JP2016036417A - Ultrasonic diagnostic equipment - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an improvement technology that specifies a noteworthy region in diagnosis.SOLUTION: Based on a shape of a spatial distribution of pixel values in image data of an ultrasonic image, an image element extraction part 32 extracts a plurality of image elements corresponding to a predetermined shape in the image data. An image element selection part 34 selects a plurality of noteworthy image elements which are candidates for a noteworthy part from the extracted plurality of image elements based on a statistical feature amount of the pixel values in each image element. A noteworthy region formation part 36 forms a noteworthy region corresponding to the noteworthy part based on the selected plurality of noteworthy image elements. Based on the noteworthy region, a region of interest (ROI) of the noteworthy part is established.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an ultrasonic diagnostic apparatus, and more particularly to a technique for specifying a region to be noted in diagnosis.

  In diagnosis using an ultrasonic diagnostic apparatus, it is known to set a region of interest in a region of interest in an ultrasound image. For example, a user such as a doctor who uses an ultrasonic diagnostic apparatus confirms an ultrasonic image displayed on the apparatus and uses an operation device of the apparatus to display a region of interest in a region of interest in the image. Perform the operation to set. However, since the operation related to the setting of the region of interest is not always easy, several techniques for reducing the burden on the user related to the operation have been proposed.

  For example, Patent Document 1 describes a technique for setting a region of interest for each of a lesioned part and a fat layer in an ultrasonic image in an ultrasonic diagnostic apparatus that obtains elasticity information of a living tissue. The ultrasonic diagnostic apparatus disclosed in Patent Literature 1 specifies a region of interest or the like based on a luminance value of a pixel in an ultrasonic image, thereby setting a suitable region of interest while reducing the burden on the user operation. It is possible.

  Incidentally, Non-Patent Document 1 describes an extraction algorithm related to feature extraction based on the three-dimensional curvature of a three-dimensional medical image, and application of this extraction algorithm to diagnosis and treatment is expected.

International Publication No. 2013/183432 Pamphlet

Yoshiki Kawada, Noboru Niki, “Extraction Algorithm for 3D Curvature Features”, MEDICAL IMAGING TECHNOLOGY Vol.19 No.3 May 2001, p142-153

  With the technique described in Patent Document 1, for example, it is possible to set a region of interest in a region to be noticed in diagnosis while reducing the burden of user operation. The inventor according to the present application has conducted research and development on further improvement of the epoch-making technique described in Patent Document 1. In particular, attention has been paid to an improved technique using the extraction algorithm described in Non-Patent Document 1.

  The present invention has been made in the course of research and development, and an object of the present invention is to provide an improved technique for identifying an area to be noted in diagnosis.

  An ultrasonic diagnostic apparatus suitable for the above object includes an image forming unit that forms an ultrasonic image based on a signal obtained by transmitting and receiving ultrasonic waves, and a shape of a spatial distribution of pixel values in the ultrasonic image. Based on the image element extraction unit for extracting a plurality of image elements corresponding to a predetermined shape in the ultrasonic image, and a plurality of images based on a statistical feature amount of a pixel value in each image element An image element selection unit that selects a plurality of target image elements that are candidates for a target region from among the elements; a target region formation unit that forms a target region corresponding to the target region based on the plurality of target image elements; It is characterized by having.

  In the above apparatus, a preferable specific example of the ultrasonic image is a B-mode image, but an image other than the B-mode image may be used as the ultrasonic image. In addition, the spatial distribution of pixel values in the ultrasound image is set such that, for example, for a plurality of pixels arranged in a secondary manner, the height of the surface is set for each pixel according to the size of the pixel value. It can be expressed by a curved surface. Then, for example, a plurality of image elements whose local shape is a predetermined shape are extracted from the distribution expressed by the curved surface. It is desirable that the predetermined shape is determined in accordance with, for example, the property of the site of interest to be noted in the diagnosis. In addition, a specific example suitable as a statistical feature amount of a pixel value in each image element is an average value, but for example, a statistical feature amount such as a variance value is used according to the property of the region of interest. May be.

  Then, the device selects a plurality of target image elements that are candidates for the target region from the plurality of image elements based on the statistical feature amount of the pixel value in each image element, and the plurality of target images. Based on the image element, it is possible to form a region of interest corresponding to the region of interest.

