Ultrasound diagnostic device and program

Abstract

To calculate a correction gain capable of making the brightness of a substantial part uniform.SOLUTION: An ultrasonic diagnostic device in an embodiment includes a generation part, a derivation part, and a calculation part. The generation part generates high frequency component image data and low frequency component image data having second resolution lower than first resolution by executing multiple resolution analysis for ultrasonic image data having the first resolution. The derivation part derives information on a structure from the high frequency component image data. On the basis of the information on the structure, the calculation part calculates a correction gain on the basis of the low frequency component image data so that the contribution of the brightness of a pixel indicating a substantial part in the low frequency component image data for the correction gain is higher than the contribution of the brightness of a pixel indicating the structure in the low frequency component image data for the correction gain.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The embodiments disclosed in the present specification and drawings relate to an ultrasound diagnostic apparatus and a program. [Background technology]

[0002] There is a need for a function that automatically adjusts brightness so that the brightness of the entire parenchymal part (parenchymal tissue) of an organ such as the liver becomes uniform. One method for achieving this function is to perform multi-resolution analysis on ultrasound image data to calculate low-frequency component image data of the ultrasound image data that is the input image data, correct the brightness so that the brightness is uniform on the low-frequency component image data, and then return the resolution of the brightness-corrected low-frequency component image data to the original resolution (the resolution of the input image data). Because information about structures is lost in the low-frequency component image data, it is possible to change only the brightness of the parenchymal part without changing the brightness of the structures.

[0003] However, with the above method, it may be difficult to correct the luminance of parenchymal tissues near high-luminance structures (e.g., abdominal wall, diaphragm, vascular wall, etc.). This is because, although it is desired to calculate a correction gain so that the luminance of the parenchymal tissue reaches a target luminance level, the luminance of the parenchymal tissue to be corrected may not be accurately calculated due to the influence of high-luminance structures near the parenchymal tissue. Thus, with the above method, it may be impossible to calculate a correction gain that can make the luminance of the parenchymal tissue uniform. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-83056 Summary of the Invention [Problem to be solved by the invention]

[0005] One of the problems to be solved by the embodiments disclosed in the present specification and drawings is to improve the brightness of the solid portion. Increase the brightness level to approach the target brightness level compared to the conventional technology However, the problem to be solved by the embodiments disclosed in this specification and the drawings is not limited to the above problem. Problems corresponding to the effects of the configurations of the embodiments described later can also be considered as other problems. [Means for solving the problem]

[0006] An ultrasound diagnostic apparatus according to an embodiment includes a generating unit, a deriving unit, and a calculating unit. The generating unit generates high-frequency component image data and low-frequency component image data having a second resolution lower than the first resolution by performing multi-resolution analysis on ultrasound image data having a first resolution. The deriving unit derives information about structures from the high-frequency component image data. The calculating unit calculates the correction gain based on the low-frequency component image data, based on the information about the structures, such that the contribution of luminance of pixels representing solid portions in the low-frequency component image data to the correction gain is greater than the contribution of luminance of pixels representing the structures in the low-frequency component image data to the correction gain. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of an ultrasound diagnostic apparatus according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of a process executed by the resolution modulation function according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of processing executed by the structure identification function according to the embodiment. [Figure 4A] FIG. 4A is a diagram for explaining an example of a method in which the correction gain calculation function according to the embodiment calculates a correction gain. [Figure 4B] FIG. 4B is a diagram for explaining an example of another method in which the correction gain calculation function according to the embodiment calculates the correction gain. [Figure 5]FIG. 5 is a diagram illustrating an example of a process executed by the resolution demodulation function according to the embodiment. [Figure 6] FIG. 6 is a flowchart showing an example of the flow of processing executed by the ultrasound diagnostic apparatus according to the embodiment. [Figure 7] FIG. 7 is a diagram showing an example of an ultrasound image based on ultrasound image data generated by a conventional ultrasound diagnostic device. [Figure 8] FIG. 8 is a diagram showing an example of an ultrasound image based on ultrasound image data generated by the ultrasound diagnostic apparatus according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, with reference to the drawings, each embodiment and each modified example of the ultrasound diagnostic apparatus and program will be described in detail. Note that the embodiments can be combined with conventional technology, other embodiments, or other modified examples to the extent that no contradiction occurs in the content. Similarly, the modified examples can be combined with conventional technology, other embodiments, or other modified examples to the extent that no contradiction occurs in the content. In the following description, similar components will be assigned common reference numerals, and duplicated descriptions may be omitted.

[0009] (Embodiment) 1 is a block diagram showing an example of the configuration of an ultrasonic diagnostic apparatus 1 according to an embodiment. As shown in FIG. 1, the ultrasonic diagnostic apparatus 1 according to the embodiment includes an apparatus main body 100, an ultrasonic probe 101, an input device 102, and a display 103.

[0010] The ultrasonic probe 101 includes, for example, a plurality of elements (piezoelectric vibrators, piezoelectric elements). These elements generate ultrasonic waves based on a drive signal supplied from a transmission circuit 111 of a transmission / reception circuit 110 included in the device main body 100. Specifically, the elements generate ultrasonic waves having a waveform corresponding to the transmission drive voltage when a voltage (transmission drive voltage) is applied by the transmission circuit 111. The waveform of the transmission drive voltage indicated by the drive signal is the waveform of the voltage applied to the plurality of elements. That is, the ultrasonic probe 101 transmits ultrasonic waves corresponding to the magnitude of the applied transmission drive voltage. The ultrasonic probe 101 also receives reflected waves from the subject P, converts them into received signals (reflected wave signals), which are electrical signals, and outputs the received signals to the device main body 100. The ultrasonic probe 101 also includes, for example, a matching layer provided on the elements and a backing material that prevents ultrasonic waves from propagating backward from the elements. The ultrasonic probe 101 is detachably connected to the device main body 100.

[0011] When ultrasonic waves are transmitted from the ultrasonic probe 101 to the subject P, the transmitted ultrasonic waves are reflected successively by discontinuous surfaces of acoustic impedance in the tissues of the subject P and are received as reflected waves by multiple elements of the ultrasonic probe 101. The amplitude of the received reflected waves depends on the difference in acoustic impedance at the discontinuous surfaces from which the ultrasonic waves are reflected. When the transmitted ultrasonic pulse is reflected by the surface of a moving blood flow, heart wall, or the like, the reflected waves undergo a frequency shift due to the Doppler effect depending on the velocity component of the moving object in the direction of ultrasonic transmission. The ultrasonic probe 101 then outputs the received signals to the receiving circuit 112 of the transmitting / receiving circuit 110, which will be described later.

