process for the scheduled number of times is completed. In addition, this process may be terminated when the area is expanded to a certain extent in the long axis direction. Furthermore, this process may be terminated when the area does not expand even if the threshold value is lowered to some extent. The termination condition may be appropriately determined according to the purpose.

  FIG. 14 schematically shows an example of setting the search range. The contents are only examples. Reference numeral 134 denotes a target length (maximum search area in the long axis direction). Based on the femur image (first seed region) 130, the center of gravity 132 is calculated, and a long axis 136 is defined as a direction passing through the center of gravity 132, and is a direction passing through the center of gravity 132 and perpendicular to the long axis 136. A short axis 138 is defined as follows. As described above, the long axis length is determined in stages from the target length 134 and the number of connected processes. The short axis length is fixedly determined from the target length 134. The search range 140 as an ellipse is defined by these parameters. Within the search range 140, a pixel group having a luminance value equal to or higher than the currently set threshold value and satisfying the connection condition is specified, and this is a countermeasure for the connection process. As a result of the connection process, typically, a region 142 that slightly extends in the direction of the long axis 136 relative to the first seed region 130 is generated, and the region 142 becomes a new seed region.

  That is, based on the area 142, the barycentric coordinates, major axis angle, major axis length, and minor axis length are recalculated, and the ellipse search range 144 is set based on them. The search range 144 has a size (long axis length) that is longer in the longitudinal direction of the femur image than the previously set search range 140. Within the search range 144, a predetermined pixel group is searched using a newly set (further reduced) threshold value, and these are to be connected. A region 146 is obtained as a result of the concatenation process. The above process is repeated until the end condition is satisfied.

  The shape of the search area is not limited to an ellipse, but may be a rectangle or the like. A threshold may be set based on a histogram of the seed region. A threshold value may be set based on the average pixel value of the seed region.

  According to the above region expansion method, on the basis of a region that is likely to be a part of a femur image, a portion connected to the region (particularly a portion having a pixel value lower than a threshold value in binarization processing) ) Can be searched in stages. In other words, it is possible to reduce the possibility that something that is not part of the femur image will be inadvertently connected. By obtaining a plurality of partial images from high-quality femur images that have undergone such processing and applying the end point detection method individually to them, each of the images is placed at an appropriate position at each end of the femur image. It becomes possible to determine an end point.

  Prior to the application of the region expansion method, the binarized femur image is divided to generate a plurality of partial images, and after the region expansion method is applied to each partial image, The end point detection method may be applied. The region expansion method has directionality and can be expected to expand the region in a specific direction. It is conceivable to use the method in other applications. A method other than the above method may be applied as pre-processing of the end point detection method.

  In the above embodiment, the femur image of the fetus is a processing target. However, the above processing may be applied to an image of another long bone in the fetus, and a long bone included in a subject other than the fetus. You may apply the said process with respect to this image. You may make it perform the said image processing method in apparatuses other than an ultrasonic diagnosing device.

20 image processing unit, 22 preprocessing unit, 24 division unit (generation unit), 26 detection unit, 28 measurement unit.

Claims (8)

Preprocessing means for extracting a long bone image having a first bone end and a second bone tip from a tomographic image;
A means for generating a plurality of partial images from the long bone image, wherein the generating means generates at least a first partial image having the first bone end and a second partial image having the second bone end. When,
A first detection means for identifying a first principal axis for the first partial image by analysis of the first partial image, and detecting a first endpoint as an edge of the first bone end on the first principal axis;
Second detection means for identifying a second principal axis for the second partial image by analysis of the second partial image, and detecting a second end point as an edge of the second bone end on the second principal axis;
Including
The ultrasonic image processing apparatus, wherein the first end point and the second end point are used in the measurement of the long bone image. The apparatus of claim 1.
The generating unit includes a dividing unit that generates the first partial image and the second partial image by dividing the long bone image.
An ultrasonic image processing apparatus. The apparatus of claim 2.
The dividing unit generates the first partial image and the second partial image by dividing the long bone image into two parts.
An ultrasonic image processing apparatus. The apparatus of claim 3.
The dividing means cuts the long bone image parallel to the vertical axis of the display coordinate system;
An ultrasonic image processing apparatus. The apparatus of claim 1.
The first principal axis is an axis that passes through first representative coordinates in the first partial image and represents the longitudinal direction of the first partial image;
The second principal axis is an axis that passes through the second representative coordinates in the second partial image and represents the longitudinal direction of the second partial image.
An ultrasonic image processing apparatus. The apparatus of claim 1.
The preprocessing means includes
Binarization processing means for generating a binarized image as the tomographic image by binarizing the original tomographic image using an initial threshold;
Extraction means for extracting a provisional long bone image from the binarized image;
A region expansion processing unit that applies a region expansion process to the temporary long bone image after setting a threshold value lower than the initial threshold value, thereby generating the long bone image from the temporary long bone image; ,
An ultrasonic image processing apparatus comprising: The apparatus of claim 6.
The region expansion processing means applies a plurality of region expansion processing to the temporary long bone image while gradually reducing the threshold within a range lower than the initial threshold.
An ultrasonic image processing apparatus. Extracting a femur image having a first epiphysis and a second epiphysis from a tomographic image representing a fetus;
Dividing the femur image, thereby generating a first partial image having the first bone end and a second partial image having the second bone end;
Identifying a first principal axis for the first partial image by analysis of the first partial image, and detecting a first endpoint as an edge of the first bone end on the first principal axis;
Identifying a second principal axis for the second partial image by analysis of the second partial image, and detecting a second endpoint as an edge of the second bone end on the second principal axis;
Measuring a distance between the first end point and the second end point as a femur length;
An ultrasonic image processing method comprising: