Information processing device, ultrasound diagnostic device, information processing method and program

Abstract

To improve image quality. [Solution] The information processing device according to the embodiment comprises a conversion unit, a calculation unit, and a generation unit. The conversion unit performs a conversion process on the first data to generate second data, which is a data array classified by base information. The calculation unit performs a segmentation process on the second data classified by the base information to generate third data consisting of multiple data. The generation unit synthesizes the multiple data in the third data to generate fourth data.

Description

Technical Field

[0001] The embodiments disclosed in this specification and the drawings relate to an information processing apparatus, an ultrasonic diagnostic apparatus, an information processing method, and a program.

Background Art

[0002] In an ultrasonic diagnostic apparatus, a quantitative value representing the morphological information of blood vessels may be calculated. To calculate a quantitative value representing the morphological information of blood vessels, for example, a binary image is generated from a signal obtained by ultrasonic transmission and reception, and the quantitative value is calculated from the generated binary image.

[0003] However, if there is brightness variation in the image, it may not be possible to appropriately determine the threshold value during binarization, and the accuracy of binarization may decrease.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Patent Document 3

Summary of the Invention

Problems 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 improve the image quality. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problems. The problems corresponding to the respective effects of each configuration shown in the embodiments described later can also be regarded as other problems.

Means for Solving the Problems

[0006] The information processing device according to this embodiment comprises a conversion unit, a calculation unit, and a generation unit. The conversion unit performs a conversion process on the first data to generate second data, which is a data array classified by base information. The calculation unit performs a segmentation process on the second data classified by the base information to generate third data consisting of multiple data. The generation unit synthesizes the multiple data in the third data for each of the base information to generate fourth data. [Brief explanation of the drawing]

[0007] [Figure 1] Figure 1 is a diagram showing an example of the configuration of an ultrasound diagnostic device according to an embodiment. [Figure 2] Figure 2 is a flowchart illustrating the processing flow performed by the information processing device according to the embodiment. [Figure 3] Figure 3 is a diagram illustrating the processing flow performed by the information processing device according to the embodiment. [Figure 4] Figure 4 is a diagram illustrating the data used to verify the information processing method according to the embodiment. [Figure 5] Figure 5 shows an example of the output result from the information processing device according to the embodiment. [Figure 6] Figure 6 shows the output results for the comparative example. [Figure 7] Figure 7 shows an example of a frame sum signal, which is the first comparative example. [Figure 8] Figure 8 shows an example of an output image in the second comparative example, where a frame-sum signal has been subjected to binarization processing using an adaptive threshold. [Figure 9] Figure 9 shows an example of an output image of the information processing device according to the embodiment. [Figure 10] Figure 10 shows an example of a monochrome output image produced by the information processing device according to this embodiment. [Figure 11] Figure 11 shows an example of a color output image produced by the information processing device according to the embodiment. [Modes for carrying out the invention]

[0008] The following describes in detail embodiments of the information processing device, ultrasound diagnostic device, information processing method, and program, with reference to the drawings.

[0009] Figure 1 is a block diagram showing the configuration of an ultrasound diagnostic apparatus according to an embodiment. The ultrasound diagnostic apparatus 110 is a device that generates ultrasound data based on the received signal (reflected wave signal) received from the ultrasound probe 105. The ultrasound diagnostic apparatus 110 shown in Figure 1 is capable of generating two-dimensional ultrasound data based on two-dimensional received signals and is capable of generating three-dimensional ultrasound data based on three-dimensional received signals. However, the embodiment is also applicable even if the ultrasound diagnostic apparatus 110 is a device dedicated to two-dimensional data. The ultrasound diagnostic apparatus 110 comprises a transmission circuit 109, a receiving circuit 111, and an information processing device 100.

[0010] The ultrasound probe 105 is, for example, an electronic scanning probe, and has a plurality of transducers 101 arranged in one or two dimensions at its tip. The transducers 101 are piezoelectric elements (electromechanical conversion elements) that perform interconversion between electrical signals (voltage pulse signals) and ultrasound (acoustic waves). The ultrasound probe 105 transmits ultrasound from the plurality of transducers 101 to the subject and receives the reflected ultrasound from the subject using the plurality of transducers 101. The reflected acoustic waves reflect the difference in acoustic impedance within the subject. When the transmitted ultrasound pulse is reflected by a moving blood flow or the surface of the heart wall, the reflected ultrasound undergoes a frequency shift due to the Doppler effect, depending on the velocity signal component of the moving object relative to the ultrasound transmission direction.