[0012] The ultrasonic probe 101 is detachably attached to the device main body 100. When scanning a two-dimensional region inside the subject P (two-dimensional scanning), the operator connects, for example, a 1D array probe in which multiple elements are arranged in a row to the device main body 100 as the ultrasonic probe 101. Types of 1D array probes include linear ultrasonic probes, convex ultrasonic probes, and sector ultrasonic probes. When scanning a three-dimensional region inside the subject P (three-dimensional scanning), the operator connects, for example, a mechanical 4D probe or a 2D array probe to the device main body 100 as the ultrasonic probe 101. A mechanical 4D probe is capable of two-dimensional scanning using multiple elements arranged in a row like a 1D array probe, and is also capable of three-dimensional scanning by swinging the multiple elements at a predetermined angle (swing angle). A 2D array probe is capable of three-dimensional scanning using multiple elements arranged in a matrix, and is also capable of two-dimensional scanning by focusing and transmitting ultrasonic waves.

[0013] The input device 102 is realized by input means such as a mouse, keyboard, button, panel switch, touch command screen, foot switch, trackball, joystick, etc. The input device 102 receives various setting requests from the operator of the ultrasound diagnostic apparatus 1 and transfers the received various setting requests to the apparatus main body 100.

[0014] The display 103 displays, for example, a GUI (Graphical User Interface) that allows the operator of the ultrasound diagnostic apparatus 1 to input various setting requests using the input device 102, and displays ultrasound images based on ultrasound image data generated in the apparatus main body 100. The display 103 is realized by a liquid crystal monitor, a CRT (Cathode Ray Tube) monitor, or the like.

[0015] The device main body 100 generates ultrasound image data based on reception signals transmitted from the ultrasound probe 101. Note that ultrasound image data is an example of image data. The device main body 100 can generate two-dimensional ultrasound image data based on reception signals transmitted from the ultrasound probe 101 corresponding to a two-dimensional region of the subject P. The device main body 100 can also generate three-dimensional ultrasound image data based on reception signals transmitted from the ultrasound probe 101 corresponding to a three-dimensional region of the subject P. As shown in FIG. 1, the device main body 100 includes a transmission / reception circuit 110, a buffer memory 120, a signal processing circuit 130, an image generation circuit 140, an image memory 150, a storage circuit 160, and a processing circuit 170.

[0016] The transmission / reception circuit 110, under the control of the control function 170f of the processing circuit 170, causes the ultrasonic probe 101 to transmit ultrasonic waves and causes the ultrasonic probe 101 to receive reflected waves of the ultrasonic waves. In other words, the transmission / reception circuit 110 performs scanning via the ultrasonic probe 101. Note that scanning is also referred to as scanning, ultrasonic scanning, or ultrasonic scanning. The transmission / reception circuit 110 is an example of a transmission / reception unit. The transmission / reception circuit 110 has a transmission circuit 111 and a reception circuit 112. The transmission circuit 111 is an example of a transmission unit, and the reception circuit 112 is an example of a reception unit.

[0017] The transmission circuit 111, under the control of the control function 170f, causes the ultrasonic probe 101 to transmit ultrasonic waves. The transmission circuit 111 has a rate pulser generating circuit, a transmission delay circuit, and a transmission pulser. The transmission circuit 111 supplies a drive signal to the ultrasonic probe 101. When scanning a two-dimensional region within the subject P, the transmission circuit 111 causes the ultrasonic probe 101 to transmit an ultrasonic beam for scanning the two-dimensional region. When scanning a three-dimensional region within the subject P, the transmission circuit 111 causes the ultrasonic probe 101 to transmit an ultrasonic beam for scanning the three-dimensional region.

[0018] The rate pulser generating circuit, under the control of the control function 170f, repeatedly generates rate pulses for forming a transmission ultrasound wave (transmission beam) at a predetermined rate frequency (PRF: Pulse Repetition Frequency). The rate pulses pass through a transmission delay circuit, so that voltages with different transmission delay times are applied to the transmission pulser. For example, the transmission delay circuit imparts a transmission delay time for each element required to focus the ultrasound waves generated from the ultrasound probe 101 into a beam and determine the transmission directivity to each rate pulse generated by the rate pulser generating circuit. The transmission pulser supplies a drive signal (drive pulse) to the ultrasound probe 101 at a timing based on the rate pulse. That is, the transmission pulser applies a voltage (transmission drive voltage) having a waveform indicated by the drive signal to the ultrasound probe 101 at a timing based on the rate pulse. The transmission delay circuit arbitrarily adjusts the transmission direction of ultrasound waves from the element surface by changing the transmission delay time imparted to each rate pulse.

[0019] The driving pulse is transmitted from the transmitting pulser via a cable to the elements in the ultrasonic probe 101, and then converted from an electrical signal into mechanical vibration in the elements. That is, when a voltage is applied to the elements, the elements vibrate mechanically. Ultrasound waves generated by this mechanical vibration are transmitted into the living body (inside the subject P). Here, the ultrasonic waves, which have different transmission delay times for each element, are focused and propagate in a predetermined direction.

[0020] The transmission circuit 111 has a function capable of instantaneously changing the transmission frequency, transmission drive voltage, etc. under the control of the control function 170f in order to execute a predetermined scanning sequence. In particular, the change in the transmission drive voltage is realized by a linear amplifier type oscillation circuit that can instantaneously switch the value of the transmission drive voltage, or a mechanism that electrically switches between multiple power supply units.

[0021] The reflected waves of the ultrasonic waves transmitted by the ultrasonic probe 101 reach the elements inside the ultrasonic probe 101, and are then converted from mechanical vibrations into electrical signals (received signals) in the elements, and the received signals are input to the receiving circuit 112. The receiving circuit 112 has a preamplifier, an A / D (Analog to Digital) converter, a quadrature detection circuit, etc., and performs various processes on the reflected wave signals transmitted from the ultrasonic probe 101 to generate reflected wave data (received data). The receiving circuit 112 then stores the generated reflected wave data in the buffer memory 120.

[0022] The preamplifier amplifies the reflected wave signal for each channel and performs gain adjustment (gain correction). The A / D converter converts the gain-corrected reflected wave signal into a digital signal by A / D converting the gain-corrected reflected wave signal. The quadrature detection circuit converts the digital reflected wave signal into an in-phase signal (I signal, I: In-phase) and a quadrature signal (Q signal, Q: Quadrature-phase) in the baseband. The quadrature detection circuit then stores the I signal and Q signal (IQ signal) in buffer memory 120 as reflected wave data.