  In a preferred embodiment, the image element extraction unit extracts a plurality of image elements using a shape index obtained by analyzing a spatial distribution of pixel values in an ultrasonic image. .

  In a preferred embodiment, the image element extraction unit obtains an index indicating the degree of spatial unevenness of the pixel value related to the target pixel and neighboring pixels located in the vicinity thereof, and uses the index to form a concave surface. A plurality of corresponding image elements and a plurality of image elements corresponding to the convex surface are extracted.

  In a preferred embodiment, the image element selection unit includes a statistical feature amount of a pixel value in the reference image element and other information about the reference image element selected as the image element corresponding to the target region and the other plurality of image elements. A plurality of target image elements are selected from a plurality of image elements including a reference image element according to a selection condition based on a statistical feature amount of a pixel value in each of the image elements.

  In a preferred embodiment, the image element selection unit performs other according to a selection condition for comparing a threshold value based on an average value of pixel values in a reference image element and an average value of pixel values in other image elements. A plurality of image elements and reference image elements that meet a selection condition among the plurality of image elements are set as a plurality of target image elements.

  In a preferred specific example, the attention area forming unit forms an attention area by connecting at least some of the plurality of attention image elements to each other.

  In a preferred embodiment, the region-of-interest forming unit performs expansion processing on each of the plurality of target image elements, and combines several target image elements connected to each other by the expansion processing, and the plurality of target image elements. A region of interest is formed based on a combined region obtained by combining the surrounded region.

  In a preferred embodiment, the attention area forming unit forms the attention area by shrinking the composite area.

  In a preferred embodiment, the image forming unit forms an ultrasonic image including a tumor that is the target region, and the image element extracting unit determines the degree of spatial unevenness of pixel values in the ultrasonic image. By obtaining the index shown, the index is used to extract a plurality of image elements corresponding to the concave surface and a plurality of image elements corresponding to the convex surface, and the image element selection unit selects as an image element corresponding to the tumor Selected concave reference image elements and some image elements of the plurality of other image elements are selected as a plurality of attention image elements, and the attention area forming unit includes at least one of the plurality of attention image elements. A mass region that is the region of interest is formed by connecting several together.

  In addition, a suitable ultrasonic image processing apparatus that meets the above-described object is provided with a plurality of image elements corresponding to a predetermined shape in the ultrasonic image based on the shape of the spatial distribution of pixel values in the ultrasonic image. An image element extracting unit that extracts a plurality of target image elements that are candidates for a target region from a plurality of image elements based on a statistical feature amount of a pixel value in each image element It has a selection part and an attention area formation part which forms an attention area corresponding to an attention part based on a plurality of attention image elements.

  The ultrasonic image processing apparatus can be realized by a computer, for example. That is, based on the shape of the spatial distribution of pixel values in the ultrasound image, an image element extraction function that extracts a plurality of image elements corresponding to a predetermined shape in the ultrasound image, Based on the statistical feature amount of the pixel value in the image element selection function for selecting a plurality of target image elements that are candidates for the target region from a plurality of image elements, and based on the plurality of target image elements, A computer can function as the ultrasonic image processing apparatus by a program that causes a computer to realize a region of interest formation function that forms a region of interest corresponding to a region of interest. The program may be stored in a computer-readable storage medium such as a disk or a memory, and may be provided to the computer via the storage medium, or may be provided to the computer via an electric communication line such as the Internet. May be provided.

  The present invention provides an improved technique for identifying a region of interest in diagnosis.

1 is an overall configuration diagram of an ultrasonic diagnostic apparatus suitable for implementing the present invention. It is a figure for demonstrating the spatial distribution of the pixel value in an ultrasonic image. It is a figure which shows the specific example of a shape index. It is a figure which shows the specific example of the concave surface element and convex surface element in an ultrasonic image. It is a figure which shows the specific example of selection based on the average value of a pixel value. It is a figure which shows the specific example of the process which forms a tumor area | region. It is a figure for demonstrating the specific example which sets circular tumor ROI.

