Ultrasound diagnostic system and ultrasound image processing method

Abstract

To simplify adjustment of image quality in image processing related to ultrasonic diagnosis.SOLUTION: An ultrasonic diagnostic system includes an image processing unit, an adjustment unit, and a composition unit. The image processing unit generates two or more derivative images derived from image processing for an ultrasonic image on a subject. The adjustment unit generates two or more post-adjustment derivative images by applying a variable coefficient value to each of the two or more derivative images. The composition unit generates a composite image of the ultrasonic image and the two or more post-adjustment derivative images.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] SUMMARY OF THE DISCLOSURE The embodiments disclosed herein and in the drawings relate to ultrasound diagnostic systems and ultrasound image processing methods. [Background technology]

[0002] One of the image processing techniques for ultrasound diagnosis is to decompose an ultrasound image into multiple resolutions, apply a nonlinear anisotropic diffusion filter or a coherence enhancing diffusion (CED) filter to each decomposed image, and use the "edge information" generated during the filter processing to control the multiple resolution high frequency signal. In this technique, the edge information (spatial map showing tissue boundaries) at each layer is also used to determine whether the area is one where noise or speckle should be reduced, or one where smoothing along the tissue boundaries or boundary emphasis should be performed.

[0003] The nonlinear anisotropic diffusion filter used in this technology has several parameters that control the strength of the filter and the degree of edge detection that depend on the direction of the tissue boundary, and since there are as many of these as there are layers of multi-resolution decomposition, the number of parameters tends to be large. Although the large number of parameters allows the image quality designer to finely set the image quality of the filter, it is difficult to quickly achieve the desired image quality unless the user is skilled in handling the filter. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2009-153918 A [Patent Document 2] JP 2009-233408 A Summary of the Invention [Problem to be solved by the invention]

[0005] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to simplify image quality adjustment in image processing related to ultrasound diagnosis. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0006] An ultrasound diagnostic system according to an embodiment includes an image processing unit, an adjustment unit, and a synthesis unit. The image processing unit generates two or more derived images derived from image processing of an ultrasound image related to a subject. The adjustment unit applies a variable coefficient value to each of the two or more derived images to generate two or more adjusted derived images. The synthesis unit generates a synthesis image of the ultrasound image and the two or more adjusted derived images. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of an ultrasound diagnostic system according to the first embodiment. [Diagram 2] FIG. 2 is a diagram showing a typical flow of a nonlinear image filter by the image processing function of the image processing circuit according to the first embodiment. [Diagram 3] FIG. 3 is a diagram showing a typical flow of nonlinear anisotropic diffusion filtering by the image processing circuit according to the first embodiment. [Figure 4] FIG. 4 is a diagram illustrating a simplified image filter by the image processing circuit according to the first embodiment. [Diagram 5] FIG. 5 is a diagram showing an example of a parameter setting screen according to the application example 1. As shown in FIG. [Figure 6] FIG. 6 is a diagram showing an example of another parameter setting screen according to the application example 1. In FIG. [Figure 7] FIG. 7 is a diagram showing an example of another parameter setting screen according to the application example 1. In FIG. [Figure 8]FIG. 8 is a diagram showing a schematic diagram of the transition of the setting values ​​of the image quality adjustment parameters α1 and α2 according to the depth position. [Figure 9] FIG. 9 is a diagram illustrating a simplified image filter according to the third application example. [Figure 10] FIG. 10 is a diagram illustrating a simplified image filter according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, embodiments of an ultrasound diagnostic system and an ultrasound image processing method will be described in detail with reference to the drawings.

[0009] (First embodiment) Fig. 1 is a diagram showing an example of the configuration of an ultrasound diagnostic system 1 according to the first embodiment. As shown in Fig. 1, the ultrasound diagnostic system 1 includes an ultrasound probe 11, a transmission / reception circuit 12, a B-mode processing circuit 13, a Doppler processing circuit 14, an image processing circuit 15, a display device 16, a storage device 17, a control circuit 18, and an input device 19.

[0010] The ultrasonic probe 11 is a device (probe) responsible for transmitting and receiving ultrasonic waves irradiated and reflected between the ultrasonic probe 11 and the subject, and is formed of an electromechanical reversible conversion element. The ultrasonic probe 11 is, for example, a phased array type having a plurality of elements arranged in an array at its tip. As a result, the ultrasonic probe 11 converts the pulse drive voltage of the drive signal supplied thereto into an ultrasonic pulse signal, transmits it in a desired direction within the scan area of ​​the subject, and converts the ultrasonic signal reflected from the subject into an echo signal of a corresponding voltage.

[0011] With regard to ultrasonic signal transmission, the transmission / reception circuit 12 supplies a drive signal to the ultrasonic probe 11. Specifically, the transmission / reception circuit 12 has a trigger generation circuit, a delay circuit, a pulser circuit, and the like. The pulser circuit repeatedly generates rate pulses for forming a transmission ultrasonic wave at a predetermined rate frequency. The delay circuit provides each rate pulse generated by the pulser circuit with a delay time for each piezoelectric transducer required for focusing the ultrasonic waves generated from the ultrasonic probe 11 into a beam shape and determining the transmission directivity. The trigger generation circuit applies a drive signal (drive pulse) to the ultrasonic probe 11 at a timing based on the rate pulse. That is, the delay circuit arbitrarily adjusts the transmission direction from the piezoelectric transducer surface by changing the delay time provided to each rate pulse.

[0012] The transmission / reception circuit 12 has a function capable of instantaneously changing the transmission frequency, transmission drive voltage, etc., in order to execute a predetermined scan sequence based on instructions from the control circuit 18. In particular, the change in the transmission drive voltage is realized by a transmission circuit capable of instantaneously switching its value, or a mechanism for electrically switching between multiple power supply units.

[0013] Regarding ultrasonic signal reception, the transmission / reception circuit 12 performs various processes on the echo signal corresponding to the reflected wave signal received by the ultrasonic probe 11, and converts the echo signal into reflected wave data corresponding to the reception directivity. Specifically, the transmission / reception circuit 12 has an amplifier circuit, an A / D converter, an adder, etc. The amplifier circuit amplifies the reflected wave signal for each channel and performs gain correction processing. The A / D converter A / D converts the gain-corrected reflected wave signal and gives the digital data a delay time required to determine the reception directivity. The adder performs addition processing of the reflected wave signal processed by the A / D converter to generate reflected wave data. The addition processing of the adder emphasizes the reflected component from the direction corresponding to the reception directivity of the reflected wave signal.

[0014] The B-mode processing circuit 13 performs logarithmic amplification, envelope detection processing, logarithmic compression, etc. on the reflected wave data from the transmission / reception circuit 12, and generates B-mode information in which the signal intensity of each of the multiple sample points is expressed as luminance brightness.

[0015] The Doppler processing circuit 14 performs color Doppler on the reflected wave data from the transmission / reception circuit 12 to calculate blood flow information, i.e., Doppler information. In the color Doppler method, ultrasonic waves are transmitted and received multiple times on the same scanning line, and a moving target indicator (MTI) filter is applied to the data string at the same position to suppress signals (clutter signals) originating from stationary or slow-moving tissues and extract signals originating from blood flow. In the color Doppler method, Doppler information such as the blood flow speed, blood flow dispersion, and blood flow power are estimated from the blood flow signal.

[0016] The image processing circuit 15 is a processor that performs image processing. The image processing circuit 15 executes a program stored in the storage device 17 to realize a function corresponding to the program. The image processing circuit 15 realizes, for example, an image generation function 151, an image processing function 152, an adjustment function 153, a composition function 154, and a display control function 155. The image generation function 151, the image processing function 152, the adjustment function 153, the composition function 154, and the display control function 155 do not need to be realized by a single image processing circuit 15, and may be realized by a plurality of image processing circuits 15 in a shared manner. The image generation function 151, the image processing function 152, the adjustment function 153, the composition function 154, and / or the display control function 155 may be implemented as hardware, not as a program.

[0017] By implementing the image generating function 151, the image processing circuit 15 converts (scan converts) the scanning method of the B-mode information into a scanning method suitable for display, and generates a B-mode image of the subject. Similarly, the image processing circuit 15 scan converts the scanning method of the Doppler information into a scanning method suitable for display, and generates a Doppler image of the subject. Display images such as B-mode images and Doppler images are collectively called ultrasound images. The image processing circuit 15 generates, together with the ultrasound image, information indicating the combination or juxtaposition of each image information, the display position, various information for assisting the operation of the ultrasound diagnostic system 1, and supplementary information necessary for ultrasound diagnosis such as patient information.

[0018] By implementing the image processing function 152, the image processing circuit 15 generates two or more derived images derived from the image processing on the ultrasound image generated by the image generation function 151. More specifically, the image processing circuit 15 generates two or more derived images representing two or more image features processed by applying the image processing to the ultrasound image, based on a first output image generated by applying the image processing to the ultrasound image, a second output image generated by applying the image processing to the ultrasound image when a parameter used in the image processing is set to a predetermined value, and the ultrasound image. The image processing is nonlinear image processing, and is image processing for improving image quality by reducing noise or speckle contained in the ultrasound image, smoothing along tissue boundaries, and emphasizing tissue boundaries. As the image processing, a nonlinear image filter using a diffusion equation is executed. The above parameters are parameters related to the diffusion tensor of the diffusion equation. In the first embodiment, the image processing circuit 15 applies a nonlinear image filter to the ultrasound image to generate two or more derived images.

[0019] By implementing the adjustment function 153, the image processing circuit 15 generates two or more adjusted derived images by applying a variable coefficient value to each of the two or more derived images generated by the image processing function 152. The derived images to which the coefficient values ​​have been applied are called adjusted derived images.

[0020] By implementing the synthesis function 154 , the image processing circuit 15 generates a synthesis image of the ultrasound image that is the processing result of the image processing function 152 and two or more adjusted derived images generated by the adjustment function 153 .

[0021] By implementing the display control function 155, the image processing circuit 15 outputs various information via the display device 16. For example, the image processing circuit 15 displays a composite image generated by the composition function 154 on the display device 16.

[0022] The display device 16 is a display device that converts display information from the image processing circuit 15 into visual video information and displays it in cooperation with the image processing circuit 15. For example, the display device 16 displays a composite image generated by the image processing circuit 15. As the display device 16, for example, a CRT display, a liquid crystal display, an organic EL display, a plasma display, etc. may be used. Note that a projector may be provided as the display device 16.

[0023] The storage device 17 is a storage device such as a ROM (Read Only Memory), a RAM (Random Access Memory), an HDD (Hard Disk Drive), an SSD (Solid State Drive), an integrated circuit storage device, etc. that stores various information. The storage device 17 may also be a drive device that reads and writes various information from and to a portable storage medium such as a CD-ROM drive, a DVD drive, or a flash memory. For example, the storage device 17 stores various information such as B-mode information, Doppler information, a B-mode image, a Doppler image, a composite image, etc.

[0024] The control circuit 18 is a processor that controls the overall processing of the ultrasound diagnostic system 1. The control circuit 18 executes a program stored in the storage device 17 to realize a function corresponding to the program. Specifically, the control circuit 18 controls the processing of the transmission / reception circuit 12, the B-mode processing circuit 13, the Doppler processing circuit 14, and the image processing circuit 15 based on various setting requests, various control programs, and various data input by an operator via an input device 19. The control circuit 18 further includes an interface function with the input device 19.

[0025] The input device 19 is various user interfaces on a touch panel or an operation panel. An operator can input various operations and commands to the ultrasound diagnostic system 1 through the input device 19. The display device 16 and the input device 19 do not need to be separate, and the two may be mechanically integrated.

[0026] The transmission / reception circuit 12, the B-mode processing circuit 13, the Doppler processing circuit 14, the image processing circuit 15, the display device 16, the storage device 17, the control circuit 18, and the input device 19 are implemented in a single housing also called the device body, and the ultrasound probe 11 is detachably connected to the device body via a cable. The hardware configuration of the ultrasound diagnostic system 1 is not limited to this. For example, some or all of the functions of the transmission / reception circuit 12, the B-mode processing circuit 13, the Doppler processing circuit 14, the image processing circuit 15, the display device 16, the storage device 17, the control circuit 18, and the input device 19 may be implemented in the ultrasound probe 11. Some or all of the functions of the image processing circuit 15, the display device 16, and the storage device 17 may be implemented in a computer connected to the device body via a network. In addition, the image processing circuit 15 and the control circuit 18 do not need to be implemented in separate hardware, and may be implemented in a single hardware.

[0027] Next, details of the processing of the image processing circuit 15 according to the first embodiment will be described. The image processing circuit 15 can execute a nonlinear anisotropic diffusion filter or a coherence weighted diffusion filter as an example of a nonlinear image filter. These nonlinear image filters reduce noise or speckle contained in the ultrasound image, smooth along tissue boundaries, and emphasize the tissue boundaries.

[0028] First, the nonlinear image filter will be described in detail. Hereinafter, the nonlinear image filter is assumed to execute a nonlinear anisotropic diffusion filter as an example. Also, it is assumed that the ultrasound image to which the nonlinear image filter is applied is a B-mode image. The B-mode image to which the nonlinear image filter is applied may be an image before scan conversion by the image processing circuit 15, or an image after scan conversion. Also, this B-mode image may be an image to which gain adjustment according to the depth position such as TGC (Time Gain Control) has been applied, or an image to which gain adjustment has not been applied.

[0029] 2 is a diagram showing a typical flow of the nonlinear image filter 200A by the image processing function 152 of the image processing circuit 15. The nonlinear image filter 200A has a multi-layer structure consisting of multiple layers in order to perform multi-resolution decomposition / reconstruction. In this embodiment, the highest order of the multi-resolution decomposition / reconstruction is level 3. Note that the highest order is not limited to level 3, and is not particularly limited as long as it is 2 or more.

[0030] The nonlinear image filter 200A has multi-resolution decomposition processes 211, 221, 231, nonlinear anisotropic diffusion filter processes 213, 223, 233, high-frequency level control processes 212, 222, 232, and multi-resolution reconstruction processes 214, 224, 234 for each level.