[0023] The receiving circuit 112 generates two-dimensional reflected wave data from the two-dimensional reflected wave signal transmitted from the ultrasonic probe 101. The receiving circuit 112 also generates three-dimensional reflected wave data from the three-dimensional reflected wave signal transmitted from the ultrasonic probe 101.

[0024] In this embodiment, the ultrasound diagnostic apparatus 1 performs various processes in real time. For example, the ultrasound probe 101 sequentially transmits one frame of reflected wave signals to the receiving circuit 112. Every time the receiving circuit 112 receives one frame of reflected wave signals transmitted from the ultrasound probe 101, it generates one frame of reflected wave data from the one frame of reflected wave signals. Every time the receiving circuit 112 generates one frame of reflected wave data, it stores the one frame of reflected wave data in the buffer memory 120.

[0025] The buffer memory 120 is a memory that temporarily stores reflected wave data generated by the transmission / reception circuit 110. For example, the buffer memory 120 is configured to be able to store a predetermined number of frames of reflected wave data. When a new frame of reflected wave data is generated by the reception circuit 112 while the buffer memory 120 is storing the predetermined number of frames of reflected wave data, the buffer memory 120, under the control of the reception circuit 112, discards the oldest generated frame of reflected wave data and stores the newly generated frame of reflected wave data. For example, the buffer memory 120 is realized by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory.

[0026] The signal processing circuit 130 reads the reflected wave data from the buffer memory 120, performs various signal processing on the read reflected wave data, and outputs the reflected wave data that has undergone various signal processing as B-mode data or Doppler data to the image generation circuit 140. The signal processing circuit 130 is realized by, for example, a processor. The signal processing circuit 130 is an example of a signal processing unit.

[0027] In order for the ultrasound diagnostic apparatus 1 to perform various processes in real time, the signal processing circuit 130 performs the following processes. For example, every time one frame of reflected wave data is newly stored in the buffer memory 120, the signal processing circuit 130 reads out the newly stored one frame of reflected wave data. The signal processing circuit 130 then performs various signal processing on the read one frame of reflected wave data to generate one new frame of B-mode data or Doppler data. Every time the signal processing circuit 130 generates one frame of B-mode data or Doppler data, it outputs the newly generated one frame of B-mode data or Doppler data to the image generation circuit 140. An example of the various signal processing performed by the signal processing circuit 130 will be described below.

[0028] For example, the signal processing circuit 130 performs quadrature detection, logarithmic amplification, envelope detection processing, etc. on the reflected wave data read out from the buffer memory 120 to generate B-mode data in which the signal strength (amplitude strength) for each sample point is expressed as luminance. Then, the signal processing circuit 130 outputs the generated B-mode data to the image generation circuit 140.

[0029] Furthermore, the signal processing circuit 130 performs frequency analysis on the reflected wave data read from the buffer memory 120 to extract motion information of the moving object (blood flow, tissue, contrast agent echo components, etc.) based on the Doppler effect from the reflected wave data, and generates Doppler data indicating the extracted motion information. For example, the signal processing circuit 130 extracts average velocity, average variance, average power value, etc. as motion information of the moving object across multiple points, and generates Doppler data indicating the extracted motion information of the moving object. The signal processing circuit 130 then outputs the generated Doppler data to the image generation circuit 140.

[0030] Using the functions of the signal processing circuit 130, the ultrasound diagnostic device 1 can perform a color Doppler method, also known as a color flow mapping (CFM) method. In the color flow mapping method, ultrasonic waves are transmitted and received multiple times along multiple scan lines. The color flow mapping method applies an MTI (Moving Target Indicator) filter to a data sequence at the same position to suppress signals (clutter signals) derived from stationary or slow-moving tissues from the data sequence at the same position and extract signals (blood flow signals) derived from blood flow. The color flow mapping method then estimates blood flow information, such as blood flow velocity, blood flow dispersion, and blood flow power, from the blood flow signal. The signal processing circuit 130 outputs color image data indicating the blood flow information estimated by the color flow mapping method to the image generation circuit 140. The color image data is an example of Doppler data.

[0031] The signal processing circuit 130 is capable of processing both two-dimensional reflected wave data and three-dimensional reflected wave data.

[0032] The image generation circuitry 140 generates ultrasound image data from the B-mode data or Doppler data output from the signal processing circuitry 130. The image generation circuitry 140 is realized by a processor.

[0033] In order for the ultrasound diagnostic apparatus 1 to perform various processes in real time, the image generation circuit 140 performs the following processes. For example, every time the image generation circuit 140 receives one frame of data (B-mode data or Doppler data) output from the signal processing circuit 130, the image generation circuit 140 generates one frame of ultrasound image data from the one frame of data. Then, every time the image generation circuit 140 generates one frame of ultrasound image data, the image generation circuit 140 stores the one frame of ultrasound image data in the image memory 150.

[0034] For example, the image generation circuit 140 generates two-dimensional B-mode image data that represents the intensity of the reflected wave as brightness from the two-dimensional B-mode data generated by the signal processing circuit 130. The image generation circuit 140 also generates two-dimensional Doppler image data in which motion information or blood flow information is visualized from the two-dimensional Doppler data generated by the signal processing circuit 130. The two-dimensional Doppler image data in which motion information is visualized is velocity image data, variance image data, power image data, or image data that is a combination of these.

[0035] Here, the image generation circuit 140 generally converts (scan converts) a scan line signal sequence of an ultrasound scan into a scan line signal sequence of a video format, such as that of a television, to generate ultrasound image data for display. For example, the image generation circuit 140 generates ultrasound image data for display by performing coordinate conversion on the data output from the signal processing circuit 130 in accordance with the ultrasound scanning format of the ultrasound probe 101. In addition to scan conversion, the image generation circuit 140 also performs various image processing, such as image processing (smoothing processing) that regenerates an average brightness image using multiple image frames after scan conversion, and image processing (edge enhancement processing) that uses a differential filter within the image. The image generation circuit 140 also combines text information of various parameters, scales, body marks, etc. with the ultrasound image data.

[0036] Furthermore, the image generation circuit 140 generates three-dimensional B-mode image data by performing coordinate transformation on the three-dimensional B-mode data generated by the signal processing circuit 130. The image generation circuit 140 also generates three-dimensional Doppler image data by performing coordinate transformation on the three-dimensional Doppler data generated by the signal processing circuit 130. That is, the image generation circuit 140 generates "three-dimensional B-mode image data and three-dimensional Doppler image data" as "three-dimensional ultrasound image data (volume data)." The image generation circuit 140 then performs various rendering processes on the volume data to generate various types of two-dimensional image data for displaying the volume data on the display 103.