  FIG. 1 is a diagram showing an overall configuration of an ultrasonic diagnostic apparatus suitable for implementing the present invention. The probe 10 is an ultrasonic probe that transmits / receives ultrasonic waves to / from a subject including a target region to be diagnosed. The probe 10 includes a plurality of vibration elements that transmit and receive ultrasonic waves, and transmission of the plurality of vibration elements is controlled by the transmission / reception unit 12 to form a transmission beam. Further, the plurality of vibration elements receive ultrasonic waves obtained from the region including the region of interest, and a signal obtained thereby is output to the transmission / reception unit 12, and the transmission / reception unit 12 forms a reception beam to form a reception beam. A received signal (echo data) is collected along. It should be noted that techniques such as transmission aperture synthesis may be used in transmission / reception of ultrasonic waves.

  The image forming unit 20 forms image data of an ultrasonic image based on the reception signal obtained from the transmission / reception unit 12. The image forming unit 20 performs signal processing such as gain correction, log compression, detection, contour emphasis, and filter processing on the received signal as necessary, for example, a cross section within the subject including the region of interest. The image data of the B mode image is formed. Note that the image forming unit 20 preferably includes a frame memory that stores image data of a plurality of time phases (a plurality of frames).

  The region-of-interest setting unit 30 sets a region of interest (ROI) for the region of interest in the image data obtained from the image forming unit 20. The region-of-interest setting unit 30 includes an image element extraction unit 32, an image element selection unit 34, and a region of interest formation unit 36. It is desirable that the image data used in the region-of-interest setting unit 30 is obtained by removing so-called speckles that are ultrasonic interference fringes.

  The image element extraction unit 32 extracts a plurality of image elements corresponding to a predetermined shape in the image data based on the shape of the spatial distribution of pixel values in the image data of the ultrasonic image. The image element selection unit 34 selects a plurality of target image elements that are candidates for the target region from the extracted plurality of image elements based on the statistical feature amount of the pixel value in each image element. The attention area forming unit 36 forms an attention area corresponding to the attention site based on the plurality of selected attention image elements. Then, based on the region of interest, a region of interest (ROI) of the region of interest is set. The processing in each part in the region-of-interest setting unit 30 will be described in detail later.

  The displacement measuring unit 42 measures the displacement of the tissue in the subject based on echo data corresponding to different time phases. Explaining a specific example, the displacement measuring unit 42 performs one-dimensional or two-dimensional correlation calculation processing on frame data (N) and frame data (X) corresponding to different time phases, thereby obtaining frame data. For each measurement point in the tomographic image, a displacement vector indicating the displacement of the tissue at the measurement point, that is, a one-dimensional or two-dimensional displacement vector related to the direction and magnitude of the displacement, is derived, The distribution of displacement vectors at a plurality of measurement points is obtained. In deriving the displacement vector, for example, a block matching method or a phase gradient method is used.

  In the block matching method, the frame data, that is, the tomographic image, is divided into a plurality of blocks by blocks each consisting of several pixels in the vertical direction and several pixels in the horizontal direction. The block most similar to the block is searched for in the other frame data. Thereby, the displacement between time phases is calculated for each measurement point (each block) in the frame data, and for example, a two-dimensional displacement vector is obtained. Note that a displacement vector at each measurement point may be obtained by referring to search results of a plurality of blocks and performing a process such as predictive encoding, that is, a process of determining a sample value based on a difference.

  In the phase gradient method, the phase information of the wave of the received signal is obtained from the received data constituting the frame data, and the amount of movement of the wave of the received signal is calculated from the change in the phase information between the time phases, and the frame data By deriving the displacement of each measurement point, for example, a one-dimensional displacement vector in the reception beam direction or a two-dimensional displacement vector in the frame data may be obtained.

  The elasticity information calculation unit 44 obtains elasticity information (strain or elastic modulus) of the tissue in the subject based on the displacement measured by the displacement measurement unit 42. For example, based on the displacement vector at each measurement point measured between two pieces of frame data corresponding to different time phases, the elasticity information calculation unit 44 performs tissue strain and elasticity at each measurement point for a plurality of measurement points. Calculate the rate, etc. Further, the elasticity information calculation unit 44 calculates the strain and elastic modulus of the tissue at a plurality of measurement points in the frame for each time phase (each frame) over a plurality of time phases.

  When obtaining the strain and elastic modulus of the tissue in the elasticity information calculation unit 44, for example, the probe 10 is pressed against the subject, the tissue in the subject is compressed from the body surface of the subject, and the tissue of the tissue by the compression is compressed. Displacement is measured. At that time, for example, a pressure sensor (not shown) detects the pressure between the transmission / reception surface of the probe 10 and the body surface of the subject, and a stress measurement unit (not shown) is based on the pressure detected by the pressure sensor. The stress at each measurement point inside the subject is measured.

