CN106725612B - Four-dimensional ultrasonic image optimization method and system - Google Patents

Four-dimensional ultrasonic image optimization method and system Download PDF

Info

Publication number
CN106725612B
CN106725612B CN201611205838.XA CN201611205838A CN106725612B CN 106725612 B CN106725612 B CN 106725612B CN 201611205838 A CN201611205838 A CN 201611205838A CN 106725612 B CN106725612 B CN 106725612B
Authority
CN
China
Prior art keywords
image
image data
optimization
dimensional
slice
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201611205838.XA
Other languages
Chinese (zh)
Other versions
CN106725612A (en
Inventor
李萍
艾金钦
陈伟璇
许龙
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Sonoscape Medical Corp
Original Assignee
Sonoscape Medical Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Sonoscape Medical Corp filed Critical Sonoscape Medical Corp
Priority to CN201611205838.XA priority Critical patent/CN106725612B/en
Publication of CN106725612A publication Critical patent/CN106725612A/en
Priority to PCT/CN2017/093471 priority patent/WO2018113283A1/en
Application granted granted Critical
Publication of CN106725612B publication Critical patent/CN106725612B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0833—Detecting organic movements or changes, e.g. tumours, cysts, swellings involving detecting or locating foreign bodies or organic structures
    • A61B8/085—Detecting organic movements or changes, e.g. tumours, cysts, swellings involving detecting or locating foreign bodies or organic structures for locating body or organic structures, e.g. tumours, calculi, blood vessels, nodules
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0866—Detecting organic movements or changes, e.g. tumours, cysts, swellings involving foetal diagnosis; pre-natal or peri-natal diagnosis of the baby
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/48—Diagnostic techniques
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/70—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/73—Deblurring; Sharpening
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/90—Dynamic range modification of images or parts thereof
    • G06T5/92—Dynamic range modification of images or parts thereof based on global image properties
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10132—Ultrasound image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10132—Ultrasound image
    • G06T2207/10136—3D ultrasound image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20172—Image enhancement details
    • G06T2207/20192—Edge enhancement; Edge preservation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30044—Fetus; Embryo
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30048—Heart; Cardiac

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Molecular Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Pathology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Veterinary Medicine (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • Biophysics (AREA)
  • Public Health (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Gynecology & Obstetrics (AREA)
  • Pregnancy & Childbirth (AREA)
  • Vascular Medicine (AREA)
  • Ultra Sonic Daignosis Equipment (AREA)
  • Image Processing (AREA)

Abstract

The embodiment of the invention discloses a four-dimensional ultrasonic image optimization method and a system, wherein the four-dimensional ultrasonic image comprises a slice image and a three-dimensional image of a target object, and the method comprises the following steps: acquiring slice image data and three-dimensional image data of a target object; performing gray scale optimization on each frame of slice image data, and performing enhancement processing on the slice image data after the gray scale optimization to obtain a processed slice image; and performing parameter optimization on the three-dimensional image data to obtain an optimized three-dimensional image. The embodiment of the invention discloses a four-dimensional ultrasonic image optimization method and a four-dimensional ultrasonic image optimization system, which can optimize a four-dimensional ultrasonic image and obtain a four-dimensional ultrasonic image with better image performance and better three-dimensional effect.

Description

Four-dimensional ultrasonic image optimization method and system
Technical Field
The embodiment of the invention relates to the technical field of ultrasonic images, in particular to a four-dimensional ultrasonic image optimization method and system.
Background
Because attenuation, reflection and artifacts generated by the propagation of ultrasonic waves in different tissues (such as the heart, the skull, etc.) are different, if the same set of preset ultrasonic four-dimensional imaging parameters is used to image different tissues, the imaging requirements of different tissues cannot be met. For example, the difference in the characteristics of the ultrasound images of the fetus at different gestational weeks and different diagnostic sites is significant, and the requirement for imaging cannot be met even if the same set of preset ultrasound four-dimensional imaging parameters is adopted. However, only the doctor is required to manually adjust various imaging parameters, so that the real-time performance is low, the operation is complicated, an ideal imaging effect cannot be achieved, and the detection rate of abnormal lesions is greatly influenced.