[0037] The rendering process performed by the image generation circuit 140 includes, for example, a process of generating MPR image data from volume data using multi-planar reconstruction (MPR). The rendering process performed by the image generation circuit 140 also includes, for example, a volume rendering (VR) process of generating two-dimensional image data reflecting three-dimensional information. The image generation circuit 140 is an example of an image generation unit.

[0038] The B-mode data and Doppler data are ultrasound image data before scan conversion processing, and the data generated by the image generation circuit 140 is ultrasound image data for display after scan conversion processing. Note that the B-mode data and Doppler data are also called raw data.

[0039] The image memory 150 is a memory that stores various types of image data generated by the image generation circuit 140. The image memory 150 also stores data generated by the signal processing circuit 130. The B-mode data and Doppler data stored in the image memory 150 can be called up by an operator after diagnosis, for example, and becomes ultrasound image data for display via the image generation circuit 140. For example, the image memory 150 is realized by a semiconductor memory element such as a RAM (Random Access Memory), a flash memory, or a hard disk or an optical disk.

[0040] The memory circuitry 160 stores control programs for scanning (transmitting and receiving ultrasound), image processing, and display processing, as well as various data such as diagnostic information (e.g., patient ID, doctor's findings, etc.), diagnostic protocols, and various body marks. The memory circuitry 160 is also used, as necessary, to store data stored in the image memory 150. For example, the memory circuitry 160 is realized by a semiconductor memory element such as a flash memory, a hard disk, or an optical disk.

[0041] The processing circuit 170 executes various processes. The processing circuit 170 includes a resolution modulation function 170a, a structure identification function 170b, a correction gain calculation function 170c, a gain correction function 170d, a resolution demodulation function 170e, and a control function 170f. The resolution modulation function 170a is an example of a generation unit. The structure identification function 170b is an example of a derivation unit. The correction gain calculation function 170c is an example of a calculation unit. The gain correction function 170d is an example of a gain correction unit. The resolution demodulation function 170e is an example of a generation unit. The control function 170f is an example of a control unit and a display control unit.

[0042] Here, for example, each processing function of the components of the processing circuit 170 shown in Fig. 1, namely, a resolution modulation function 170a, a structure identification function 170b, a correction gain calculation function 170c, a gain correction function 170d, a resolution demodulation function 170e, and a control function 170f, is stored in the storage circuit 160 in the form of a program executable by a computer. The processing circuit 170 reads each program from the storage circuit 160 and executes the read program to realize the function corresponding to each program. In other words, the processing circuit 170 in a state in which each program has been read has each function shown in the processing circuit 170 of Fig. 1.

[0043] Of the multiple processing functions described above, the control function 170f controls the overall processing of the ultrasound diagnostic apparatus 1. Specifically, the control function 170f controls the processing of each processing function of the transmission circuitry 111, the reception circuitry 112, the signal processing circuitry 130, the image generation circuitry 140, and the processing circuitry 170 based on various setting requests input by the operator via the input device 102 and various control programs and various data read from the storage circuitry 160. The control function 170f also controls the display 103 to display an ultrasound image based on ultrasound image data for display stored in the image memory 150. For example, the control function 170f controls the display 103 to display a B-mode image based on B-mode image data or a color image based on color image data. The control function 170f also controls the display 103 to display a color image superimposed on the B-mode image.

[0044] For example, in order for the ultrasound diagnostic device 1 to perform processing in real time, the control function 170f acquires one frame of ultrasound image data from the image memory 150 every time the image generation circuit 140 stores one frame of ultrasound image data in the image memory 150. Then, every time the control function 170f acquires one frame of ultrasound image data from the image memory 150, it causes the display 103 to display an ultrasound image based on the one frame of ultrasound image data. Therefore, a series of processes from when the ultrasound probe 101 outputs a reflected wave signal to when the display 103 displays an ultrasound image is executed in real time.

[0045] The processing circuit 170 is realized by, for example, a processor. An ultrasound image is an example of an image.

[0046] The control function 170f also controls the ultrasonic probe 101 via the transmission / reception circuit 110, thereby controlling ultrasonic scanning.

[0047] The term "processor" used in the description refers to a circuit such as a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), or a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), or a field programmable gate array (FPGA)). The processor reads a program stored in the memory circuit 160 and executes the read program to realize its function. Instead of storing the program in the memory circuit 160, the processor may be configured so that the program is directly embedded in its circuit. In this case, the processor reads and executes the program embedded in the circuit to realize its function. Each processor in this embodiment is not limited to being configured as a single circuit, but may be configured as a single processor by combining multiple independent circuits to realize its function. 1 (for example, the signal processing circuit 130, the image generation circuit 140, and the processing circuit 170) may be integrated into a single processor to realize its functions. That is, the signal processing circuit 130, the image generation circuit 140, and the processing circuit 170 may be integrated into a single processing circuit realized by a processor.

[0048] The above has described the overall configuration of the ultrasound diagnostic device 1 according to the embodiment. According to this embodiment, the ultrasound diagnostic device 1 executes various processes described below so as to calculate a correction gain that can make the brightness of the solid part uniform.

[0049] 2 is a diagram illustrating an example of processing executed by the resolution modulation function 170a according to the embodiment. For example, every time the image generation circuit 140 stores one frame of ultrasound image data 11 shown in FIG. 2 in the image memory 150, the resolution modulation function 170a acquires the newly stored one frame of ultrasound image data 11 from the image memory 150.

[0050] Then, every time one frame of ultrasound image data 11 is acquired from the image memory 150, the processing circuitry 170 executes the following processing on the acquired one frame of ultrasound image data 11 in real time.

[0051] For example, the resolution modulation function 170a performs multi-resolution analysis on the ultrasound image data 11 to generate multi-layer low-frequency component image data 12, 14 and high-frequency component image data 13, 15. In this way, the low-frequency component image data 12, 14 and the high-frequency component image data 13, 15 are image data obtained in the process of multi-resolution analysis. For the multi-resolution analysis, a Gaussian pyramid or a discrete wavelet transform, etc. is used. In the example shown in FIG. 2, a two-layer (two-hierarchy) Gaussian pyramid or a discrete wavelet transform, etc. is performed. Below, a case where a Gaussian pyramid is performed will be described, but other methods such as a discrete wavelet transform may also be used to perform the multi-resolution analysis.

[0052] The resolution modulation function 170a decomposes the ultrasound image data 11 into high-frequency component image data 13, 15 representing high-frequency components and low-frequency component image data 12, 14 representing low-frequency components using a Gaussian pyramid. In the example shown in FIG. 2, the number of layers is two, so the Gaussian pyramid is repeated twice. Each time the Gaussian pyramid is performed, the number of pixels in each direction is halved, i.e., downsampling is performed.