  The elasticity information calculation unit 44 calculates the elastic modulus of the tissue at each measurement point with reference to the stress measured by the stress measurement unit. The strain data is calculated by spatially differentiating the amount of tissue movement, for example, displacement. The elastic modulus data is calculated by dividing the change in stress by the change in strain.

  For example, assuming that the displacement measured by the displacement measuring unit 42 is L (x) and the stress measured by the stress measuring unit is P (x) for the position x in the frame data, that is, the tomographic image, the strain ΔS (x). Can be calculated by spatially differentiating L (x), and for example, distortion can be calculated using an equation “ΔS (x) = ΔL (x) / Δx”. Further, the Young's modulus Ym (x) of the elastic modulus data can be derived by the equation “Ym (x) = ΔP (x) / ΔS (x)”. For example, the elastic modulus of the tissue corresponding to each measurement point in the frame data, that is, the tomographic image, can be obtained from the Young's modulus Ym. The Young's modulus is a ratio of a simple tensile stress applied to the object and a strain generated in parallel with the tension.

  The elasticity information calculation unit 44 has a function of calculating an FLR (Fat Lesion Ratio). FLR is the ratio of the elasticity value of fat to the mass (the elasticity value of fat / the elasticity value of the mass), and is a suitable diagnostic value in diagnosis of the mammary gland, for example. As the elasticity value of fat, an average value of distortion in a region of interest (fat ROI) set for fat in an ultrasound image (in a tomographic image) is preferable. The average value of the distortion in the region of interest (tumor ROI) set for the tumor in the image (in the tomographic image) is suitable.

  The fat ROI is a circular ROI set in the fat region in the ultrasonic image. For example, a user such as a doctor visually confirms the ultrasonic image displayed on the display unit 52 and the ultrasonic ROI. A circular fat ROI is set by adjusting the center position of the fat ROI and the size of the radius in the sound image. The fat ROI may be set using a known technique. For example, by using the technique described in Patent Document 1, by specifying the tissue boundary based on the luminance value of the pixel in the ultrasound image, the fat ROI is reduced while reducing the burden on the user operation. It may be set.

  On the other hand, the tumor ROI is an ROI set in the region of the tumor in the ultrasonic image, and is set in the region-of-interest setting unit 30 as will be described in detail later.

  The display processing unit 50 forms a display image based on the image data of the ultrasonic image obtained from the image forming unit 20 and the elasticity information obtained from the elasticity information calculating unit 44. The display image formed in the display processing unit 50 is displayed on the display unit 52.

  The display processing unit 50 forms a display image including the ultrasonic image, for example, a B-mode image, based on the image data of the ultrasonic image obtained from the image forming unit 20. Further, the display processing unit 50 may form a display image including an elasticity image that visually indicates the elasticity value obtained from the elasticity information calculation unit 44. The elastic image is formed by a known technique.

  For example, an elasticity image for visually indicating elasticity information in the cross section of the subject is formed based on elasticity frame data indicating elasticity values (tissue strain and elastic modulus) at each measurement point in the frame data. The For example, hue information corresponding to the elasticity value at the measurement point is given to each measurement point of the elastic frame data, and red (R) which is the three primary colors of light for each measurement point based on the elasticity frame data. ), Green (G), and blue (B) elastic image data is formed, and an elastic image corresponding to the elastic image data is displayed.

  The controller 70 generally controls the inside of the ultrasonic diagnostic apparatus shown in FIG. The overall control by the control unit 70 also reflects an instruction received from the user via the operation device 60.

  Among the configurations shown in FIG. 1 (respectively assigned parts), the transmitting / receiving unit 12, the image forming unit 20, the image element extracting unit 32, the image element selecting unit 34, the attention area forming unit 36, the displacement measuring unit 42, and elasticity information Each unit of the calculation unit 44 and the display processing unit 50 can be realized by using hardware such as an electric / electronic circuit or a processor, for example, and a device such as a memory may be used as necessary for the realization. In addition, functions corresponding to the above-described units may be realized by cooperation of hardware such as a CPU, a processor, or a memory, and software (program) that defines the operation of the CPU or the processor.