[0031] The multi-resolution decomposition processes 211, 221, and 231 at each level perform multi-resolution decomposition on the input image. The multi-resolution decomposition processes 211, 221, and 231 can use various methods such as discrete wavelet transform and Laplacian pyramid method. As a result of the multi-resolution decomposition of a two-dimensional image, the decomposed image is divided into low-pass (LL), horizontal high-pass (LH), vertical high-pass (HL), and diagonal high-pass (HH) images, each of which has half the length (number of pixels) of the original image.

[0032] The level 1 multi-resolution decomposition process 211 applies multi-resolution decomposition processing to the B-mode image generated by the image generation function 151 to generate a low-pass image, a horizontal high-pass image, a vertical high-pass image, and a diagonal high-pass image of level 1. The level 2 and level 3 multi-resolution decomposition processes 221, 231 apply multi-resolution decomposition processing to the low-pass images generated by the multi-resolution decomposition processes 211, 221 of the previous layer to generate a low-pass image, a horizontal high-pass image, a vertical high-pass image, and a diagonal high-pass image of the same level.

[0033] The nonlinear anisotropic diffusion filter processes 213, 223, 233 at each level apply a nonlinear anisotropic diffusion filter to the low-pass image generated in the multiresolution decomposition processes 211, 221, 231 at the same level to generate a filtered low-pass image. The nonlinear anisotropic diffusion filter processes 213, 223, 233 also output edge information based on the low-pass image. The edge information is information regarding the size and direction of an edge.

[0034] Here, the nonlinear anisotropic diffusion filter will be described in detail. The nonlinear anisotropic diffusion filter is expressed by the following partial differential equation (1).

[0035]

number

[0036] Here, I is the pixel value of the image to be processed, ∇I is its gradient vector, and t is the time related to the processing. In actual processing, t indicates the number of times this diffusion equation is processed. In this embodiment, the number of times t may be any number of times, but for the sake of concrete explanation, it is assumed to be one time.

[0037] D in equation (1) is the diffusion tensor, and is expressed as the following equation (2).

[0038]

number

[0039] (2) λ1 and λ D2 are the eigenvalues ​​of the diffusion tensor D, and R is the eigenvector of the diffusion tensor D. R is a rotation matrix. Based on the eigenvectors ω1 and ω2 of the diffusion tensor D, it is expressed as R = (ω1, ω2).

[0040] The diffusion tensor D is calculated by multiplying the gradient vector of each pixel by a specific direction and a perpendicular direction by coefficients c1 and c2, respectively. The specific direction is the direction of the edge of a structure such as tissue depicted in the image, and the coefficient depends on the size of the edge.

[0041] To find the edge magnitude and orientation, the structure tensor of the image is calculated and its eigenvalues ​​and eigenvectors are calculated. The eigenvalues ​​are related to the edge magnitude, and the eigenvectors represent the edge orientation.

[0042] The structure tensor S is expressed by the following equation (3).

[0043]

number

[0044] I x is the spatial derivative of image I in the x direction (horizontal direction), and Iy represents the spatial derivative of image I in the y direction (vertical direction). Gρ is a two-dimensional Gaussian function, and the operator "*" represents convolution. The eigenvalues ​​μ1 and μ2 are the first and second eigenvalues ​​of the two-dimensional structure tensor S, respectively. R is a rotation matrix consisting of the eigenvectors of the structure tensor S.

[0045] The edge information of the structure tensor S is used to calculate the diffusion tensor D. First, the edge size E depends on the difference between the first eigenvalue μ1 and the second eigenvalue μ2, and is calculated, for example, according to the following formula (4).

[0046]

number

[0047] The parameter k is a parameter that indicates the degree of extraction of edge components. The parameter k can be arbitrarily set by the user via the input device 19, etc. For example, when the parameter k is made smaller, the edge components are more easily extracted.

[0048] Furthermore, the coefficient c1 used in the diffusion tensor D becomes a function f1 of the edge size E according to the following equation (5), and the coefficient c2 becomes a function f2 of the edge size E according to the following equation (6).

[0049]

number

[0050] The direction of the edge corresponds to the rotation matrix R. Based on the coefficients c1 and c2 and the rotation matrix R, each element value d of the diffusion tensor D is calculated according to the above formula (2). 11 , d 12 and d 22 is calculated.

[0051] The calculation of the edge size and direction does not necessarily have to strictly follow the above method. x and I yInstead of calculating the above, a Sobel filter, a Gabor filter, or high-frequency components of multi-resolution decomposition may be applied.

[0052] Incidentally, since equations (5) and (6) are actually linear polynomials of the edge magnitude E, about four parameters are required to control the coefficients c1 and c2, respectively.

[0053] The nonlinear anisotropic diffusion filter is calculated by a numerical analysis solution of a partial differential equation according to the above formula (1). That is, at time t, the pixel values ​​of a certain pixel and its surrounding eight pixels, and the element values ​​d 11 , d 12 and d 22 Based on this, a new pixel value of that point at time t+Δt is calculated, and then t+Δt is set as the new t, and the same calculation is repeated one or several times.

[0054] 3 is a diagram showing a typical flow of the nonlinear anisotropic diffusion filter processes 213, 223, and 233 performed by the image processing circuit 15. Note that the processes of steps 301 to 305 are performed for each pixel constituting the low-pass image to be processed.

[0055] As shown in FIG. 3, the image processing circuit 15 first calculates a differential value I x and the differential value I y (Step 301). x and I y Then, the image processing circuit 15 calculates the differential value I x and I y and the two-dimensional Gaussian function G ρ and convolve the element s of the structure tensor S. 11 , s 12 and s 22 (Step 302). In the calculation in step S2, a two-dimensional Gaussian function G ρ This also includes the calculation of

[0056] Element s of the structure tensor S 11 , s 12 and s 22 When is calculated, the image processing circuit 15 calculates the calculated element s 11 , s 12 and s 22 A first eigenvalue μ1 and a second eigenvalue μ2 of the two-dimensional structure tensor S are calculated by linear algebraic operation, and an edge magnitude E is calculated based on the first eigenvalue μ1 and the second eigenvalue μ2 according to equation (4) (step 303). The edge magnitude E is used in the high-frequency level control processes 212, 222, and 232. In addition, a rotation matrix R of the two-dimensional structure tensor S, i.e., the edge direction, is also calculated according to equation (3).

[0057] In addition, the image processing circuit 15 calculates the element s of the structure tensor S. 11 , s 12 and s 22 (step 304). For example, the image processing circuit 15 calculates coefficients c1 and c2 according to equations (5) and (6), and calculates each element value d of the diffusion tensor D based on the coefficients c1, c2 and the rotation matrix R according to equation (2). 11 , d 12 and d 22 For the sake of efficiency, the edge size E may be used in the calculation. After that, the image processing circuit 15 performs a numerical analysis calculation of the partial differential equation (step 305). Specifically, the image processing circuit 15 calculates the element value d 11 , d 12 and d 22 and the differential value I x and I y Based on this, the partial differential equation (1) is numerically analyzed to calculate the output pixel value. At time t, a new pixel value of the processing target pixel at time t+Δt is calculated from the pixel values ​​of the processing target pixel and its neighboring voxels and each element value of the diffusion tensor. Next, t+Δt is set as new t, and the same calculation is repeated one or several times. The calculated pixel value is used in the multi-resolution reconstruction processes 214, 224, and 234.