It is necessary to say that the adjusted imaging parameters include adjustment of a dynamic range. The dynamic range and the like are defined in detail in the book "medical ultrasound equipment-principle design application" published by scientific and technical literature press at month 4 of 2012. In fact, the ultrasonic apparatus generally has corresponding input devices such as knobs or buttons for the user to adjust the required dynamic range, and the user can generally adjust the required dynamic range according to different diagnostic parts and the actual imaging requirements, for example, the dynamic range generally needs to be selected by abdominal ultrasonic diagnosis to be 50-55db, the value of the user selected or adjusted dynamic range is the user dynamic range value DrtDB, and the maximum value of the dynamic range that can be selected or adjusted by the user on the ultrasonic apparatus is the maximum dynamic range value DRMax that can be supported by the ultrasonic system or the ultrasonic apparatus. The article "debugging of an ultrasound imaging diagnostic apparatus" adjustment in dynamic range "disclosed by the hundred-degree library on 7/12/2014 discloses technical contents related to user dynamic range adjustment. Further, chinese patent having patent publication No. CN104217401A and a patent name "ultrasonic imaging method and apparatus" disclosed on 12, 17.2014, also discloses "user dynamic range" in the last two sentences of paragraph [0060 ].
Disclosure of Invention
The embodiment of the invention provides a four-dimensional ultrasonic image optimization method and system, which can be used for optimizing a four-dimensional ultrasonic image and obtaining a four-dimensional ultrasonic image with better image performance and better three-dimensional effect.
In a first aspect, an embodiment of the present invention provides a four-dimensional ultrasound image optimization method, where the four-dimensional ultrasound image includes a slice image and a three-dimensional image of a target object, the method includes:
acquiring slice image data and three-dimensional image data of a target object;
performing gray scale optimization on each frame of slice image data, and performing enhancement processing on the slice image data after the gray scale optimization to obtain a processed slice image;
and performing parameter optimization on the three-dimensional image data to obtain an optimized three-dimensional image.
Further, the step of performing gray scale optimization on each frame of the slice image comprises:
obtaining demodulated amplitude data from ultrasonic echo data of a target object;
calculating a standard characteristic value of the demodulated amplitude data;
calculating a representative value of the standard feature values in a depth direction;
calculating the difference between the standard characteristic value and the representative value to obtain a gain compensation curve;
and applying the gain compensation curve to the slice image data to obtain the slice image data with optimized gray scale.
Further, the step of performing enhancement processing on the slice image data after the gray scale optimization to obtain a processed slice image includes:
carrying out pyramid decomposition on the slice image data after gray scale optimization to obtain image sublayers of a plurality of frequency components;
respectively carrying out anisotropic diffusion processing on each sublayer image to obtain a plurality of sublayer images subjected to anisotropic diffusion processing;
and performing pyramid-based image reconstruction on the plurality of anisotropic diffusion processed sub-layer images to obtain processed slice images, wherein the pyramid-based image reconstruction is an inverse process of the pyramid decomposition.
Further, the pyramid decomposition step includes:
low-pass filtering the input image data;
down-sampling the filtered input image data according to the horizontal and vertical directions to obtain down-sampled images, wherein the input image data of each layer is the down-sampled data of the previous layer;
up-sampling the down-sampled image according to the horizontal and vertical directions to obtain an up-sampled image;
low-pass filtering the up-sampled image;
and subtracting the filtered up-sampling image from the input image data to obtain a sub-layer image with a preset frequency component.
Further, the step of performing anisotropic diffusion processing on each of the sub-layer images to obtain a plurality of sub-layer images after anisotropic diffusion processing includes:
low-pass filtering the sub-layer image;
calculating the horizontal gradient and the vertical gradient of the filtered sub-layer image;
constructing a tissue tensor according to the transverse gradient and the longitudinal gradient, and calculating an eigenvalue of the tissue tensor;
calculating an expansion tensor according to the tissue tensor and the eigenvalue;
and performing discrete anisotropic diffusion on the sublayer image according to the expansion tensor to obtain the sublayer image after the anisotropic diffusion treatment.