[0011] The probe connection unit 103 connects to the ultrasonic probe 105 and transmits and receives ultrasonic waves between it and the ultrasonic probe 105. The means of connecting the ultrasonic probe 105 by the probe connection unit 103 may be either wired or wireless. In the case of a wired connection, the probe connection unit 103 has a connector unit (receptacle) to which the connector (plug) of the ultrasonic probe 105 is connected. In the case of a wireless connection, it has a communication unit that performs wireless communication with the ultrasonic probe 105.

[0012] The transmitting circuit 109 is a transmitting unit that outputs pulse signals (drive signals) to multiple transducers 101. By applying pulse signals to multiple transducers 101 with a time difference, ultrasonic waves with different delay times are transmitted from the multiple transducers 101, forming a transmitted ultrasonic beam. The direction and focus of the transmitted ultrasonic beam can be controlled by selectively changing the transducer 101 to which the pulse signal is applied, or by changing the delay time (application timing) of the pulse signal. By sequentially changing the direction and focus of this transmitted ultrasonic beam, the observation area inside the subject is scanned. Furthermore, by changing the delay time of the pulse signal, a transmitted ultrasonic beam that is a plane wave (focus is far away) or a diffuse wave (focus point is opposite to the ultrasonic transmission direction for multiple transducers 101) may be formed. Alternatively, the transmitted ultrasonic beam may be formed using one transducer or a portion of multiple transducers 101. The transmitting circuit 109 transmits a pulse signal of a predetermined drive waveform to the transducer 101, thereby generating transmitted ultrasonic waves with a predetermined transmission waveform in the transducer 101.

[0013] The receiving circuit 111 is a receiving unit that inputs the electrical signal output from the transducer 101, which receives reflected ultrasonic waves, as the received signal. The received signal is input to the processing circuit 150. In this embodiment, both the analog signal output from the transducer 101 and the digital data obtained by sampling (digital conversion) it are referred to as the received signal without any particular distinction. However, depending on the context, the received signal may be referred to as received data or measurement data to clearly indicate that it is digital data.

[0014] The information processing apparatus 100 is connected to a transmission circuit 109 and a reception circuit 111, and executes processing of signals received from the reception circuit 111 and control of the transmission circuit 109. The information processing apparatus 100 includes a processing circuit 150, a memory 132, an input device 134, and a display 135.

[0015] The memory 132 is composed of a semiconductor memory element such as a RAM (Random Access Memory), a flash memory, a hard disk, an optical disk, etc. The memory 132 is a memory that stores data such as display image data generated by the processing circuit 150. Further, the memory 132 can also store the reception signal (reflected wave signal) output by the reception circuit 111. In addition, the memory 132 stores, as necessary, a control program for performing ultrasonic transmission and reception, image processing, and display processing, diagnostic information (for example, patient ID, doctor's findings, etc.), diagnostic protocols, and various data such as various body marks.

[0016] The input device 134 receives various instructions and information inputs from an operator. The input device 134 is composed of, for example, an input interface device such as a mouse, a keyboard, a button, a trackball, etc.

[0017] The display 135 receives input of imaging conditions through a GUI (Graphical User Interface) and displays various images under the control of the processing circuit 150. The display 135 is composed of, for example, a display interface device such as a liquid crystal display.