  A preferred specific example of the display unit 52 is a liquid crystal display or the like, and the operation device 60 can be realized by at least one of a mouse, a keyboard, a trackball, a touch panel, and other switches, for example. And the control part 70 is realizable by cooperation with hardware (CPU, a processor, memory, etc.) and the software (program) which prescribes | regulates operation | movement of CPU, a processor, for example.

  The overall configuration of the ultrasonic diagnostic apparatus in FIG. 1 is as described above. Next, functions related to the setting of the region of interest in the ultrasonic diagnostic apparatus will be described. In addition, about the structure (each part which attached | subjected the code | symbol) shown in FIG. 1, the code | symbol of FIG. 1 is utilized in the following description.

  FIG. 2 is a diagram for explaining the spatial distribution of pixel values in an ultrasonic image. The image element extraction unit 32 extracts a plurality of image elements corresponding to a predetermined shape in the ultrasonic image based on the shape of the spatial distribution of pixel values in the ultrasonic image.

  The spatial distribution of pixel values in an ultrasonic image is, for example, a curved surface in which the height of a surface is set for each pixel according to the size of the pixel value for a plurality of pixels arranged secondarily. Can be expressed. FIG. 2 shows the curved surface (distributed curved surface of pixel values).

  In FIG. 2, the ultrasonic image is, for example, a B-mode image, which is image data formed in the image forming unit 20. The ultrasonic image is composed of a plurality of pixels regularly and two-dimensionally arranged in the horizontal direction and the vertical direction. When an ultrasonic image is displayed on the display unit 52, the lightness and darkness of the pixel is expressed according to the pixel value of each pixel, for example, the luminance.

  In FIG. 2, in the XYZ orthogonal coordinate system, the horizontal direction and the vertical direction of the ultrasonic image are respectively associated with the X-axis direction and the Y-axis direction, and the pixel value (luminance) of each pixel constituting the ultrasonic image is the Z-axis. It is associated with the direction. For example, the pixel value p (x, p) of the pixel at the coordinates (x, y) in the ultrasonic image is associated with the height of the curved surface (the size in the Z-axis direction) at the coordinates (x, y). A distribution curved surface of pixel values is formed.

  The image element extraction unit 32 analyzes the shape of the spatial distribution of pixel values in the ultrasonic image, for example, the shape of the distribution curved surface of the pixel values shown in FIG. A plurality of image elements corresponding to are extracted.

  The image element extraction unit 32 obtains an index related to the shape by analyzing the shape of the distribution curved surface of the pixel value, and extracts a plurality of image elements using the index. As a shape-related index, a known shape index is suitable.

  In order to obtain the shape index by analyzing the shape of the distribution curved surface of the pixel value, for example, an extraction algorithm detailed in Non-Patent Document 1 may be used. The image element extraction unit 32 obtains a shape index as a feature quantity based on the three-dimensional curvature of the distribution curved surface of the pixel values shown in FIG.

  FIG. 3 is a diagram showing a specific example of the shape index. The image element extraction unit 32 obtains a shape index representing the degree of unevenness of the curved surface determined from the relationship between the pixel of interest and its adjacent pixels in the pixel value distribution curved surface (see FIG. 2). That is, a shape index is derived for each image element including a target pixel and a plurality of pixels located in the vicinity thereof in the ultrasonic image.

  In FIG. 3, the shape index is shown on the vertical axis, and the range of the shape index value is −1.0 to 1.0. The image element extraction unit 32 extracts an image element having a shape index value smaller than −0.2, for example, as a concave element, and extracts an image element having a shape index value larger than 0.2, for example, as a convex element.

  In addition, the horizontal axis of FIG. 3 shows Curvedness which is one of the indexes related to the shape. In order to obtain the Curvedness by analyzing the shape of the distribution surface of pixel values, for example, an extraction algorithm detailed in Non-Patent Document 1 may be used. Curvedness is an index representing the degree of curvature of a curved surface determined from the relationship between a pixel of interest and its adjacent pixels in a pixel value distribution curved surface (see FIG. 2). The image element extraction unit 32 may refer to the Curvedness in addition to the shape index when extracting the concave element and the convex element. Further, the concave surface element and the convex surface element may be extracted using another feature amount based on the three-dimensional curvature of the pixel value distribution curved surface (see FIG. 2) as an index.