Further, the step of performing parameter optimization on the three-dimensional image data includes:
performing histogram distribution statistics on the three-dimensional image data, and performing adaptive equalization on the histogram to obtain equalized three-dimensional image data;
counting the mean value, the large-law threshold value and the boundary gray value of the voxel gray value of the balanced three-dimensional image data;
determining an optimization parameter value according to the mean value, the large law threshold value and the mapping relation between the boundary gray value and the optimization parameter;
and optimizing the three-dimensional image data by using the optimized parameter values and the rotating light angle to obtain an optimized three-dimensional image. In a second aspect, an embodiment of the present invention further provides a four-dimensional ultrasound image optimization system, where the four-dimensional ultrasound image includes a slice image and a three-dimensional image of a target object, the system includes:
the acquisition module is used for acquiring slice image data and three-dimensional image data of a target object;
the gray scale optimization module is used for performing gray scale optimization on each frame of slice image data;
the enhancement processing module is used for carrying out enhancement processing on the slice image data after the gray scale optimization to obtain a processed slice image;
and the parameter optimization module is used for performing parameter optimization on the three-dimensional image data to obtain an optimized three-dimensional image.
Further, the gray scale optimization module comprises:
the acquisition unit is used for acquiring demodulated amplitude data from the ultrasonic echo data of the target object;
the standard characteristic value calculating unit is used for calculating a standard characteristic value of the demodulated amplitude data;
a representative value calculation unit for calculating a representative value of the standard feature values in a depth direction;
a gain compensation curve obtaining unit for calculating a difference between the standard characteristic value and the representative value to obtain a gain compensation curve;
and the gray scale optimization unit is used for applying the gain compensation curve to the slice image data to obtain the slice image data after gray scale optimization.
Further, the enhancement processing module comprises:
the decomposition unit is used for carrying out pyramid decomposition on the slice image data after the gray scale optimization to obtain image sublayers of a plurality of frequency components;
the anisotropic diffusion processing unit is used for respectively carrying out anisotropic diffusion processing on each sublayer image to obtain a plurality of sublayer images after the anisotropic diffusion processing;
and the image reconstruction unit is used for carrying out pyramid-based image reconstruction on the plurality of anisotropic diffusion processed sub-layer images to obtain processed slice images, wherein the pyramid-based image reconstruction is the inverse process of the pyramid decomposition.
Further, the decomposition unit includes:
a first filtering subunit, configured to perform low-pass filtering on input image data;
the down-sampling subunit is used for down-sampling the filtered input image data according to the horizontal and vertical directions to obtain a down-sampled image, wherein the input image data of each layer is the down-sampled data of the previous layer;
the up-sampling sub-unit is used for up-sampling the down-sampled image according to the horizontal and vertical directions to obtain an up-sampled image;
the first filtering subunit is further configured to perform low-pass filtering on the up-sampled image;
and the sub-layer image acquisition sub-unit is used for subtracting the filtered up-sampling image from the input image data to obtain a sub-layer image with a preset frequency component.
Further, the anisotropic diffusion processing unit includes:
the second filtering subunit is used for performing low-pass filtering on the sub-layer image;
the gradiometer unit is used for calculating the transverse gradient and the longitudinal gradient of the filtered sublayer image;
the tissue tensor constructing subunit is used for constructing a tissue tensor according to the transverse gradient and the longitudinal gradient and calculating an eigenvalue of the tissue tensor;
the expansion tensor calculation operator unit is used for calculating and obtaining an expansion tensor according to the tissue tensor and the characteristic value;
and the discrete anisotropic diffusion subunit is used for performing discrete anisotropic diffusion on the sublayer image according to the expansion tensor to obtain the sublayer image after the anisotropic diffusion processing.