[0018] The processing circuit 150 includes an acquisition function 150a, a conversion function 150b, a calculation function 150c, a generation function 150d, and a display control function 150e. In this embodiment, each processing function performed by the acquisition function 150a, the conversion function 150b, the calculation function 150c, the generation function 150d, and the display control function 150e is stored in memory 132 in the form of a program that can be executed by a computer. The processing circuit 150 is a processor that reads the program from memory 132 and executes it to realize the function corresponding to each program. In other words, the processing circuit 150 in the state in which each program has been read will have the functions shown in the processing circuit 150 of Figure 1. In Figure 1, the processing functions performed by the acquisition function 150a, the conversion function 150b, the calculation function 150c, the generation function 150d, and the display control function 150e are realized by a single processing circuit 150, but it is also possible to configure the processing circuit 150 by combining multiple independent processors, and each processor can realize the functions by executing a program. In other words, each of the above functions may be configured as a program, and one processing circuit 150 may execute each program. As another example, a specific function may be implemented in a dedicated, independent program execution circuit. In Figure 1, the acquisition function 150a, conversion function 150b, calculation function 150c, generation function 150d, and display control function 150e are examples of the acquisition unit, conversion unit, calculation unit, generation unit, and display control unit, respectively.

[0019] Furthermore, the term "processor" used in the above explanation refers to circuits such as CPUs (Central Processing Units), GPUs (Graphical Processing Units), Application Specific Integrated Circuits (ASICs), and Programmable Logic Devices (e.g., Simple Programmable Logic Devices (SPLDs), Complex Programmable Logic Devices (CPLDs), and Field Programmable Gate Arrays (FPGAs)). The processor performs its functions by reading and executing programs stored in memory 132.

[0020] The processing circuit 150, using the acquisition function 150a, causes the ultrasonic probe 105 to perform an ultrasonic scan and collects multiple time-sequential frame data (multiple frame data within a predetermined time) obtained by the execution of the ultrasonic scan.

[0021] Furthermore, the processing circuit 150 performs phase-alignment summing and quadrature detection processing on the received signal (CH data) collected via the receiving circuit 111 using phase-alignment summing and quadrature detection functions (not shown). Phase-alignment summing is a process that adds up the received signals from multiple oscillators 101 by changing the delay time and weight for each oscillator 101, and is also called Delay and Sum (DAS) beamforming. Quadrature detection is a process that converts the received signal into a common-mode signal and a quadrature signal (IQ data (measurement data)) in the baseband bandwidth. In addition, adaptive beamforming, model-based processing, and processing using machine learning may also be performed on the received signal.

[0022] Furthermore, the processing circuit 150 may use a correction function (not shown) to estimate the amount of tissue displacement due to the subject's body movement, etc., between multiple frame data, and correct each frame data based on the estimated result.

[0023] Furthermore, the processing circuit 150, using the generation function 150d, performs envelope detection processing, logarithmic compression processing, etc., to generate B-mode data (data with information extracted or enhanced from tissue) in which the signal intensity at each point within the observation area is represented by brightness. The processing circuit 150, using the generation function 150d, also generates blood flow data (power signal data) in which information derived from blood flow in the measurement data is extracted or enhanced.

[0024] For example, the processing circuit 150 applies an MTI (Moving Target Indicator) filter to multiple frame data using a correction function (not shown). This reduces information originating from stationary or minimally moving tissues between frames (tissue signal component (clutter)) and extracts information originating from blood flow (blood flow signal component). As the MTI filter, a filter with fixed filter coefficients, such as a Butterworth-type IIR (infinite impulse response) filter or a polynomial regression filter, may be used. The MTI filter may also be an adaptive filter that changes its coefficients according to the input signal using eigenvalue decomposition or singular value decomposition.

[0025] Furthermore, the processing circuit 150 reads and executes a program stored in the memory 132, thereby activating the conversion function 150b, the calculation function 150c, and the generation function 150d, and performing processing to output high-resolution ultrasonic data. Details of these processes will be described later. In addition, the processing circuit 150 displays the output high-resolution ultrasonic data on the display unit, the display 135, using the display control function 150e.

[0026] The processing circuit 150 may also decompose the frame data into multiple basis sets by eigenvalue decomposition or singular value decomposition, and extract information derived from tissue by removing specific basis sets. Alternatively, the processing circuit 150 may use methods such as vector Doppler, speckle tracking, or vector flow mapping to obtain velocity vectors for each coordinate in the received signal data and obtain blood flow vectors that represent the magnitude and direction of blood flow. In addition to the methods exemplified here, any method that can extract or enhance information derived from blood flow, or remove or reduce information derived from tissue, contained in the frame data (received data, measurement data) may be adopted.