[0058] After step 305 is performed, the pixel to be processed is changed and steps 301 to 305 are performed again. When steps 301 to 305 are performed in this manner for all pixels constituting the image to be processed, the nonlinear anisotropic diffusion filter processes 213, 223, and 233 by the image processing circuit 15 are completed.

[0059] Returning to FIG. 2, the high-frequency level control processes 212, 222, and 232 and the multi-resolution reconstruction processes 214, 224, and 234 will now be described.

[0060] The high-pass level control processes 212, 222, and 232 at each level control pixel values ​​of three high-pass images generated by the multi-resolution decomposition processes 211, 221, and 231 at the same level using edge information from the nonlinear anisotropic diffusion filter processes 213, 223, and 233 at the same level. The edge information is a normalized edge size based on the eigenvalue of the structure tensor. The high-pass level control processes 212, 222, and 232 calculate an integrated value for each pixel between the edge information and each high-pass image, and further multiply the calculated value by the control coefficient of each high-pass image. As another example of controlling pixel values, a threshold value may be set for the edge size, and anything above the threshold value may be regarded as an edge, and the control coefficient of each high-pass image may be applied to areas other than the edge. The three high-pass images processed in this way are used for the multi-resolution reconstruction processes 214, 224, and 234.

[0061] The multi-resolution reconstruction processes 214, 224, and 234 at each level generate one composite image based on one low-pass image processing circuit from the nonlinear anisotropic diffusion filter processes 213, 223, and 233 at the same level and three high-pass images from the high-pass level control processes 212, 222, and 232 at the same level. The length and width of the composite image are twice the length and width of the low-pass and high-pass images used.

[0062] The composite image output by the level 3 multi-resolution reconstruction process 234 is input to the level 2 nonlinear anisotropic diffusion filter process 223, where it is subjected to the same filtering process as in level 3, and then input as a low-pass image to the multi-resolution reconstruction process 224. Meanwhile, the high-pass image output by the level 2 multi-resolution decomposition process 221 is subjected to the same high-pass level control as in level 3 in the level 2 high-pass level control process 222, and then input as a high-pass image to the level 2 multi-resolution reconstruction process 224. The level 2 multi-resolution reconstruction process 224, like level 3, forms one composite image from one low-pass image and three high-pass images.

[0063] The processing of level 1 is similar to that of level 2. That is, the final composite image, that is, the resultant image, is obtained by the nonlinear anisotropic diffusion filter processing 213, the high-pass level control processing 212, and the multi-resolution reconstruction processing 214 of level 1.

[0064] This completes the description of the nonlinear image filter executed by the image processing function 152 of the image processing circuit 15.

[0065] As described above, the nonlinear anisotropic diffusion filter has several parameters that control the strength of the filter and the degree of edge detection that depend on the direction of the tissue boundary, and since there are the parameters for the number of layers of multiresolution decomposition, the number of parameters tends to be large. Although the large number of parameters allows the image quality designer to set the image quality of the filter in detail, it is difficult to quickly achieve the desired image quality unless the user is skilled in handling the filter.

[0066] However, as an exception, it is possible to adjust the filter strength of the entire image by changing the composition ratio of the images before and after processing, which provides the operator with a means of adjusting the filter strength, but it is not possible to make finer changes than that, such as changing the filter strength only at tissue boundaries.

[0067] Furthermore, the nonlinear anisotropic diffusion filter is a process that solves partial differential equations numerically, and requires iterative calculations to obtain high-quality results with strong filtering. However, repeated iterations of the calculations require a considerable amount of time.

[0068] The image processing circuit 15 according to this embodiment reduces the number of parameters for adjusting image quality (hereinafter referred to as image quality adjustment parameters) to a small number compared to a nonlinear image filter, and fine-tunes desired features among image features that can be processed by the nonlinear image filter, thereby making it possible to easily and quickly obtain desired image quality. Hereinafter, this processing will be referred to as a simplified image filter. Note that the image quality adjustment parameters are an example of coefficient values ​​applied to a derived image.

[0069] 4 is a diagram showing a simplified image filter by the image processing circuit 15. As shown in FIG. 4, the image processing circuit 15 executes a nonlinear image filter 200A as the nonlinear image filter by implementing the image processing function 152. The nonlinear image filter 200A has the same basic processing procedure as the nonlinear image filter 200 in FIG. 2, but an operation for obtaining a first derived image D1 and a second derived image D2 is added. The first derived image D1 and the second derived image D2 are obtained by multiplying an input image I in is an image representative of two or more image features to be processed by applying a nonlinear image filter 200 to the input image I in A first output image I is generated by applying a nonlinear image filter 200 to out When the parameters used in the nonlinear image filter 200A are set to predetermined values, the input image I in A second output image generated by applying a nonlinear image filter 200A to the input image I in These parameters are different from image quality adjustment parameters and are parameters that are normally used in nonlinear image filter 200A. Hereinafter, these parameters will be referred to as filter parameters.

[0070] Here, the input image I inis a B-mode image input to the nonlinear image filter 200A. The two or more image features processed by applying the nonlinear image filter 200 include, for example, smoothing of tissue boundaries (tissue boundaries in the edge direction) and tissue parenchyma, enhancement of tissue boundaries (tissue boundaries in the direction perpendicular to the edge), and reduction (or smoothing) of noise or speckle. The filter parameters include, for example, the edge magnitude, edge direction, element s of the structure tensor S, 11 , s 12 and s 22 , the differential value I x and I y , the eigenvalues ​​μ 1 and μ 2 , the parameter k, or any other parameter used in the nonlinear image filter 200 .

[0071] A procedure for generating the first derived image D1 and the second derived image D2 will be specifically described. The image processing circuit 15 applies a nonlinear image filter 200A to an ultrasound image (B-mode image) to generate a resultant image, that is, a normal output image I out Generates a normal output image I out The edge size E when generating the derived image is generated in step 303 shown in FIG. 3. Before obtaining each derived image, the image processing circuit 15 performs the normal output image I out In addition to the above, an output image I0 is generated when the edge size E is set to zero. Specifically, the image processing circuit 15 calculates the first eigenvalue μ1 and the second eigenvalue μ2 when the edge size E shown in the above formula (4) is set to 0, and also calculates the coefficients c1 and c2 according to the above formulas (5) and (6). The image processing circuit 15 then calculates a partial differential equation according to the above formula (1) based on the first eigenvalue μ1, the second eigenvalue μ2, and the coefficients c1 and c2, to generate an output image I0. The edge sizes used in the nonlinear anisotropic image filters 213, 223, and 233 of all levels may be set to zero, but it is sufficient that the edge size used in at least the nonlinear anisotropic image filter 213 of level 1 is set to zero. The output image I0 corresponds to an image resulting from application of smoothing that does not take into account tissue boundaries.