Further, the parameter optimization module comprises:
the execution unit is used for carrying out histogram distribution statistics on the three-dimensional image data and executing self-adaptive equalization on the histogram to obtain equalized three-dimensional image data;
the statistical unit is used for counting the mean value, the large-law threshold value and the boundary gray value of the voxel gray value of the balanced three-dimensional image data;
the optimization parameter value determining unit is used for determining an optimization parameter value according to the mean value, the law-wide threshold value and the mapping relation between the boundary gray value and the optimization parameter;
and the optimization unit is used for optimizing the three-dimensional image data by using the optimization parameter value and the rotation light angle so as to obtain an optimized three-dimensional image.
The four-dimensional ultrasonic imaging optimization method provided by the embodiment of the invention comprises the steps of firstly, acquiring slice image data and three-dimensional image data of a target object; then, carrying out gray scale optimization on each frame of slice image data, and carrying out enhancement processing on the slice image data after the gray scale optimization to obtain a processed slice image; finally, parameter optimization is carried out on the three-dimensional image data, and an optimized three-dimensional image is obtained; by the technical means, the four-dimensional ultrasonic image is optimized, and the four-dimensional ultrasonic image with better image performance and better three-dimensional effect is obtained.
Drawings
Fig. 1 is a schematic flow chart of a four-dimensional ultrasound image optimization method according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for performing gray scale optimization on each frame of slice image according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method for performing enhancement processing on slice image data after gray scale optimization according to an embodiment of the present invention;
FIG. 4 is a schematic flowchart illustrating a process of pyramidal decomposition of slice image data according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart of pyramidally decomposing slice image data according to an embodiment of the present invention;
fig. 6 is a schematic flowchart of a process of performing anisotropic diffusion processing on each sub-layer image to obtain a plurality of sub-layer images after the anisotropic diffusion processing according to an embodiment of the present invention;
FIG. 7 is a schematic flowchart of pyramid reconstruction on a sub-layer image according to an embodiment of the present invention;
FIG. 8 is a flowchart illustrating a method for performing parameter optimization on three-dimensional image data according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a four-dimensional ultrasound image optimization system according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the various steps may be rearranged. The process may be terminated when its steps are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Examples
Fig. 1 is a flowchart of a four-dimensional ultrasound image optimization method according to an embodiment of the present invention, where the method is applicable to scanning different tissues and/or organs to acquire ultrasound images, and the method can be implemented by a four-dimensional ultrasound image system, where the system can be implemented by hardware and/or software. The four-dimensional ultrasound image comprises a slice image and a three-dimensional image of a target object, and the method specifically comprises the following steps:
step 110, acquiring slice image data and three-dimensional image data of the target object.
In particular, the target objects may be different tissues (e.g. heart, skull, etc.) or fetuses of different gestational weeks and different diagnostic sites. Each four-dimensional ultrasound image includes A, B and C (sagittal, coronal, and transverse) three-plane slice images and a three-dimensional image. The slice image data and the three-dimensional image data of the acquired target object can be obtained by scanning the target object through an ultrasonic imaging system.
And 120, performing gray scale optimization on each frame of slice image data, and performing enhancement processing on the slice image data after the gray scale optimization to obtain a processed slice image.
Specifically, gray scale optimization of each frame of slice image data can be performed by firstly calculating a gain compensation curve of the slice image data and then processing the slice image data based on the calculated gain compensation curve, so that the slice image data after gray scale optimization can be obtained.
The enhancement processing of the slice image data after the gray scale optimization is specifically to perform enhancement processing of signal-to-noise ratio and edge definition on the slice image data after the gray scale optimization, and aims to obtain the slice image data with a larger signal-to-noise ratio and higher edge definition.
The slice image data after the gray scale optimization is subjected to enhancement processing, and image sub-layers with a plurality of frequency components can be obtained by carrying out pyramid decomposition on the slice image data after the gray scale optimization; then, carrying out anisotropic diffusion treatment on each sublayer image to obtain a plurality of sublayer images subjected to anisotropic diffusion treatment; and finally, carrying out image reconstruction based on a pyramid on the plurality of sub-layer images subjected to anisotropic diffusion processing to obtain slice images subjected to enhancement processing.