[0053] Here, the ultrasound image data 11, the low-frequency component image data 12, 14, and the high-frequency component image data 13, 15 are two-dimensional image data. Specifically, the ultrasound image data 11 is image data in which 1024 pixels are arranged in a row direction (for example, the x-axis direction in image space) and 1024 pixels are arranged in a column direction perpendicular to the row direction (for example, the y-axis direction perpendicular to the x-axis direction in image space). In this way, the ultrasound image data 11 is image data composed of 1024 × 1024 pixels. However, the ultrasound image data 11 may also be composed of N × N pixels (N is an integer of 2 or more other than 1024). In the following description, "a × b pixels" means that a pixels are arranged in the row direction and b pixels are arranged in the column direction.

[0054] The low-frequency component image data 12 and the high-frequency component image data 13 are image data obtained by downsampling the ultrasound image data 11. Specifically, the low-frequency component image data 12 and the high-frequency component image data 13 are image data in which 512 pixels are arranged in the row direction and 512 pixels are arranged in the column direction. In this way, the low-frequency component image data 12 and the high-frequency component image data 13 are image data composed of 512 × 512 pixels.

[0055] The low-frequency component image data 14 and the high-frequency component image data 15 are image data obtained by downsampling the low-frequency component image data 12. Specifically, the low-frequency component image data 14 and the high-frequency component image data 15 are image data in which 256 pixels are arranged in the row direction and 256 pixels are arranged in the column direction. In this way, the low-frequency component image data 14 and the high-frequency component image data 15 are image data composed of 256 × 256 pixels.

[0056] Therefore, the resolution of the low-frequency component image data 12 and the high-frequency component image data 13 is lower than the resolution of the ultrasound image data 11. Also, the resolution of the low-frequency component image data 14 and the high-frequency component image data 15 is lower than the resolution of the low-frequency component image data 12 and the high-frequency component image data 13. The resolution of the ultrasound image data 11 is an example of a first resolution. The resolution of the low-frequency component image data 14 and the high-frequency component image data 15 is an example of a second resolution.

[0057] As described above, the resolution modulation function 170a performs multi-resolution analysis on the ultrasound image data 11 to generate high frequency component image data 15 and low frequency component image data 14 having a resolution lower than that of the ultrasound image data 11.

[0058] Then, the structure identification function 170b identifies pixels of the structure in the high frequency component image data 15 of the lowest layer (bottom layer). Specifically, the structure identification function 170b identifies the position of pixels representing the structure. Here, the structure is, for example, the abdominal wall, the diaphragm, blood vessels, etc. The pixels representing the structure have a relatively high brightness (pixel value).

[0059] The structure identification function 170b identifies pixels having a brightness equal to or greater than a threshold as pixels of the structure from among the multiple pixels that make up the high frequency component image data 15. Here, in this embodiment, the structure identification function 170b adaptively determines the threshold used when identifying pixels of the structure.

[0060] Fig. 3 is a diagram for explaining an example of processing executed by the structure identification function 170b according to the embodiment. Fig. 3 shows an example of a statistical distribution of a plurality of luminance values of a plurality of pixels constituting the high frequency component image data 15. For example, Fig. 3 shows a histogram as the statistical distribution, with the horizontal axis representing luminance and the vertical axis representing the number of pixels.

[0061] 3, the distribution 20 of the intensities of the pixels representing the parenchymal portion resembles a normal distribution having a predetermined peak. That is, the intensities of the pixels representing the parenchymal portion fall substantially within a range of intensities having a certain width centered on an intensity 20a corresponding to the peak of the distribution 20. The parenchymal portion is, for example, a part such as the liver.

[0062] A distribution 21 of multiple luminance values of multiple pixels indicating a structure exists on the higher luminance side of the distribution 20. Therefore, the structure identification function 170b sets a threshold value 22 used when identifying a structure between the distribution 20 and the distribution 21. That is, the structure identification function 170b determines the luminance value between the distribution 20 and the distribution 21 as the threshold value 22. Note that the structure identification function 170b may determine a predetermined threshold value as the threshold value 22.

[0063] Then, the structure identification function 170b identifies, as pixels of the structure, pixels having a brightness equal to or greater than the threshold value 22, among the multiple pixels constituting the high frequency component image data 15. In this way, the structure identification function 170b derives pixels indicating the structure (positions of the pixels of the structure) as information about the structure from the high frequency component image data 15.

[0064] As described above, the structure identification function 170b derives pixels that indicate structures based on the statistical distribution of multiple luminance values of multiple pixels that make up the high frequency component image data 15. Also, as described above, the structure identification function 170b determines the threshold value 22 for deriving pixels that indicate structures based on the statistical distribution of multiple luminance values of multiple pixels that make up the high frequency component image data 15, and derives pixels that indicate structures based on the threshold value 22.

[0065] Then, the correction gain calculation function 170c identifies pixels in the low-frequency component image data 14 that are at the same positions as the positions of the pixels of the structure identified in the high-frequency component image data 15. The correction gain calculation function 170c also divides the low-frequency component image data 14 into a plurality of small regions.

[0066] Then, for each small region, the correction gain calculation function 170c calculates the average brightness value of pixels other than those identified in the low-frequency component image data 14. Here, the pixels other than those identified in the low-frequency component image data 14 are considered to be pixels that represent the solid portion. That is, for each small region, the correction gain calculation function 170c calculates the average brightness value of multiple pixels that constitute the solid portion, excluding pixels that represent structures, from among the multiple pixels that constitute the small region.

[0067] Then, the correction gain calculation function 170c calculates a correction gain for each small region using the calculated average luminance value. For example, the correction gain calculation function 170c calculates the difference between a predetermined target luminance and the calculated average luminance value as the correction gain for each small region. FIG. 4A is a diagram for explaining an example of a method for the correction gain calculation function 170c according to the embodiment to calculate a correction gain. For example, as shown in FIG. 4A, for a certain small region, the correction gain calculation function 170c calculates a value 31a obtained by subtracting an average luminance value 30a from a target luminance value 25 as the correction gain 31a. For another small region, the correction gain calculation function 170c calculates a value 31b obtained by subtracting an average luminance value 30b from the target luminance value 25 as the correction gain 31b. For yet another small region, the correction gain calculation function 170c calculates a value 31c obtained by subtracting an average luminance value 30c from the target luminance value 25 as the correction gain 31c. In the following description, one of the small regions described above will be referred to as a "first small region," another small region will be referred to as a "second small region," and yet another small region will be referred to as a "third small region."