  FIG. 4 is a diagram illustrating a specific example of a concave element and a convex element in an ultrasonic image. FIG. 4 shows a plurality of concave elements S1, S3, S6 and a plurality of convex elements S2, S4, S5, S7 extracted from the ultrasonic image.

  When the site of interest is a tumor, the user confirms the tumor of interest in the ultrasound image displayed on the display unit 52 and uses the operation device 60 to designate the designated point A as the image location of the tumor of interest. Set.

  When the designated point A is set, the image element selection unit 34 selects the concave element closest to the designated point A designated by the user from the plurality of concave elements and the plurality of convex elements as the first element (reference). Element). In the specific example of FIG. 4, among the plurality of concave elements S1, S3, S6, for example, the concave element S1 whose center position (position of the center of gravity of the area) is closest to the designated point A is selected as the first element.

  Furthermore, the image element selection unit 34 selects four elements from the second element to the fifth element in order from the closest to the designated point A from the plurality of concave elements other than the first element and the plurality of convex elements. . In the specific example of FIG. 4, among the plurality of concave elements S3, S6 and the plurality of convex elements S2, S4, S5, S7 other than the concave element S1, which is the first element, for example, the center position (position of the center of gravity of the area) is. The convex element S2, the concave element S3, the convex element S4, and the convex element S5 are selected as the fifth element from the second element in order from the side closer to the designated point A.

  Then, the image element selection unit 34, for the four elements from the first element and the second element to the fifth element as a reference, the statistical feature amount of the pixel value in the first element and the second element to the second element A plurality of target image elements are selected based on a statistical feature amount of pixel values in each of up to five elements. As the statistical feature quantity of the pixel value, an average value of the pixel values in each element is preferable.

  FIG. 5 is a diagram illustrating a specific example of selection based on an average value of pixel values. FIG. 5 shows the luminance (pixel value) in each element for each of the concave element S1, convex element S2, concave element S3, convex element S4, and convex element S5 selected as the first to fifth elements. Is shown on the vertical axis. Note that the luminance of each element in FIG. 5 is, for example, a luminance distribution on a straight line passing through the center of each element shown in FIG.

  In the specific example shown in FIG. 5, the average value of the pixel values related to the concave element S1, that is, the average luminance is M1, and the average luminance of the convex element S2, concave element S3, convex element S4, and convex element S5 is M2 respectively. , M3, M4, M5.

  The image element selection unit 34 determines a threshold value based on the average luminance M1 of the concave surface element S1. For example, (M1 × k) is set as a threshold value using a threshold coefficient k. For example, it is desirable to use an empirically obtained value for the magnitude of the coefficient k, but for example, the magnitude of the coefficient k may be appropriately adjusted by the user.

  Then, the image element selection unit 34 compares the threshold value with the average luminance for each element of the convex element S2, the concave element S3, the convex element S4, and the convex element S5, and the average luminance is smaller than the threshold ( Alternatively, an element whose average luminance is equal to or less than a threshold value is selected as an attention image element. Further, the reference concave surface element S1 is also selected as the target image element.

  Thereby, in the specific example shown in FIG. 5, concave element S1, convex element S2, concave element S3, and convex element S4 are selected as several attention element images. When a plurality of target image elements are selected, a tumor region corresponding to a tumor that is a target site is formed based on the plurality of target image elements.

  FIG. 6 is a diagram illustrating a specific example of a process for forming a tumor region. FIG. 6 <1> shows a concave element S1, a convex element S2, a concave element S3, and a convex element S4 selected as a plurality of target image elements. The attention area forming unit 36 connects these attention image elements to each other to form a tumor area that is the attention area.

  The attention area forming unit 36 performs an expansion process on each of the plurality of attention image elements, and closely or partially overlaps the plurality of attention image elements. The attention area forming unit 36 repeats the process of expanding each of the concave element S1, the convex element S2, the concave element S3, and the convex element S4, for example, by one pixel toward the outside several times.

  Thereby, like the example shown in FIG. 6 <2>, about the concave surface element S1, convex surface element S2, concave surface element S3, and convex surface element S4, the elements which adjoin mutually are closely_contact | adhered or partially overlapped and connected. Note that elements that are not connected to other elements by several expansion processes may be removed from the image element of interest.