And step 130, performing parameter optimization on the three-dimensional image data to obtain an optimized three-dimensional image.
The method comprises the steps of adaptively adjusting optimized parameters such as contrast, brightness, smoothness, threshold value and transparency aiming at three-dimensional image data, and adjusting the position of a light shadow under rendering modes such as gradient light, so as to obtain an image with better detail resolution and contrast, clear edge, high noise-power ratio, moderate gain, good consistency and strong stereoscopic impression.
In the four-dimensional ultrasonic imaging optimization method provided by the embodiment, slice image data and three-dimensional image data of a target object are obtained at first; performing gray scale optimization on each frame of slice image data, and performing enhancement processing on the slice image data after the gray scale optimization to obtain a processed slice image; and performing parameter optimization on the three-dimensional image data to obtain an optimized three-dimensional image. By the technical means, the four-dimensional ultrasonic image is quickly optimized, and the four-dimensional ultrasonic image with better performance and better three-dimensional effect is obtained.
Based on the above embodiment, further optimization is performed, and fig. 2 is a schematic flow chart of a method for performing gray scale optimization on each frame of slice image according to an embodiment of the present invention, specifically referring to fig. 2, the gray scale optimization method includes:
step 210, obtaining demodulated amplitude data from the ultrasonic echo data of the target object.
And step 220, calculating a standard characteristic value of the demodulated amplitude data.
A standard pixel value (such as 70) is preset by a system, a standard characteristic value stationvalue of demodulated amplitude data is reversely calculated according to a set formula, and the reverse calculation can only consider calculation factors influencing a gain compensation curve, such as LOG change, system dynamic range conversion and the like.
Specifically, the set formula is as follows:
Figure GDA0002412562030000081
Figure GDA0002412562030000082
wherein, standValue represents a standard characteristic value, standPixel represents a standard pixel value, DRMax represents a maximum dynamic range value that the ultrasound system can support, DrtDB represents a user dynamic range value, and round () represents a function of a rounding function.
And step 230, calculating a representative value of the standard characteristic values along the depth direction.
In the present embodiment, calculating the representative value of the standard feature values in the depth direction includes the steps of:
(1) and calculating the effective point of the demodulated amplitude data in the depth direction.
Specifically, the ultrasonic echo data of the target object is composed of a plurality of pixel points, different pixel points represent different parts of the target object, each target object is three-dimensional, correspondingly, all pixel points forming the target object are distributed in a three-dimensional shape, each target object has a certain thickness called depth, and each depth direction is formed by a plurality of depth points, meanwhile, the horizontal direction of each depth point also has a plurality of pixel points, the effective points of the demodulated amplitude data in the depth direction can be calculated by counting the variance of the amplitude values of each point in the horizontal direction of each depth point, the point of which the variance is smaller than the set proportion threshold value of the maximum variance is taken as the effective point, for example, if the maximum variance is 50 and the set ratio threshold is (0.8), a point in the horizontal direction where the variance is less than 50 × 0.8 — 40 is taken as the valid point. Or, the mean value of the amplitude values of the horizontal points of each depth point may be calculated, and the points whose amplitude values are within a certain proportional range (e.g., 0.8-1.2) of the mean value are taken as the valid points, for example, the mean value of the amplitudes of the horizontal points of a certain depth point is 10, and all the points whose amplitude values are within 8-12 are taken as the valid points.
(2) And calculating the mean value of the effective points, and taking the mean value as a representative value of the standard characteristic value in the depth direction.
And 240, calculating a difference value between the standard characteristic value and the representative value to obtain a gain compensation curve.
And calculating a difference value between the standard characteristic value and the representative value, and normalizing the difference value to a front-end TGC set effective dynamic range (DB) domain of the ultrasonic imaging system to obtain a gain compensation curve changing along with the depth.
And 250, applying the gain compensation curve to the slice image data to obtain the slice image data with optimized gray scale.
And applying the gain compensation curve obtained in the step to a front end TGC module to obtain the slice image data after gray scale optimization. The method for performing gray scale optimization on the slice image provided by the embodiment can obtain the slice image with better detail resolution.