[0027] Next, the background of the embodiment will be explained.

[0028] In ultrasound diagnosis, morphological information about blood vessels is sometimes used to make a diagnosis. For example, to differentiate between benign and malignant tumors, a diagnosis may be made based on information about the number and diameter of blood vessels. In this case, it is conceivable to calculate quantitative values ​​that represent the morphological information of blood vessels. One method for calculating quantitative values ​​that represent the morphological information of blood vessels is to generate a binarized image from the image obtained based on the signal obtained by ultrasound transmission and reception, perform edge detection on the generated binarized image, and then calculate the quantitative value.

[0029] Here, in order to obtain quantitative values ​​representing the morphological information of blood vessels with high accuracy, high accuracy in binarization is necessary. In other words, if the threshold when generating the binarized image is too small or too large, the diameter and number of blood vessels cannot be calculated accurately.

[0030] However, the accuracy of binarization may decrease due to variations in brightness within the image. For example, when generating blood flow images, if a filter (wall filter) is applied to remove slow signals to eliminate tissue components, the brightness of slow blood flow decreases due to the filter, and accuracy may not be obtained even if a common binarization threshold is used across the entire region. Therefore, one could consider, for example, dividing the original data by blood flow velocity, performing binarization for each blood flow velocity, and then synthesizing the binarized images. More generally, one could consider some kind of baseline information, classify the data using that baseline information, perform binarization for each baseline information, and then synthesize the data.

[0031] The embodiment is based on this idea, and the information processing device 100 according to the embodiment includes a processing circuit 150. The processing circuit 150 performs a conversion process on the first data using a conversion function 150b to generate second data, which is a data array classified by base information. The processing circuit 150 performs a segmentation process on the second data classified by base information using a calculation function 150c to generate third data consisting of multiple data. The processing circuit 150 uses a generation function 150d to synthesize the multiple data in the third data to generate fourth data.

[0032] Furthermore, the ultrasound diagnostic apparatus 110 according to the embodiment includes a processing circuit 150, which acquires first data obtained based on the transmission and reception of ultrasound from the ultrasound probe using an acquisition function 150a. The processing circuit 150 in the ultrasound diagnostic apparatus 110 also performs the above-mentioned processing using a conversion function 150b, a calculation function 150c, and a generation function 150d.

[0033] Furthermore, the information processing method according to the embodiment performs a conversion process on the first data to generate a second data, which is a data array classified by base information; performs a segmentation process on the second data classified by base information to generate a third data consisting of multiple data; and synthesizes the multiple data in the third data to generate a fourth data. The program according to the embodiment causes a computer to execute the above-described processes.

[0034] The process described above will be explained using Figures 2 and 3. Figure 2 is a flowchart illustrating the flow of processing performed by the information processing device 100 according to the embodiment. Figure 3 is a diagram illustrating the flow of processing performed by the information processing device 100 according to the embodiment.

[0035] First, in step S100, the processing circuit 150 acquires first data obtained based on the transmission and reception of ultrasound from the ultrasound probe using the acquisition function 150a. Here, the first data acquired by the processing circuit 150 using the acquisition function 150a is data consisting of multiple frames. As an example, as shown in Figure 3, the processing circuit 150 acquires first data 10, which is data consisting of multiple frames of frame data 10a, 10b, 10c, and 10d, using the acquisition function 150a.

[0036] In other words, the ultrasound diagnostic apparatus 110 according to this embodiment causes the ultrasound probe to perform an ultrasound scan and collects first data 10, which is a plurality of time-continuous frame data (a plurality of frame data within a predetermined time) obtained by the execution of the ultrasound scan. The first data 10, which is frame data, is collected at a predetermined frame rate by the execution of the ultrasound scan. Here, the first data 10, which is frame data, refers to any of the following: received data, measurement data, blood flow data, or tissue data. Received data is, for example, the received ultrasound signal received by the ultrasound probe (e.g., CH data). Measurement data is data obtained by performing phase addition and quadrature detection processing on the received ultrasound signal (e.g., IQ data). Blood flow data is data obtained by ultrasound transmission and reception, in which information derived from blood flow in the measurement data is extracted or emphasized (e.g., power signal data). Tissue data is data in which information derived from tissue is extracted or emphasized (e.g., B-mode data).