[0072] The image processing circuit 15 calculates the output image I0 and the input image I according to the following equation (7).in A first derived image D1 is generated as a difference image between the first and second derived images, and the output image I out A second derived image D2 is generated as a difference image between the output image I0 and the input image I1. in The first derived image D1 is a differential image between the output image I and the first derived image D2, and contains image components for smoothing. In other words, the first derived image D1 can be said to be an image representative of the smoothing of tissue structures and the like contained in the ultrasound image, which are image features processed by the nonlinear image filter 200A. out The difference image between the output image I0 and the output image I1 contains image components for enhancing the tissue boundary. 2 Derived Images D 2 is an image representative of the enhancement of the boundaries of tissue structures contained in an ultrasound image, which is an image feature processed by the nonlinear image filter 200A.

[0073]

number

[0074] From the above equations (7) and (8), the output image I out is expressed by the following equation (9).

[0075]

number

[0076] When the nonlinear image filter 200A is executed, the image processing circuit 15 executes a first adjustment process 401 and a second adjustment process 402 by implementing the adjustment function 153. In the first adjustment process 401, the image processing circuit 15 multiplies the first derived image D1 by the image quality adjustment parameter α1 to generate an adjusted first derived image α1D1. In the second adjustment process 402, the image processing circuit 15 multiplies the second derived image D2 by the image quality adjustment parameter α2 to generate an adjusted second derived image α2D2. The image quality adjustment parameters α1 and α2 are real numbers in the range from 0 to 1. The values ​​of the image quality adjustment parameters α1 and α2 can be adjusted independently of each other. By adjusting the image quality adjustment parameter α1, the texture of the first derived image D1 is enhanced. structure of smoothing The intensity of the image component for can be adjusted, and the image quality adjustment parameter α2 is adjusted to adjust the intensity of the image component for the second derived image D2. Emphasizing organizational boundaries The image quality adjustment parameters α1 and α2 can be individually and arbitrarily adjusted by the operator via the input device 19 or the like.

[0077] After the first adjustment process 401 and the second adjustment process 402 are executed, the image processing circuit 15 executes the synthesis function 154. In the synthesis function 154, the image processing circuit 15 synthesizes the input image I in The adjusted first derived image α1D1 and the adjusted second derived image α2D2 are synthesized to obtain a synthesized image I′ out The image processing circuit 15 generates an input image I in The adjusted first derived image α1D1 and the adjusted second derived image α2D2 are added together to obtain a composite image I′ out Generate.

[0078]

number

[0079] The synthesis method is not limited to addition, but may be various other methods such as multiplication, inversion multiplication, and the like.

[0080] Composite image I´ out When the composite image I' is generated, the simplified image filter by the image processing circuit 15 is completed. After that, the image processing circuit 15 executes the display control function 155 to generate the composite image I' out The image is displayed on the display device 16. At this time, the image processing circuit 15 displays the composite image I' out As well as the input image I in and / or the first output image I out may be displayed side by side, overlapping, or switchably.

[0081] The simplified image filter is thus completed. The simplified image filter described above is merely an example, and is not limited thereto. For example, in the above process, the derived image is a difference image of two different nonlinear image processes, but is not limited thereto, and may be an image in which the sum of pixel values ​​or numerical analysis values ​​of pixel values ​​in a spatially global image range is approximately zero. For example, a differential value of pixel values ​​is adopted as the numerical analysis value. In this regard, the derived image may be an added image or a multiplied image of nonlinear image processes, etc.

[0082] In the above processing example, the first derivative image is the output image I0 of the nonlinear image filter when the edge size is set to zero, and the input image I in The output image I0 of the nonlinear image filter and the input image I1 when the edge size is set to an arbitrary value such as 1 are in and the output image I0 may be a difference image between the two. Furthermore, the derived image may be an image representative of an image feature processed by the nonlinear image filter, that is, the output image I0 may be an output image of the nonlinear image filter when any filter parameter other than the edge size is set to any value. By appropriately selecting the type and setting value of the filter parameter, it is possible to generate any derived image representative of any image feature processed by the nonlinear image filter.

[0083] For example, in the above processing example, the image features of the derived image depend on edge information calculated from the eigenvalues ​​of the structure tensor of the nonlinear anisotropic diffusion filter according to equation (4), but may also depend on the spatial derivative of the image or the difference in pixel position.

[0084] In the above processing example, the nonlinear image filter 200A includes a nonlinear anisotropic diffusion filter as a component, but may include various image filters other than a nonlinear anisotropic diffusion filter, and may include multiple image filters rather than being limited to including a single image filter.

[0085] A nonlinear image filter 200A, which is an example of a nonlinear image filter, applies a nonlinear anisotropic diffusion filter to each level of multi-resolution analysis, as shown in Fig. 2. For example, as shown in equations (4) to (6) and the like, the nonlinear anisotropic diffusion filter itself has many filter parameters for adjusting image quality, and there are as many filter parameter groups as there are levels of multi-resolution analysis, so there are a large number of filter parameters. In this state, it is difficult to quickly achieve a desired image quality.

[0086] As described above, according to this embodiment, instead of adjusting a large number of filter parameters used in a nonlinear image filter such as a nonlinear anisotropic diffusion filter, two or more image quality adjustment parameters corresponding to two or more derived images derived from a nonlinear anisotropic diffusion filter are adjusted. Since a derived image is an image obtained by condensing various image components that are emphasized or reduced by a nonlinear image filter, the image quality adjustment parameters corresponding to the derived image correspond to parameters for adjusting the image components represented by the derived image. For example, since the first derived image D1 represents an image component for smoothing, the image quality adjustment parameter α1 mainly functions as a parameter for adjusting the smoothing strength, and since the second derived image D2 represents an image component for enhancing a tissue boundary, the image quality adjustment parameter α2 mainly functions as a parameter for adjusting the enhancement strength of the tissue boundary. The image quality adjustment parameters α1 and α2 can be said to be meaningful parameters. According to this embodiment, the operator only needs to adjust the image quality adjustment parameter that directly corresponds to a certain image component, so that it is possible to intuitively and simply adjust the image quality. In addition, since the number of image quality adjustment parameters is small, it is possible to easily achieve a desired image quality.

[0087] Various application examples according to the first embodiment will be described below.

[0088] (Application example 1) In the above embodiment, the image quality adjustment parameters α1 and α2 can be set by the operator via the input device 19. The image quality adjustment parameters α1 and α2 according to Application Example 1 can be set via a GUI screen (hereinafter referred to as the parameter setting screen). The parameter setting screen is generated by the display control function 155 of the image processing circuit 15 and displayed on the display device 16. The parameter setting screen is displayed so as to be operable via the input device 19. The parameter setting screen may be displayed on a touch panel in which the display device 16 and the input device 19 are integrated, or may be displayed on the display device 16, such as a display that is physically separated from the input device 19.