Based on the foregoing embodiment, optimization is continued, and fig. 3 is a schematic flow chart of a method for performing enhancement processing on slice image data after gray scale optimization according to an embodiment of the present invention, and specifically, as shown in fig. 3, the method includes:
and 310, carrying out pyramid decomposition on the slice image data after the gray scale optimization to obtain image sublayers with a plurality of frequency components.
The gray-scale optimized slice image data ImgCom is subjected to pyramid decomposition, for example, gaussian pyramid decomposition and laplacian pyramid decomposition are sequentially performed, and the image data ImgCom is divided into a plurality of (for example, 4) image sub-layers with different frequency components.
And 320, performing anisotropic diffusion processing on each sublayer image respectively to obtain a plurality of sublayer images subjected to anisotropic diffusion processing.
And respectively carrying out anisotropic diffusion treatment on the sublayer images to obtain a plurality of sublayer images subjected to anisotropic diffusion treatment.
And 330, carrying out image reconstruction based on a pyramid on the plurality of sub-layer images subjected to the anisotropic diffusion processing to obtain processed slice images.
And carrying out pyramid reconstruction on each diffusion result to obtain a slice image with high signal-to-noise ratio and enhanced edge. In this embodiment, the pyramid-based image reconstruction is the inverse of the pyramid decomposition.
Exemplarily, the step of subjecting the gray-scale optimized slice image data to pyramid decomposition as shown in fig. 4 and 5 includes:
step 402, low pass filtering the input image data.
And carrying out low-pass filtering processing on the input image data Img to eliminate noise in the image and prevent aliasing.
And step 404, down-sampling the filtered input image data according to the horizontal and vertical directions to obtain a down-sampled image, wherein the input image data of each layer is the down-sampled data of the previous layer.
And performing down-sampling on the filtered input image data according to the horizontal and vertical directions at a decimation rate of N (N can be 2,3,4 and the like) to obtain a down-sampled image, wherein the image represents the low-frequency components of the input original image. For a two-dimensional image, each slice consists of pixels at one-N x N times the resolution of the previous slice.
And 406, performing up-sampling on the down-sampled image according to the horizontal and vertical directions to obtain an up-sampled image.
The up-sampling is performed by interpolating the down-sampled image in the horizontal and vertical directions to N (the value of N corresponds to the down-sampling), and an up-sampled image is obtained, which has a size equal to that of the input image data.
In step 408, the up-sampled image is low-pass filtered.
And performing low-pass filter smoothing on the up-sampled image to resist mirror image.
Step 410, subtracting the filtered up-sampled image from the input image data to obtain a sub-layer image with a predetermined frequency component.
And subtracting the filtered up-sampling image from the input image data Img to obtain a sub-layer image with a preset frequency component.
According to steps 402 to 410, a series of images with different frequency bands, such as HP1, HP2, HP3 and LPD3 of fig. 5, can be obtained by performing pyramid decomposition on the input image data for a plurality of times, and the total frequency range of all the images covers the frequency range of the input image data.
Illustratively, as shown in fig. 6, performing anisotropic diffusion processing on each of the sub-layer images to obtain a plurality of anisotropic diffusion processed sub-layer images includes:
step 602, low-pass filtering the sub-layer image.
The sub-layer images are low-pass filtered to remove speckle noise for each sub-layer image.
And step 604, calculating the horizontal gradient and the vertical gradient of the filtered sublayer image.
Respectively calculating the horizontal and vertical gradients I of the filtered sub-layer imagexAnd Iy. In this embodiment, the gradient calculation may be obtained by using a central difference, a forward difference, a backward difference, or a sobel operator, and may also be calculated by using other algorithms, which are not described herein again.
Step 606, a tissue tensor is constructed according to the transverse gradient and the longitudinal gradient, and eigenvalues of the tissue tensor are calculated.