[0037] The measurement data includes, for example, tissue-derived information (tissue signal component (clutter)) and blood flow-derived information (blood flow signal component). Blood flow-derived information may include not only information derived from blood but also information derived from contrast agents in the blood. Furthermore, blood flow data is data in which blood flow-derived information has been extracted or enhanced, and includes blood flow velocity values, variance values, and power values.

[0038] Extracting information derived from blood flow is, for example, the operation of extracting the blood flow signal component from measurement data. Enhancing information derived from blood flow is, for example, the operation of making the blood flow signal component more prominent relative to the tissue signal component. Note that blood flow data may be obtained by processing to extract or enhance information derived from blood flow, or by processing to remove or reduce information derived from tissue.

[0039] Next, in step S200, the processing circuit 150 uses the conversion function 150b to perform a conversion process on the first data to generate second data, which is a data array classified by the basis information. Here, the conversion process is, for example, a Fourier transform, and the conversion basis is, for example, a Fourier basis. In this case, the second data generated in step S200 becomes a data array classified by the Fourier basis. Here, for example, when depicting blood flow, the Fourier frequency is a quantity corresponding to the blood flow velocity, so in this case, the second data generated in step S200 becomes a data array classified by the blood flow velocity. As shown in Figure 3, the processing circuit 150 uses the conversion function 150b to generate data 20a, 20b, 20c, and 20d obtained for each basis information based on the frame data 10a, 10b, 10c, and 10d. Data 20a, 20b, 20c, and 20d constitute the second data 20.

[0040] Next, in step S300, the processing circuit 150 performs segmentation processing on the second data 20 classified by the base information using the calculation function 150c, and generates a third data 30 consisting of multiple data. As an example, the processing circuit 150 performs segmentation processing by creating a binarization map from the second data 20 using the calculation function 150c, and generates a third data 30 consisting of multiple data. Specifically, as shown in Figure 3, the processing circuit 150 performs segmentation processing by creating binarization maps, data 30a, 30b, 30c, and 30d, from data 20a, 20b, 20c, and 20d respectively for each piece of base information using the calculation function 150c, and generates a third data 30 consisting of multiple data. The binarization maps, data 30a, 30b, 30c, and 30d, constitute the third data 30.

[0041] Next, in step S400, the processing circuit 150 generates the fourth data 40 by synthesizing the third data 30 using the generation function 150d. As an example, as shown in Figure 3, the processing circuit 150 generates the fourth data 40 by synthesizing the binarization maps 30a, 30b, 30c, and 30d using the generation function 150d. As an example, the processing circuit 150 generates the fourth data 40 by counting the number of times each pixel of the binarization maps 30a, 30b, 30c, and 30d created for each base information is above the threshold, above the threshold, below the threshold, or below the threshold, using the generation function 150d. The reason for counting the number of times each pixel is above the threshold, etc., is that when comparing noise data and signal data, the signal data is consistently above or below the threshold regardless of the base information, compared to the noise data. Therefore, the processing circuit 150 can reduce the contribution of noise components and improve image quality by aggregating the number of times each pixel is above a threshold, above a threshold, below a threshold, or below a threshold for the binarized maps 30a, 30b, 30c, and 30d created for each basis information by the generation function 150d.

[0042] As another example of a method for synthesizing the fourth data 40, the processing circuit 150 generates the fourth data based on the magnitude of the variation in the value of the third data (or second data) for each pixel, using the generation function 150d for the binarized maps 30a, 30b, 30c, and 30d created for each base information. As an example, the processing circuit 150 generates the fourth data by extracting pixels where the magnitude of the variation in the value of the third data is smaller than a threshold, using the generation function 150d. For example, in a certain pixel, the data for each frame corresponds to the speeds "slow 1, slow 2, slow 3, fast 1, fast 2, fast 3", where the speeds are slow 1 < slow 2 < slow 3 < fast 1 < fast 2 < fast 3. Here, comparing the data "1, 1, 1, 0, 0, 0" with the data "1, 0, 1, 1, 0, 1", the former is likely to be a signal because it has continuity, while the latter is likely to be noise because it lacks continuity. Therefore, the processing circuit 150 generates a fourth data based on the magnitude of the fluctuation in the value of the third data (or second data) created for each base information by the generation function 150d.