[0068] As described above, the correction gain calculation function 170c divides the low-frequency component image data 14 into multiple small regions, and for each small region, the region excluding pixels representing structures is defined as a real portion. Then, the correction gain calculation function 170c calculates the average luminance value of the real portion for each small region, and calculates a correction gain based on the calculated average luminance value and the target luminance 25. A small region is an example of a region.

[0069] Furthermore, the correction gain calculation function 170c may identify a small region among the multiple small regions in which the ratio of pixels representing structures to the total number of pixels is equal to or greater than a threshold. Then, the correction gain calculation function 170c may calculate the correction gain for the identified small region by interpolating the correction gains calculated for small regions surrounding the identified small region. Here, the small regions surrounding the identified small region are, for example, one or more small regions adjacent to the identified small region.

[0070] Furthermore, the correction gain calculation function 170c may calculate the correction gain for each small region using another method. Fig. 4B is a diagram for explaining an example of another method for the correction gain calculation function 170c according to the embodiment to calculate the correction gain.

[0071] For example, the correction gain calculation function 170c defines a three-dimensional Cartesian coordinate system in which the x-axis, y-axis, and z-axis are orthogonal to one another. The row direction described above is the x-axis direction, and the column direction described above is the y-axis direction. In this case, the low-frequency component image data 14 is image data in which 256 pixels are arranged in the x-axis direction and 256 pixels are arranged in the y-axis direction on the xy plane passing through z=0.

[0072] The z-axis represents the correction gain. In the above Cartesian coordinate system, if the position of the center of the first small region in the x-axis direction is x1 and the position in the y-axis direction is y1, the position of the first small region is expressed as (x1, y1, 0). Then, in the first small region, when a value 31a is calculated by subtracting the average luminance value 30a from the target luminance value 25 as shown in FIG. 4A, the correction gain calculation function 170c plots a point at the position (x1, y1, z1), where z1 is the value 31a obtained by subtracting the average luminance value 30a from the target luminance value 25.

[0073] Furthermore, if the center position of the second small region is represented by (x2, y2, 0), and a value 31b is calculated by subtracting the average luminance value 30b from the target luminance value 25 in the second small region as shown in Fig. 4A, the correction gain calculation function 170c plots a point at the position (x2, y2, z2), where z2 is the value 31b obtained by subtracting the average luminance value 30b from the target luminance value 25.

[0074] Furthermore, if the center position of the third small region is represented by (x3, y3, 0), and a value 31c is calculated by subtracting the average luminance value 30c from the target luminance value 25 in the third small region as shown in Fig. 4A, the correction gain calculation function 170c plots a point at the position (x3, y3, z3), where z3 is the value 31c obtained by subtracting the average luminance value 30c from the target luminance value 25.

[0075] The correction gain calculation function 170c performs the above-described processing for all small regions. Then, the correction gain calculation function 170c performs regression analysis on all points plotted in the three-dimensional Cartesian coordinate system to generate the approximated surface 35c shown in FIG. 4B. Note that, for the sake of simplicity, what is indicated by the symbol "35c" in FIG. 4B is a straight line, but in reality it is an approximated surface. Also, in FIG. 4B, the symbol "33" indicates the xy plane passing through z=0.

[0076] When calculating the correction gain for the first small region, the correction gain calculation function 170c calculates the z-axis value of the point on the approximated surface where the x-axis value is x1 and the y-axis value is y1 as the correction gain. The correction gain calculation function 170c performs similar processing for the other small regions to calculate the correction gains.

[0077] As described above, the correction gain calculation function 170c calculates the correction gain based on the pixels representing the structure, such that the contribution of the brightness of the pixels representing the solid part in the low-frequency component image data 14 to the correction gain is higher than the contribution of the brightness of the pixels representing the structure in the low-frequency component image data 14 to the correction gain.

[0078] In this way, the correction gain calculation function 170c calculates the correction gain so that the contribution of the luminance of pixels representing the solid portion is higher than the contribution of the luminance of pixels representing the structure. Therefore, according to this embodiment, it is possible to calculate a correction gain that can make the luminance of the solid portion uniform.

[0079] Specifically, for example, the correction gain calculation function 170c determines, as a substantial portion, a region formed by a plurality of pixels excluding pixels corresponding to pixels indicating the structure identified by the structure identification function 170b in the low-frequency component image data 14. Then, the correction gain calculation function 170c calculates a correction gain based on the luminance of the substantial portion.

[0080] The gain correction function 170d uses the correction gain calculated by the correction gain calculation function 170c to apply gain correction to the low-frequency component image data 14. For example, the gain correction function 170d applies gain correction to each small region using the correction gain corresponding to the small region, thereby generating gain-corrected low-frequency component image data 14a (see FIG. 5).

[0081] In this embodiment, the correction gain is calculated so that the contribution of the luminance of pixels representing solid portions is higher than the contribution of the luminance of pixels representing structures. Then, in this embodiment, a correction gain that can uniformize the luminance of such solid portions is used, and gain correction is applied to the low-frequency component image data 14. Therefore, in the low-frequency component image data 14a after gain correction, the luminance of the solid portions becomes uniform.

[0082] The resolution demodulation function 170e generates ultrasound image data 11a (see FIG. 5) having the same resolution as the ultrasound image data 11 from the gain-corrected low-frequency component image data 14a. FIG. 5 is a diagram for explaining an example of processing executed by the resolution demodulation function 170e according to the embodiment.

[0083] The resolution demodulation function 170e upsamples the gain-corrected low-frequency component image data 14a, which is made up of 256 × 256 pixels as shown in Fig. 5, to image data IM1, which is made up of 512 × 512 pixels. The resolution demodulation function 170e also upsamples the high-frequency component image data 15, which is made up of 256 × 256 pixels as shown in Fig. 5, to image data IM2, which is made up of 512 × 512 pixels. The resolution demodulation function 170e then combines the image data IM1 and the image data IM2 to generate the low-frequency component image data 12a, which is image data made up of 512 × 512 pixels as shown in Fig. 5.

[0084] The resolution demodulation function 170e then upsamples the low-frequency component image data 12a to image data IM3 consisting of 1024 x 1024 pixels. The resolution demodulation function 170e also upsamples the high-frequency component image data 13 consisting of 512 x 512 pixels shown in Fig. 5 to image data IM4 consisting of 1024 x 1024 pixels. The resolution demodulation function 170e then combines the image data IM3 and the image data IM4 to generate ultrasound image data 11a, which is image data consisting of 1024 x 1024 pixels shown in Fig. 5.

[0085] Then, the control function 170f causes the display 103 to display an ultrasound image based on the ultrasound image data 11a.