[0089] FIG. 5 is a diagram showing an example of a parameter setting screen I1 according to the application example 1. As shown in FIG. 5, the parameter setting screen I1 displays a slider bar I11 for setting the image quality adjustment parameter α1. The slider bar I11 is assigned a value of the image quality adjustment parameter α1, and for example, from the left end to the right end, the image quality adjustment parameter α1 is assigned a lower limit value (e.g., "0") to an upper limit value (e.g., "1") consecutively. The slider bar I11 is provided with a tab I12. The tab I12 is provided so as to be freely movable along the slider bar I11. By placing the tab I12 at an arbitrary position on the slider bar I11 via the input device 19, the image quality adjustment parameter α1 is set to a value corresponding to the arbitrary position. The set value of the image quality adjustment parameter α1 is displayed in a display field I13. In the case of FIG. 5, it can be seen that the set value of the image quality adjustment parameter α1 is set to "0.1". Similarly, for the image quality adjustment parameter α2, a slider bar I14 to which lower and upper limit values ​​of the image quality adjustment parameter α2 are assigned, a tab I15 for setting the value of the image quality adjustment parameter α2, and a display field I16 in which the setting value of the image quality adjustment parameter α2 (in the case of FIG. 5, "0.3") is displayed.

[0090] FIG. 6 is a diagram showing an example of another parameter setting screen I2 according to Application Example 1. As shown in FIG. 6, a slider bar I21 to which a lower limit value to an upper limit value of the image quality adjustment parameter α1 is assigned, a tab I22 for setting the value of the image quality adjustment parameter α1, and a display field I23 for displaying the set value of the image quality adjustment parameter α1 are displayed. The slider bar I21, the tab I22, and the display field I23 are similar to the slider bar I11, the tab I12, and the display field I13 shown in FIG. 5, respectively. In the parameter setting screen I2, an explanatory text such as "smoothing" is displayed for the slider bar I21, the tab I22, and the display field I23 as an explanation of the image quality adjustment parameter α1 to be set. Similarly, an explanatory text such as "boundary emphasis" is displayed for the slider bar I24, the tab I25, and the display field I26 for the image quality adjustment parameter α2.

[0091] Note that the explanation of the image quality adjustment parameter to be set is not limited to text, and pictograms or the like may be displayed.

[0092] As described above, parameter setting screens I1 and I2 are provided with input components (GUI components) such as slider bars and tabs for inputting setting values ​​for each image quality adjustment parameter.

[0093] FIG. 7 is a diagram showing an example of another parameter setting screen I3 according to application example 1. As shown in FIG. 7, the parameter setting screen I3 displays an input component (GUI component) I31 for setting the setting values ​​of both image quality adjustment parameters α1 and α2 with a single operation. The input component I31 is referred to as a setting field. The setting field I31 is a GUI component having a coordinate space with a number of dimensions corresponding to the number of image quality adjustment parameters. In this embodiment, the number of image quality adjustment parameters is "2", so the setting field I31 is a two-dimensional coordinate space. Specifically, in the setting field I31, the horizontal axis is defined as the image quality adjustment parameter α1, and the vertical axis is defined as the image quality adjustment parameter α2, and combinations of the values ​​of the image quality adjustment parameters α1 and α2 are assigned to each coordinate. A lower limit value (e.g., "0") to an upper limit value (e.g., "1") are consecutively assigned to the horizontal axis from the left end to the right end, and a lower limit value (e.g., "0") to an upper limit value (e.g., "1") are consecutively assigned to the vertical axis from the top end to the bottom end. A tab I32 is provided in the setting field I31 so that it can be moved freely. By placing the tab I32 at any position in the setting field I31 via the input device 19, the image quality adjustment parameter α1 and the image quality adjustment parameter α2 are set to values ​​corresponding to that position. The setting value of the image quality adjustment parameter α1 ("0.8" in the example of FIG. 7) is displayed in display field I33, and the setting value of the image quality adjustment parameter α2 ("0.3" in the example of FIG. 7) is displayed in display field I34.

[0094] As described above, according to Application Example 1, it is possible to set image quality adjustment parameters using a GUI screen. By using the GUI screen, the operator can intuitively and easily set image quality adjustment parameters.

[0095] In addition, in Application Example 1, each of GUI components I11-I16, I21-I26, and I31-I34 of each of parameter setting screens I1, I2, and I3 may be a mechanical component provided in input device 19. These mechanical components may be mounted on, for example, an operation panel provided in the main body of ultrasound diagnostic system 1.

[0096] (Application example 2) In the above embodiment, it is assumed that the image quality adjustment parameters have a constant value for all pixels constituting the derived image. Since ultrasonic waves are greatly affected by attenuation in the living body and have frequency-dependent attenuation, the image quality of the shallow and deep parts of the ultrasonic image differs greatly. In addition, some ultrasonic probes 11 generate images that spread out like a fan, and the image quality of the deep parts is also coarse because the scanning line density is coarse in the deep parts, so there is a difference in how the image processing is affected between the shallow and deep parts. Therefore, if the image processing setting is suitable for the shallow parts, the image processing for the deep parts is too strong, while if the setting is suitable for the deep parts, the image processing for the close parts is too weak. In this way, even if a constant value of the image quality adjustment parameters is set for the entire image, there are cases where the effect of a nonlinear image filter such as a nonlinear anisotropic diffusion filter cannot be obtained uniformly over the entire image.

[0097] The image quality adjustment parameters according to Application Example 2 have values ​​corresponding to spatial positions in the derived image. Image processing circuit 15 according to Application Example 2 sets the values ​​of the image quality adjustment parameters in adjustment function 153 according to the spatial positions in the derived image. For example, image processing circuit 15 stores a function that specifies an adjustment rate of the image quality adjustment parameters α1 and α2 that depends on the spatial positions in the derived image. The adjustment rate may be specified as the deviation amount of the image quality adjustment parameter from a reference value or a ratio to the reference rate. The reference value may be set via a parameter setting screen such as those shown in FIGS. 5 to 7 according to Application Example 1.

[0098] The adjustment ratio corrects the difference in frequency-dependent attenuation or scan line density between different spatial positions. Since the influence of frequency-dependent attenuation or scan line density is more pronounced in the acoustic line direction (depth direction) than in the acoustic scan direction of the ultrasound, the image quality adjustment parameters α1 and α2 may be set to depend only on the depth position, as shown in Fig. 8. For example, it is preferable to set the image quality adjustment parameters α1 and α2 to be larger for pixels located deeper.

[0099] The image processing circuit 15 specifies the spatial position of each pixel in the first derived image D1 for the pixel value of the pixel, and applies the pixel value and spatial position of the pixel to the function to calculate an adjustment rate for the image quality adjustment parameter α1 of the pixel. The image processing circuit 15 then multiplies the calculated adjustment rate by a reference value to calculate the value of the image quality adjustment parameter α1 of the pixel, and applies the calculated value of the image quality adjustment parameter α1 to the pixel value of the pixel to calculate the adjusted pixel value. By performing this for all pixels of the first derived image D1, it is possible to generate an adjusted first derived image. The same applies to the second derived image D2. Note that the adjustment rate may be set to different values ​​or the same value for the image quality adjustment parameter α1 and the image quality adjustment parameter α2 for the same spatial position.

[0100] Note that image processing circuit 15 may store a lookup table (LUT) that associates spatial positions with adjustment rates of image quality adjustment parameters instead of functions. In this case, image processing circuit 15 applies the LUT to each pixel of the derived image to identify the adjustment rate of the image quality adjustment parameter, calculates the value of the image quality adjustment parameter of the pixel by multiplying the identified adjustment rate by a reference value, and applies the calculated image quality adjustment parameter value to the pixel value of the pixel to calculate the adjusted pixel value.