[0043] In step S400, the processing circuit 150 may calculate feature quantities 50 from the third data 30 or the like, instead of generating the fourth data 40 using the generation function 150d. In this case, the processing circuit 150 calculates the feature quantities 50 of the object from the second data 20, the third data 30, or the fourth data 40 using the calculation function 150c. For example, if the object is a blood vessel, the processing circuit 150 calculates the diameter of the blood vessel, the degree of tortuousness of the blood vessel, etc., as feature quantities of the blood vessel region using the calculation function 150c.

[0044] The effectiveness of the processing performed by the information processing device 100 according to this embodiment was simulated. These simulation results will be explained using Figures 4 to 6.

[0045] In Figure 4, Image 60 shows the original image from which the simulation was performed. In Image 60, region 61 corresponds to a low-velocity blood flow region, and region 62 corresponds to a high-velocity blood flow region. Ultrasonic signal data from multiple frames was created to simulate a situation in which both region 61 (low-velocity blood flow region) and region 62 (high-velocity blood flow region) exist. This data was then subjected to binarization and noise reduction processing using both the conventional method and the method according to the embodiment.

[0046] Figure 5 shows a comparative example where binarization and noise reduction processing were performed using the conventional method. Image 70 shows the original image, and Image 80 shows the image after noise reduction processing when binarization and noise reduction processing were performed using the conventional method. In the comparative example, a certain amount of noise remains in the image after noise reduction.

[0047] Figure 6 shows the results of binarization and noise reduction processing performed using the method according to the embodiment, for each basis information. In the example in Figure 6, the transformation function 150b generates the second data from the first data using the Fourier transform, and the basis information is the Fourier basis. That is, the basis information is information that roughly corresponds to the blood flow velocity.

[0048] In the upper panel of Figure 6, data 20a, 20b, 20c, and 20d represent the components of the second data for each baseline information. Data 20a is the baseline information for the lowest frequency component, i.e., the image corresponding to low-velocity blood flow, and as we move to data 20b, 20c, and 20d, the baseline information corresponds to the high-frequency component, i.e., the image corresponding to high-velocity blood flow.

[0049] Furthermore, in the lower part of Figure 6, data 30a, 30b, 30c, and 30d show the data components that have been binarized and denoised, categorized by baseline information. Data 30a is the baseline information for the lowest frequency component, i.e., the image component corresponding to low-velocity blood flow, and as we move to data 30b, 30c, and 30d, the baseline information corresponds to the high-frequency component, i.e., the data for high-velocity blood flow. In the method of this embodiment, the peak positions of data 30a, 30b, 30c, and 30d differ for each baseline information. Therefore, in this embodiment, it is possible to distinguish between blood vessels with high and low flow velocities and to change the threshold of the binarization mask according to the baseline information. This makes it possible to appropriately depict blood vessels.

[0050] Using Figures 7 to 9, examples of images output by the information processing device 100 according to the embodiment will be explained. Figures 7 and 8 are images related to comparative examples. Image 90 in Figure 7 is the first comparative example and shows an image obtained by simply adding up multiple frames of acquired ultrasonic signals. Image 91 in Figure 8 is the second comparative example and is an example of an output image when an image obtained by simply adding up multiple frames of acquired ultrasonic signals is subjected to binarization processing using an adaptive threshold. In contrast, Image 92 in Figure 9 shows an example of an output image output by the information processing device 100 according to the embodiment. In Image 92 in Figure 9, a binarization map is created for each base information and then the images are synthesized, so unlike Images 90 and 91, structures that could not be depicted in the first comparative example and the second comparative example, such as region 93 and region 94, can be clearly depicted.

[0051] As described above, in the first embodiment, the processing circuit 150 performs a conversion process on the first data to generate second data, which is a data array classified by the basis information. It then performs segmentation processing on each piece of basis information to perform binarization and generate third data. After that, it synthesizes multiple data points in the third data to generate fourth data, or calculates features from the third data. This improves the accuracy of the binarization process and improves image quality.