[0086] Fig. 6 is a flowchart showing an example of the flow of processing executed by the ultrasound diagnostic apparatus 1 according to the embodiment. The processing shown in Fig. 6 is executed when one frame of ultrasound image data 11 shown in Fig. 2 is stored in the image memory 150 by the image generation circuit 140. That is, the processing shown in Fig. 6 is executed every time one frame of ultrasound image data 11 shown in Fig. 2 is stored in the image memory 150. Therefore, the processing shown in Fig. 6 is executed in real time.

[0087] 6, the resolution modulation function 170a acquires the ultrasound image data 11 from the image memory 150 (step S101). Then, the resolution modulation function 170a performs multi-resolution analysis on the ultrasound image data 11 to generate multi-layered low-frequency component image data 12, 14 and high-frequency component image data 13, 15 (step S102).

[0088] Then, the structure identification function 170b identifies pixels that represent the structure (positions of pixels that represent the structure) in the high frequency component image data 15 (step S103).

[0089] Then, the correction gain calculation function 170c calculates, for each small region, the average value of the luminance of pixels other than the pixels identified in the low-frequency component image data 14 (step S104).

[0090] Then, the correction gain calculation function 170c calculates a correction gain for each small region using the calculated average brightness value (step S105).

[0091] Then, the gain correction function 170d performs gain correction on each small region using the correction gain corresponding to the small region, thereby generating the low-frequency component image data 14a after gain correction (step S106).

[0092] The resolution demodulation function 170e generates ultrasound image data 11a having the same resolution as the ultrasound image data 11 from the gain-corrected low-frequency component image data 14a (step S107).

[0093] Then, the control function 170f causes the display 103 to display an ultrasound image based on the ultrasound image data 11a (step S108), and ends the processing shown in FIG.

[0094] Fig. 7 is a diagram showing an example of an ultrasound image 50 based on ultrasound image data generated by a conventional ultrasound diagnostic device. Fig. 8 is a diagram showing an example of an ultrasound image 60 based on ultrasound image data generated by the ultrasound diagnostic device 1 according to the embodiment.

[0095] 7, as shown within an oval frame 50c, the region of the solid portion 50b near the structure 50a does not have sufficient correction gain applied. Therefore, the brightness of the region of the solid portion 50b near the structure 50a is lower than the brightness of other regions of the solid portion 50b. This is because the brightness of the structure 50a is relatively high, and therefore the correction gain corresponding to the region of the solid portion 50b near the structure 50a is relatively small.

[0096] On the other hand, in the ultrasound image 60 shown in FIG. 8 , as shown within an oval frame 60c, a sufficient correction gain is applied to the region of the solid portion 60b near the structure 60a. Therefore, the brightness of the region of the solid portion 60b near the structure 60a is similar to the brightness of the other regions of the solid portion 60b. This is because the ultrasound diagnostic device 1 according to the embodiment calculates the correction gain so that the contribution of the brightness of the pixels representing the solid portion is higher than the contribution of the brightness of the pixels representing the structure. In this way, the ultrasound diagnostic device 1 can correct the ultrasound image data so that the brightness of the solid portion becomes uniform. Therefore, the ultrasound diagnostic device 1 can generate ultrasound image data that allows users, such as doctors, to easily understand the state of the solid portion. As a result, the ultrasound diagnostic device 1 can improve the reliability of diagnoses made by users.

[0097] (Various Modifications of the Embodiments) Various modified examples of the embodiment will be described below. For example, in the above-described embodiment, the correction gain calculation function 170c calculates the average luminance value of the solid portion for each small region, and calculates the correction gain based on the calculated average luminance value and the target luminance 25. However, the correction gain calculation function 170c may calculate the correction gain for each pixel based on the luminance of the solid portion and the target luminance 25. Note that, if a pixel indicates a structure, the luminance of this pixel may be complemented using the luminance of surrounding pixels. Specifically, the correction gain calculation function 170c calculates the correction gain for each pixel based on the luminance of each of the multiple pixels that make up the solid portion and the target luminance 25.

[0098] Furthermore, instead of identifying pixels that represent structures, the structure identification function 170b may derive a value that indicates structure-likelihood. For example, the structure identification function 170b may derive a value that indicates structure-likelihood for each pixel that constitutes the high frequency component image data 15. Then, the correction gain calculation function 170c may calculate a correction gain using the value that indicates structure-likelihood. A specific example will be described below.

[0099] For example, the larger the value indicating the likelihood of a structure, the higher the possibility that the pixel is a structure. For example, a pixel having a relatively high value indicating the likelihood of a structure is relatively likely to be a pixel indicating a structure.

[0100] An example of a method by which the structure identification function 170b derives a value indicating the structure-likelihood will be described with reference to Fig. 3. For example, when the brightness of a pixel is within distribution 20, the structure identification function 170b sets a relatively small value (for example, 0) as the value indicating the structure-likelihood corresponding to this pixel.

[0101] Furthermore, for pixels whose brightness is greater than that of distribution 20, structure identification function 170b sets a value indicating the likelihood of a structure so that the greater the difference between a predetermined brightness (e.g., brightness 20a) of distribution 20 and the brightness of this pixel, the greater the value indicating a structure.

[0102] As described above, the structure identification function 170b derives a value indicating the likelihood of a structure as information about a structure for each of the plurality of pixels that make up the high frequency component image data 15.

[0103] Then, the correction gain calculation function 170c first sets the value indicating the structure-likeliness derived for each of the plurality of pixels constituting the high frequency component image data 15 to each of the plurality of pixels constituting the low frequency component image data 14. Note that the correction gain calculation function 170c sets a value indicating the structure-likeliness to each of the plurality of pixels constituting the low frequency component image data 14 that corresponds to each of the plurality of pixels constituting the high frequency component image data 15.

[0104] Then, the correction gain calculation function 170c sets a weight w for each of the plurality of pixels that make up the low-frequency component image data 14, the weight w decreasing as the value indicating the structure-likeliness set for the pixel increases.

[0105] Then, the correction gain calculation function 170c calculates the multiplication value of the weight w set for the pixel and the luminance of the pixel for each of the plurality of pixels that make up the low-frequency component image data 14. Then, the correction gain calculation function 170c calculates the sum of the plurality of multiplication values calculated for the plurality of pixels that make up the low-frequency component image data 14. Then, the correction gain calculation function 170c calculates the correction gain based on the sum of the plurality of multiplication values.

[0106] For example, the correction gain calculation function 170c calculates the correction gain by subtracting the sum of multiple multiplication values from the target luminance 25. The correction gain calculated in this way is the correction gain corresponding to all pixels that make up the low-frequency component image data 14. Then, the gain correction function 170d generates the low-frequency component image data after gain correction by applying gain correction to the luminance of each pixel using the correction gain.