  Further, the attention area forming unit 36 performs hole filling processing on a plurality of image elements connected by the expansion processing. For example, the region surrounded by the concave element S1, convex element S2, concave element S3, and convex element S4 in FIG. 6 <2> is filled as shown in FIG. 6 <3>. Furthermore, a gap between adjacent elements, for example, a region surrounded by a solid line and a broken line in FIG. 6 <3> may be filled.

  Then, the attention area forming unit 36 performs a contraction process on the area (composite area) obtained by the expansion process and the hole filling process to form the attention area. The attention area forming unit 36, for example, repeats the process of contracting the area surrounded by the solid line in FIG. 6 <3> inward by, for example, one pixel at a time several times, thereby generating the tumor area shown in FIG. 6 <4>. Form. For example, the contraction process is repeated the same number of times as the expansion process.

  In FIG. 6 <3>, when the region surrounded by the solid line and the broken line is also subjected to the filling process, the region surrounded by the solid line and the broken line is also included in the tumor region in FIG. 6 <4>.

  The region-of-interest setting unit 30 may use the tumor region formed by the region-of-interest forming unit 36 as a region of interest (ROI) of the tumor. It is desirable to set a circular mass ROI based on the region.

  FIG. 7 is a diagram for explaining a specific example of setting a circular tumor ROI. In setting the circular tumor ROI, first, as shown in FIG. 7 <1>, the boundary of the living tissue is detected in the ultrasonic image including the tumor, for example, in the B-mode image. In detecting the boundary, any known technique may be used, for example, detecting as a boundary a portion where the spatial differential value of the pixel value (luminance) in the ultrasonic image is greater than or equal to a threshold value. In the specific example of FIG. 7 <1>, the boundaries B1 to B6 are detected.

  Next, as shown in FIG. 7 <2>, a tumor region (broken line) is set in the ultrasonic image. As described above, the tumor region is formed by the attention region forming unit 36 (see FIG. 6). Then, the boundary is cleaned in the tumor region. For example, in the specific example shown in FIG. 7 <2>, the boundaries B4 to B6 in the broken line tumor region are removed. That is, the boundaries B4 to B6 are not recognized as boundaries in the subsequent processing.

  Furthermore, the region-of-interest setting unit 30 sets a circular tumor ROI by a known method described in detail in Patent Document 1, for example, in the ultrasonic image from which the boundary in the tumor region is removed. For example, as shown in FIG. 7 <3>, the largest possible tumor ROI that does not include the remaining boundaries B1 to B3 around the designated point U set by the user is set. In the specific example of FIG. 7 <3>, a circular tumor ROI inscribed in the boundary B3 that is closest to the designated point U is set. The designated point A designated in FIG. 4 may be used as the designated point U in FIG. 6, or the designated point U in FIG. 6 may be set separately from the designated point A in FIG. 4.

  In addition, the region-of-interest setting unit 30 does not use the designated point U as the center of the tumor ROI as it is, but, for example, in the vicinity of the designated point U by a known method detailed in Patent Document 1, a maximally large circular tumor. You may search the center point used as ROI. For example, a candidate point having the longest shortest distance to the boundary B3 is searched from among a plurality of candidate points located in the vicinity of the designated point U in FIG. 7 <3>, thereby, for example, shown in FIG. 7 <4>. As a specific example, a circular tumor ROI inscribed in the boundary B3 may be set with the found candidate point as the center point U ′.

  In this way, the circular region ROI is set by the region-of-interest setting unit 30. On the other hand, the fat ROI is, for example, that a user such as a doctor visually confirms the ultrasonic image displayed on the display unit 52, and the center position and the radius of the fat ROI are large in the ultrasonic image. It is set by adjusting the height. The fat ROI may be set using a known technique. For example, by using the technique described in Patent Document 1, by specifying the tissue boundary based on the luminance value of the pixel in the ultrasound image, the fat ROI is reduced while reducing the burden on the user operation. It may be set.

  The ultrasonic diagnostic apparatus suitable for implementing the present invention has been described above. For example, at least one of the image element extraction unit 32, the image element selection unit 34, and the attention area formation unit 36 shown in FIG. And the computer may function as an ultrasonic image processing apparatus.