[0101] According to Application Example 2, it is possible to change the value of the image quality adjustment parameter according to the spatial position of the derived image. This makes it possible to obtain the effect of a nonlinear image filter such as a nonlinear anisotropic diffusion filter uniformly over the entire image.

[0102] (Application example 3) In the above embodiment, there are two derived images and therefore two types of image quality adjustment parameters. In Application Example 3, the number of derived images and the number of types of image quality adjustment parameters are generally assumed to be "n", where "n" is an integer of 2 or greater.

[0103] Fig. 9 is a diagram illustrating a simplified image filter according to application example 3. As shown in Fig. 9, the image processing circuit 15 executes a nonlinear image filter 200B as a nonlinear image filter by implementing the image processing function 152. The nonlinear image filter 200B is similar to the nonlinear image filter 200A in Fig. 4, but differs in that the number of derived images to be generated is "n".

[0104] When the nonlinear image filter 200B is executed, the image processing circuit 15 executes the adjustment process 501 by implementing the adjustment function 153. In the adjustment process 501, the image processing circuit 15 adjusts the kth (k is an index of the derived image, 1≦k≦n) derived image D k Image quality adjustment parameter α k The kth derivative image α after adjustment by multiplying k D k Generate the image quality adjustment parameter α k is a real number in the range from 0 to 1. The image quality adjustment parameter α k The image quality adjustment parameters α k can be individually and arbitrarily adjusted by an operator via the input device 19, etc. For example, an output image I0 when the edge size is set to "0" and an output image I1 when the edge size is set to "1" are calculated, and the input image I in and the output image I0, a first derived image based on the output image I0 and the output image I out and the second derivative image based on the input image I in and the output image I1, and a third derived image based on the output image I1 and the output image I out It is also possible to generate a fourth derived image based on the output image and the input image I when other filter parameters are set to zero or to predetermined values. inand generating a derivative image based on the output image I and the output image I when the other filter parameters are set to zero or a predetermined value. out It is also possible to generate a derived image based on the

[0105] After the adjustment process 501 is performed, the image processing circuit 15 executes a synthesis process 502 by implementing the synthesis function 154. In the synthesis process 502, the image processing circuit 15 performs a synthesis process 502 on the input image I in and the adjusted k-th derivative image α k D k The composite image I' is out The image processing circuit 15 generates an input image I in , the adjusted kth derivative image α k D k Adding these, we get the composite image I´ out Generate a synthetic image I´ out is displayed by the display device 16.

[0106]

number

[0107] In addition, in the application example 3, the synthesis method is not limited to addition, but may be multiplication, inversion multiplication, or various other methods.

[0108] According to application example 3, a composite image I' based on three or more derived images is out This allows the synthesis image I' to be generated. out It is possible to adjust the image quality in more detail.

[0109] Second embodiment In the first embodiment, the output image I is out In order to obtain a strong processing, it is necessary to calculate a nonlinear image filter. The calculation of a nonlinear image filter is complicated and takes time. In particular, if the number of repeated calculations of a nonlinear anisotropic diffusion filter is increased in order to obtain a strong processing, the processing time increases accordingly.

[0110] As a method for solving this problem, a method of replacing the method with a method in which a pair of input and output images of a nonlinear image filter is used as learning data, and an output image is inferred for an unknown input image according to a machine learning model created based on the learning data, and the result is output. However, with such a machine learning method, the image quality cannot be adjusted other than by changing the composition ratio of the images before and after processing to adjust the filter strength of the entire image.

[0111] The ultrasound diagnostic system 1 according to the second embodiment uses a machine learning model that outputs a derived image. The ultrasound diagnostic system 1 according to the second embodiment will be described below. In the following description, components having substantially the same functions as those in the first embodiment are given the same reference numerals and will be described repeatedly only when necessary.

[0112] In addition, the machine learning model according to the second embodiment uses a neural network with two or more layers. The architecture of the neural network may be of any type as long as it can input an image and output an image, but as an example, a CNN (Convolutional neural network) or an advanced version thereof may be used.

[0113] FIG. 10 is a diagram showing a simplified image filter according to the second embodiment. As shown in FIG. 10, the simplified image filter is divided into a learning stage and an implementation stage. In the learning stage, the image processing circuit 15 learns an untrained neural network 601 based on a plurality of learning samples to generate a trained neural network 602. The learning samples are input data, i.e., a learning input image I inL and the training data, the derived image D 1L and D. 2L The learning derived image is a set of the first derived image D 1L and the second derivative image D 2L The combination of the input image I inL and training derivative image D 1L and D. 2Lmay be input to and output from the neural network 601 in any format, but may be input and output as a multidimensional vector having the same number of elements as the number of pixels. Each element has the pixel value of the pixel corresponding to the element as the element value. In this case, the learning derived image D 1L and D. 2L The derived image for learning D can be treated as a multidimensional vector having the same number of elements as the total number of pixels. 1L and D. 2L is an arbitrary training input image I inL The image may be generated by applying the simplified image filter of the first embodiment to the image.

[0114] The learning method is not particularly limited. For example, the image processing circuit 15 may inL Enter the first derivative image D 1L and the second derivative image D 2L The learnable parameters of the neural network 601 are determined by supervised learning so as to output the above-mentioned. The learnable parameters include weight parameters, bias parameters, and the like.

[0115] More specifically, the image processing circuit 15 processes the learning input image I in Then, the image processing circuit 15 applies the neural network 601 to perform forward propagation processing to output a first guessed derived image and a second guessed derived image. 1L and the second derived image D 2LA backpropagation process is performed by applying the difference (error) between the training sample and the training sample to the neural network 601, and a gradient vector is calculated, which is a differential coefficient of an error function, which is a function of the trainable parameters. Next, the image processing circuit 15 updates the trainable parameters based on the gradient vector. These forward propagation process, backpropagation process, and parameter update process are repeated while changing the training sample, and trainable parameters that minimize the error function are determined according to a predetermined optimization method. This generates a trained neural network 602. The trained neural network 602 is stored in the storage device 17. The trained neural network 602 is implemented in the ultrasound diagnostic system 1 as a replacement for the nonlinear image filter 200A.

[0116] In the implementation stage, the image processing circuit 15 inputs an unknown input image I in to estimate a sequence of derived images (D1, D2). Thereafter, as in the first embodiment, the image processing circuit 15 applies the image quality adjustment parameter α1 to the first derived image D1 in an adjustment process 401 to generate an adjusted first derived image α1D1, and applies the image quality adjustment parameter α2 to the second derived image D2 in an adjustment process 402 to generate an adjusted second derived image α2D2. The image processing circuit 15 then calculates the image quality adjustment parameter α1 of the input image I in accordance with, for example, the above equation (10). in The adjusted first derived image α1D1 and the adjusted second derived image α2D2 are added together to obtain a composite image I′ out Generate a synthetic image I´ out is displayed on the display device 16.

[0117] This completes the simplified image filter according to the second embodiment.