[0052] (Other embodiments) In the above-described embodiment, the basis information is explained as a data array in which the second data is classified by a Fourier basis, and as a result the basis information corresponds to the velocity of an object such as blood flow. However, the embodiment is not limited to this. For example, the basis transformation may be performed using Legendre transforms, wavelet transforms, or expansions using various orthogonal bases other than Fourier transforms, such as spherical harmonics.

[0053] As another example, the processing circuit 150 may use information about the dispersion, direction, or displacement of the object as base information to perform the processing in steps S200 to S400.

[0054] When generating second data using the dispersion or displacement of an object as base information, for example, the magnitude of the dispersion / displacement of the first data in the frame direction becomes the base information. In this case, in step S200, the processing circuit 150 uses the calculation function 150c to divide the first data into multiple second data depending on the magnitude of the dispersion / displacement of each pixel in the frame direction. As an example, the processing circuit 150 uses the calculation function 150c to divide the first data into multiple second data depending on the dispersion of the power value of the signal in the frame direction. As another example, the processing circuit 150 uses the calculation function 150c to divide the first data into multiple second data depending on the amount of tissue variation due to body movement at each point. In step S300, the processing circuit 150 uses the generation function 150d to perform segmentation processing on the second information for each magnitude of the dispersion / displacement in the frame direction, which is the base information, to generate a third data, which is a binarization mask. In step S400, the processing circuit 150 either generates a fourth data set by combining multiple data points in the third data set using the generation function 150d, or calculates feature quantities from the third data set using the calculation function 150c.

[0055] Furthermore, when generating second data using the direction of the object as base information, for example, the direction of blood flow (direction of blood vessels) becomes the base information. As an example, the processing circuit 150 classifies the direction of blood vessels into one of eight directions: 0 degrees, 45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 270 degrees, and 325 degrees. In steps S200 and S300, the processing circuit 150 uses the calculation function 150c to classify each blood vessel in the first data according to the direction of blood flow (direction of blood vessels) at each pixel of the first data, and segments it into multiple second data to generate third data, which is a binarized mask separated by the direction of blood flow (direction of blood vessels). In step S400, the processing circuit 150 uses the generation function 150d to synthesize multiple data from the third data to generate fourth data, or uses the calculation function 150c to calculate features from the third data.

[0056] Furthermore, the selection of the basis information is not limited to the example described above. The processing circuit 150 may use the harmonic order in the ultrasound transmission as the basis information to process and classify the second data. In this case, in step S100, the type of first data acquired by the processing circuit 150 using the acquisition function 150a is, for example, received data from among received data, measurement data, blood flow data, and tissue data. In step S200, the processing circuit 150 uses the calculation function 150c to divide the first data into a plurality of second data according to the harmonic order in the ultrasound transmission. In step S300, the processing circuit 150 uses the generation function 150d to perform segmentation processing on the second information for each harmonic order in the ultrasound transmission, which is the basis information, to generate a third data, which is a binarization mask. In step S400, the processing circuit 150 either generates a fourth data set by combining multiple data points in the third data set using the generation function 150d, or calculates feature quantities from the third data set using the calculation function 150c.

[0057] Furthermore, the selection of base information is not limited to the example described above. In step S200, the processing circuit 150 may generate second data by dividing the first data based on the morphological information of the object obtained from the first data using the conversion function 150b. For example, if the object is a blood vessel, in step S200, the processing circuit 150 obtains power images, which are blood flow data, from the first data, calculates morphological information such as the blood vessel diameter based on the obtained power images, and calculates an estimated value of blood flow velocity based on the calculated morphological information. Based on the estimated value of blood flow velocity calculated based on the morphological information such as the blood vessel diameter, the processing circuit 150 generates second data by dividing the first data using the conversion function 150b. In this case, the first data is not limited to data from multiple frames, but may be data from a single frame.

[0058] Furthermore, as a user interface according to this embodiment, the processing circuit 150 may, by means of the display control function 150e, display objects determined to be different segments in the segmentation process in step S300 on the display unit, the display 135, in different colors.