[0107] In the above-described embodiment, the structure identification function 170b automatically determines the threshold value 22 used when deriving pixels indicating a structure. However, the threshold value 22 may be determined by a user. For example, the structure identification function 170b displays, on the display 103, a reception screen for receiving the threshold value 22 used when deriving pixels indicating a structure. This reception screen includes the distributions 20 and 21 shown in FIG. 3 . Therefore, the user inputs an appropriate threshold value 22 to the processing circuit 170 via the input device 102 while visually checking the distributions 20 and 21. Then, the structure identification function 170b derives pixels indicating a structure based on the received threshold value 22.

[0108] In the above-described embodiment, the correction gain calculation function 170c automatically sets a weight w for each of the plurality of pixels constituting the low-frequency component image data 14. However, the user may determine the weight w for each pixel. For example, the correction gain calculation function 170c displays a reception screen for receiving the weight w on the display 103. This reception screen includes the distributions 20 and 21 shown in FIG. 3 above. Therefore, the user inputs an appropriate weight w for each pixel to the processing circuit 170 via the input device 102 while visually checking the distributions 20 and 21.

[0109] Here, the distributions 20 and 21 displayed on the display 103 are distributions based on the high frequency component image data 15, not the low frequency component image data 14. Therefore, the weights w input to the processing circuit 170 are the weights w for each pixel of the high frequency component image data 15. However, there is a one-to-one correspondence between each pixel of the high frequency component image data 15 and each pixel of the low frequency component image data 14. Therefore, the correction gain calculation function 170c can treat the weights w for each pixel of the input high frequency component image data 15 as the weights w for each pixel of the low frequency component image data 14.

[0110] Then, the correction gain calculation function 170c calculates a multiplication value of the input weight w for each pixel and the luminance of each of the plurality of pixels constituting the low-frequency component image data. This calculates a plurality of multiplication values (the number of all pixels). The correction gain calculation function 170c then calculates a correction gain based on the sum of the plurality of multiplication values.

[0111] Here, the program executed by the processor is provided in advance in a read-only memory (ROM) or a storage circuit. The program may be provided by being recorded on a computer-readable, non-transitory storage medium such as a compact disk (CD)-ROM, a flexible disk (FD), a recordable CD-R (CD-R), or a digital versatile disk (DVD) in a format that can be installed or executed on these devices. The program may also be provided or distributed by being stored on a computer connected to a network such as the Internet and downloaded via the network. For example, the program may be composed of modules including the above-described processing functions. In actual hardware, a CPU reads and executes the program from a storage medium such as a ROM, whereby each module is loaded into a main memory device and generated on the main memory device.

[0112] According to at least one of the embodiments or modifications described above, it is possible to calculate a correction gain that can make the luminance of the substantial portion uniform.

[0113] Although several 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, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0114] 1. Ultrasound diagnostic equipment 170 Processing Circuit 170a Resolution Modulation Function 170b Structure identification function 170c Correction gain calculation function

Claims

1. a generating unit that generates high-frequency component image data and low-frequency component image data having a second resolution lower than the first resolution by performing multi-resolution analysis on ultrasound image data having a first resolution; a derivation unit that derives information about a structure from the high frequency component image data; a calculation unit that calculates the correction gain based on the low frequency component image data based on information about the structure so that the contribution of luminance of pixels representing substantial parts in the low frequency component image data to the correction gain for correcting luminance of pixels in the low frequency component image data is higher than the contribution of luminance of pixels representing the structure in the low frequency component image data to the correction gain; An ultrasound diagnostic device comprising:

2. the derivation unit derives pixels indicating the structure as information about the structure from the high frequency component image data; the calculation unit determines, in the low-frequency component image data, a region configured by a plurality of pixels excluding pixels corresponding to pixels of the structure as the substantial portion, and calculates a correction gain based on the luminance of the substantial portion. The ultrasonic diagnostic apparatus according to claim 1 .

3. the derivation unit derives a value indicating a likelihood of a structure as information about the structure for each of a plurality of pixels constituting the high frequency component image data; the calculation unit calculates a multiplication value of a weight that decreases as the value indicating the structure-likelihood increases and a luminance of each of a plurality of pixels constituting the low-frequency component image data, and calculates the correction gain based on a sum of the multiple multiplication values. The ultrasonic diagnostic apparatus according to claim 1 .

4. The ultrasound diagnostic apparatus according to claim 2 , wherein the deriving unit derives the pixel indicating the structure based on a statistical distribution of a plurality of intensities of a plurality of pixels constituting the high frequency component image data.

5. The ultrasound diagnostic apparatus according to claim 4 , wherein the derivation unit determines a threshold value for deriving the pixels representing the structure based on the statistical distribution, and derives the pixels representing the structure based on the threshold value.

6. 5. The ultrasound diagnostic device according to claim 4, wherein the derivation unit causes a display unit to display a reception screen for receiving a threshold value used in deriving the pixels representing the structure, and derives the pixels representing the structure based on the received threshold value.

7. 4. The ultrasound diagnostic apparatus according to claim 3, wherein the calculation unit causes a display unit to display a reception screen for receiving the weights, calculates the multiplication values of the received weights and the luminance of each of the plurality of pixels constituting the low-frequency component image data, and calculates the correction gain based on a sum of the plurality of multiplication values.

8. 7. The ultrasonic diagnostic apparatus according to claim 1, wherein the calculation unit calculates the correction gain for each pixel based on a target luminance and each luminance of a plurality of pixels constituting the substantial portion.

9. 3. The ultrasound diagnostic apparatus according to claim 1, wherein the calculation unit divides the low-frequency component image data into a plurality of regions, and for each region, calculates an average value of luminance of the substantial portion, excluding pixels representing the structure, as the substantial portion, and calculates the correction gain based on the average value of luminance and a target luminance.

10. 10. The ultrasound diagnostic device according to claim 9, wherein the calculation unit complements the correction gain of a region among the plurality of regions where a ratio of the number of pixels representing the structure to the number of all pixels is equal to or greater than a threshold value, using the correction gain calculated for regions surrounding the region.

11. On the computer, performing multi-resolution analysis on ultrasound image data having a first resolution to generate high-frequency component image data and low-frequency component image data having a second resolution lower than the first resolution; deriving information about the structure from the high frequency component image data; calculating the correction gain based on the low frequency component image data so that the contribution of the luminance of pixels representing the substantial part in the low frequency component image data to the correction gain for correcting the luminance of pixels of the low frequency component image data is higher than the contribution of the luminance of pixels representing the structure in the low frequency component image data to the correction gain based on the information about the structure; A program for executing a process.

Images