[0118] In the above description, the number of derived images is two, but it can be extended to n, as in Application Example 3 of the first embodiment. In this case, in the learning stage, the nonlinear image filter 200B in Fig. 9 is executed instead of the nonlinear image filter 200A in Fig. 10. Also, in the second embodiment, Application Examples 1-3 of the first embodiment can be combined.

[0119] According to the second embodiment, in the implementation stage, a derived image can be obtained directly from an input image by using a trained neural network without executing a nonlinear image filter. This makes it possible to reduce the processing time and calculation load of the simplified image filter compared to the first embodiment in which a nonlinear image filter is executed.

[0120] According to at least one of the embodiments described above, image quality adjustment in image processing related to ultrasound diagnosis can be simplified.

[0121] The term "processor" used in the above description means a circuit such as a CPU, a GPU, or an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)). The processor realizes its function by reading and executing a program stored in a memory circuit. Instead of storing a program in a memory circuit, the processor may be configured to directly incorporate the program in its circuit. In this case, the processor realizes its function by reading and executing a program incorporated in its circuit. Instead of executing a program, a function corresponding to the program may be realized by a combination of logic circuits. Each processor in this embodiment is not limited to being configured as a single circuit for each processor, but may be configured as a single processor by combining multiple independent circuits to realize its function. Furthermore, the multiple components in FIG. 1 may be integrated into a single processor to realize its function.

[0122] Although some embodiments have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, modifications, and combinations of embodiments can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0123] 1. Ultrasound diagnostic system 11 Ultrasound probe 12 Transmitting and receiving circuit 13 B-mode processing circuit 14 Doppler processing circuit 15 Image processing circuit 16 Display equipment 17 Storage device 18 Control circuit 19 Input Devices 200, 200A, 200B Nonlinear Image Filters

Claims

1. An image processing unit that generates a first derived image including a first image component for smoothing and a second derived image including a second image component for highlighting tissue structure by applying a trained model, which is a neural network trained to receive an ultrasound image and output an image including an image component for smoothing and an image including an image component for highlighting tissue structure, to an ultrasound image of a subject, the trained model being a neural network trained to receive an ultrasound image and output an image including an image component for smoothing and an image including an image component for highlighting tissue structure, the image processing unit generating a first derived image including a first image component for smoothing and a second derived image including a second image component for highlighting tissue structure, the image processing unit generating a first derived image including a first image component for smoothing and a second derived image including a second image component for highlighting tissue structure, the image processing unit generating a second derived image including a first derived image for smoothing and a second derived image including a second image component for highlighting tissue structure, the image processing unit generating a second derived image including a first derived image for smoothing and a second derived image including a second image component for highlighting tissue structure, the image processing unit generating a second derived image including a first derived image for smoothing and a second derived image including a second derived image ... second derived image for highlighting tissue structure, the image processing unit generating a second derived image including a second derived image for highlighting tissue structure, the image processing unit generating a second derived image including a second derived image for highlighting an adjustment unit that applies a variable first adjustment parameter value for adjusting a smoothing strength to the first derived image to generate a first adjusted derived image, and applies a variable second adjustment parameter value for adjusting an enhancement strength of a tissue boundary to the second derived image to generate a second adjusted derived image; a synthesis unit that synthesizes the ultrasound image of the object, the first adjusted derived image, and the second adjusted derived image to generate a synthetic image; An ultrasound diagnostic system comprising:

2. The image including the image component for smoothing and the image including the image component for highlighting the tissue structure are generated based on the ultrasound image and a first output image and a second output image generated by nonlinear image processing, the nonlinear image processing is performed at least in one layer of multiresolution decomposition / reconstruction and includes an image filter having filter parameters for smoothing tissue and enhancing tissue boundaries; The first output image is generated by applying the image filter, to which the filter parameters calculated based on the ultrasound image in the nonlinear image processing are set, to the ultrasound image; The second output image is generated by applying the image filter, the filter parameter of which is set to an arbitrary value, to the ultrasound image; the image including the image component for smoothing is a difference image between the ultrasound image and the second output image, the image including the image component for enhancing the tissue structure is a difference image between the first output image and the second output image; The ultrasound diagnostic system of claim 1 .

3. The ultrasound diagnostic system according to claim 2 , wherein the image filter includes a nonlinear image filter using a diffusion equation.

4. The filter parameter is the size of the edge of the tissue; the first output image is an output image of the nonlinear image processing when the size of the edge is calculated based on an eigenvalue and an eigenvector of a structure tensor of the ultrasound image; the second output image being an output image of the non-linear image processing when the edge magnitude is set to zero; The ultrasound diagnostic system of claim 3.

5. The ultrasound diagnostic system according to claim 1 , further comprising an input unit for inputting the first adjustment parameter value and the second adjustment parameter value.

6. The ultrasound diagnostic system according to claim 5 , wherein the input unit has an input part for inputting the first adjustment parameter value and the second adjustment parameter value, respectively.

7. 6. The ultrasound diagnostic system of claim 5, wherein the input unit has an input component having a two-dimensional coordinate space corresponding to the number of images of the first derived image and the second derived image, and the first adjustment parameter value is assigned to a first axis of the coordinate space, and the second adjustment parameter value is assigned to a second axis of the coordinate space.

8. the input unit has a display device that displays the input component, which is a GUI component; the display device displays text or pictograms for explaining adjustment parameter values ​​corresponding to each of the input components. The ultrasound diagnostic system of claim 7.

9. 8. The ultrasonic diagnostic system according to claim 6, wherein the input component is a GUI component or a mechanical component.

10. the first adjustment parameter value has a different value depending on a spatial position of the first derived image; the second adjustment parameter value varies depending on a spatial position of the second derived image; The ultrasound diagnostic system of claim 1 .

11. the first adjustment parameter value has a different value depending on a depth position of the first derived image; the second adjustment parameter value has a different value depending on a depth position of the second derived image; The ultrasound diagnostic system of claim 10.

12. an ultrasonic probe that transmits ultrasonic waves to the subject, receives reflected waves from the subject, and outputs an echo signal corresponding to the reflected waves; A conversion unit that converts the echo signal into reflected wave data according to a receiving directivity; A B-mode processing unit that generates B-mode information based on the reflected wave data; and an image generator that generates an ultrasound image of the object based on the B-mode information. The ultrasound diagnostic system of claim 1 .

13. An image processing step of generating a first derived image including a first image component for smoothing and a second derived image including a second image component for highlighting tissue structure by applying a trained model, which is a neural network trained to input an ultrasound image and output an image including an image component for smoothing and an image including an image component for highlighting tissue structure, to an ultrasound image of a subject, the trained model including a trained neural network trained to input an ultrasound image and output an image including an image component for smoothing and an image including an image component for highlighting tissue structure, the image processing step of generating an image including a trained neural network trained to ... an adjustment step of applying a variable first adjustment parameter value for adjusting a smoothing strength to the first derived image to generate a first adjusted derived image, and applying a variable second adjustment parameter value for adjusting a tissue boundary enhancement strength to the second derived image to generate a second adjusted derived image; combining the ultrasound image of the object, the first adjusted derived image, and the second adjusted derived image to generate a combined image; 13. An ultrasound image processing method comprising:

Images