[0059] Examples of such cases are illustrated using Figures 10 and 11. Image 200 in Figure 10 is an example of an output image when the processing circuit 150 outputs the fourth data as a monochrome output image using the display control function 150e. In contrast, image 210 in Figure 11 is an example of an output image when the processing circuit 150 outputs the fourth data as a color image using the display control function 150e. In the color image 210, regions 211, 212, and 213 with different blood flow velocities are recognized as separate parts and displayed in different colors, allowing the user to more easily recognize different parts.

[0060] Furthermore, the user interface according to the embodiment is not limited to the example described above. For example, the processing circuit 150 may display the third data and the fourth data in parallel on the display unit, the display 135, using the display control function 150e. By displaying the third data classified by the base information, for example, data for each blood flow velocity, and the combined fourth data in parallel on the display unit, the user can, for example, check the blood flow velocity while checking the output image.

[0061] According to at least one embodiment described above, image quality can be improved.

[0062] While several embodiments have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be implemented in a variety of other forms, and various omissions, substitutions, modifications, and combinations of embodiments are possible without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of Symbols]

[0063] 100 Information Processing Devices 110 Ultrasound diagnostic equipment 132 memory 134 Input device 135 displays 150 Processing Circuits 150a acquisition function 150b conversion function 150c calculation function 150d generation function

Claims

1. A conversion unit performs a conversion process on the first data to generate a second data classified by the base information, A calculation unit performs segmentation processing on the second data classified by the aforementioned base information to generate a third data consisting of multiple data, A generation unit that synthesizes multiple data in the third data to generate a fourth data, An information processing device equipped with the following features.

2. The information processing apparatus according to claim 1, wherein the first data is data from multiple frames.

3. The information processing apparatus according to claim 2, wherein the transformation unit performs a Fourier transform on the first data to generate the second data which is classified by a Fourier basis.

4. The information processing apparatus according to claim 1, wherein the first data is blood flow data obtained by ultrasonic transmission and reception.

5. The information processing apparatus according to claim 1, wherein the calculation unit generates the third data by performing the segmentation process by creating a binarized map from the second data.

6. The information processing apparatus according to claim 5, wherein the generation unit generates the fourth data by aggregating the number of times, for each pixel, that the binarized map created for each base information is greater than or equal to a threshold, exceeds a threshold, is less than or equal to a threshold, or is less than a threshold.

7. The information processing apparatus according to claim 1, wherein the base information is the velocity of the object.

8. The information processing apparatus according to claim 1, wherein the basis information is information on the dispersion, direction, or displacement of an object.

9. The information processing apparatus according to claim 1, wherein the basis information is the order of harmonics in ultrasonic transmission.

10. The information processing apparatus according to claim 1, wherein the calculation unit calculates the characteristic quantity of an object from the second data, the third data, or the fourth data.

11. The information processing apparatus according to claim 1, wherein the generation unit generates the fourth data based on the magnitude of the variation in the value of the second data or the third data for each pixel.

12. The information processing apparatus according to claim 1, wherein the conversion unit generates the second data by dividing the first data based on morphological information of blood vessels obtained from the first data.

13. The information processing apparatus according to claim 1, further comprising a display control unit that causes objects determined to be different segments in the segmentation process to be displayed on a display unit in different colors.

14. The information processing apparatus according to claim 1, further comprising a display control unit that displays the third data and the fourth data in parallel on a display unit.

15. An acquisition unit that acquires first data obtained based on the transmission and reception of ultrasound from an ultrasound probe, A conversion unit performs a conversion process on the first data to generate a second data, which is a data array classified by the base information. A calculation unit performs segmentation processing on the second data classified by the aforementioned base information to generate a third data consisting of multiple data, A generation unit that synthesizes multiple data in the third data to generate a fourth data, An ultrasound diagnostic device equipped with the following features.

16. The first data is transformed to generate the second data, which is a data array classified by the base information. Segmentation processing is performed on the second data classified by the aforementioned base information to generate a third data consisting of multiple data points. An information processing method for generating a fourth data by combining multiple data in the third data.

17. On the computer, The first data is transformed to generate the second data, which is a data array classified by the base information. Segmentation processing is performed on the second data classified by the aforementioned base information to generate a third data consisting of multiple data points. A program that performs a process to generate a fourth data by combining multiple data in the third data.

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