  The above-described embodiments are merely examples in all respects, and do not limit the scope of the present invention. The present invention includes various modifications without departing from the essence thereof.

  DESCRIPTION OF SYMBOLS 10 Probe, 12 Transmission / reception part, 20 Image formation part, 30 Region of interest setting part, 32 Image element extraction part, 34 Image element selection part, 36 Area of interest formation part, 42 Displacement measurement part, 44 Elasticity information calculation part, 50 Display processing Part, 52 display part, 60 operation device, 70 control part.

Claims (10)

An image forming unit that forms an ultrasonic image based on a signal obtained by transmitting and receiving ultrasonic waves;
An image element extraction unit that extracts a plurality of image elements corresponding to a predetermined shape in the ultrasonic image based on the shape of the spatial distribution of pixel values in the ultrasonic image;
An image element selection unit that selects a plurality of target image elements that are candidates for a target region from a plurality of image elements based on a statistical feature amount of a pixel value in each image element;
An attention area forming unit that forms an attention area corresponding to an attention site based on a plurality of attention image elements;
Having
An ultrasonic diagnostic apparatus. The ultrasonic diagnostic apparatus according to claim 1,
The image element extraction unit extracts a plurality of image elements using a shape index obtained by analyzing a spatial distribution of pixel values in an ultrasonic image.
An ultrasonic diagnostic apparatus. The ultrasonic diagnostic apparatus according to claim 2,
The image element extraction unit obtains an index indicating the degree of spatial unevenness of pixel values related to the target pixel and neighboring pixels located in the vicinity thereof, and uses the index to obtain a plurality of images corresponding to the concave surface. Extract multiple image elements corresponding to elements and convex surface,
An ultrasonic diagnostic apparatus. The ultrasonic diagnostic apparatus according to any one of claims 1 to 3,
The image element selection unit, for the reference image element selected as the image element corresponding to the region of interest and the plurality of other image elements, the statistical feature amount of the pixel value in the reference image element and the other image elements Selecting a plurality of target image elements from a plurality of image elements including a reference image element according to a selection condition based on a statistical feature amount of a pixel value in
An ultrasonic diagnostic apparatus. The ultrasonic diagnostic apparatus according to claim 4,
The image element selection unit includes a plurality of other image elements according to a selection condition for comparing a threshold value based on an average value of pixel values in a reference image element and an average value of pixel values in other image elements. A plurality of image elements that are a plurality of image elements and a reference image element that meet a selection condition of
An ultrasonic diagnostic apparatus. The ultrasonic diagnostic apparatus according to any one of claims 1 to 5,
The attention area forming unit forms an attention area by connecting at least some of the plurality of attention image elements to each other.
An ultrasonic diagnostic apparatus. The ultrasonic diagnostic apparatus according to claim 6,
The attention area forming unit performs expansion processing on each of a plurality of attention image elements, and includes several attention image elements connected to each other by the expansion processing, and an area surrounded by the several attention image elements. , To form a region of interest based on the synthesized region
An ultrasonic diagnostic apparatus. The ultrasonic diagnostic apparatus according to claim 7,
The attention area forming unit forms an attention area by performing shrinkage processing on the combined area.
An ultrasonic diagnostic apparatus. The ultrasonic diagnostic apparatus according to any one of claims 1 to 8,
The image forming unit forms an ultrasonic image including a tumor that is the target site,
The image element extraction unit obtains an index indicating the degree of spatial unevenness of pixel values in an ultrasonic image, and uses the index to correspond to a plurality of image elements corresponding to a concave surface and a convex surface. Extract multiple image elements,
The image element selection unit selects, as a plurality of attention image elements, a concave reference image element selected as an image element corresponding to a tumor, and some image elements among a plurality of other image elements,
The attention area forming unit forms a tumor area as the attention area by connecting at least some of the plurality of attention image elements to each other.
An ultrasonic diagnostic apparatus. An image element extraction unit that extracts a plurality of image elements corresponding to a predetermined shape in the ultrasonic image based on the shape of the spatial distribution of pixel values in the ultrasonic image;
An image element selection unit that selects a plurality of target image elements that are candidates for a target region from a plurality of image elements based on a statistical feature amount of a pixel value in each image element;
An attention area forming unit that forms an attention area corresponding to an attention site based on a plurality of attention image elements;
Having
An ultrasonic image processing apparatus.

Images