Constructing a tissue tensor from the longitudinal and transverse gradients, the tissue tensor being defined as
Figure GDA0002412562030000111
Wherein Ixx，IxyRespectively, a transverse gradient IxGradient in the x, y direction of (I)yyIs a longitudinal gradient IyGradient in y direction.
Eigenvectors V using the tissue tensor1，V2And corresponding characteristic value mu1，μ2Local structural features of the sub-layer image are characterized. From linear algebra, the eigenvalues of the tissue tensor are:
Figure GDA0002412562030000121
wherein the content of the first and second substances,
Figure GDA0002412562030000122
and 608, calculating to obtain an expansion tensor according to the tissue tensor and the characteristic value.
Further, an expansion tensor is calculated from the tissue eigenvalues
Figure GDA0002412562030000123
Assuming eigenvalues λ of the expansion tensor1，λ2Comprises the following steps:
λ1＝alpha
λ2＝alpha+(1-alpha)*exp(-c./di.^(2*m))。
the expansion tensor is further computed:
Figure GDA0002412562030000124
wherein, alpha, c, m are preset constants, the expansion tensor is:
Figure GDA0002412562030000125
and 610, performing discrete anisotropic diffusion on the sub-layer image according to the expansion tensor to obtain the sub-layer image subjected to anisotropic diffusion processing.
And performing discrete anisotropic diffusion on the filtered sublayer image according to the expansion tensor obtained by calculation. Discretizing the diffusion coefficient can be 8 directions, for example:
Figure GDA0002412562030000131
the diffusion algorithm may employ a thomas fast algorithm.
Exemplarily, the pyramid-based image reconstruction of the plurality of anisotropic diffusion processed sub-layer images is an inverse process of pyramid decomposition.
For a specific image reconstruction process, see fig. 7 for a schematic pyramid reconstruction diagram, where the images LPD3Proc, HP2Proc, and HP1Proc in fig. 7 respectively represent images obtained by performing anisotropic diffusion processing on the image sublayers, see fig. 7 for a reference.
Firstly, the image LPD3Proc is subjected to upsampling and low-pass filtering processing to obtain an image ImgCmp4, the image is subjected to weighted fusion after the upsampling and low-pass filtering processing is carried out on the image HP3Proc by multiplying a corresponding weight w4, the obtained image is subjected to weighted fusion with the image after the upsampling and low-pass filtering processing is carried out on the image HP2Proc, and finally the image is subjected to weighted fusion with the image HP1Proc, wherein the weight values w4, w3, w2 and w1 can be set according to the reality.
By the method for enhancing the slice image data after the gray scale optimization, the slice image data with the signal-to-noise ratio and the edge definition meeting the preset conditions can be obtained.
In an embodiment, there is further provided a method for performing parameter optimization on three-dimensional image data, as shown in fig. 8, the method specifically includes:
and 810, performing histogram distribution statistics on the three-dimensional image data, and performing adaptive equalization on the histogram to obtain equalized three-dimensional image data.
Specifically, the equalized three-dimensional image data refers to three-dimensional image data with uniform gray scale distribution and contrast meeting a preset value.
And step 820, counting the mean value, the law maximum threshold value and the boundary gray value of the voxel gray value of the balanced three-dimensional image data.
Specifically, the boundary grayscale value may be a grayscale value in a minimum 5% range or a maximum 5% range, for example, the grayscale values are 1, 2 … … 98, 99, 100, 5% by 100 are equal to 5, the grayscale value in the minimum 5% range is 5, the grayscale value in the maximum 5% range is 95, that is, the number of all grayscale values is 5, and the number of all grayscale values is 5.
And 830, determining an optimization parameter value according to the mean value, the law maximum threshold value and the mapping relation between the boundary gray value and the optimization parameter.
The mean, large law threshold, and mapping between the boundary grayscale values and the optimization parameters include linear relationships, the boundary grayscale values include grayscale values at the minimum 5% range boundary and grayscale values at the maximum 5% range boundary, and the optimization parameters include, but are not limited to, brightness, threshold, smoothness, contrast, and transparency.
Determining an optimized parameter value according to the mean value, the large law threshold value and the mapping relation between the boundary gray value and the optimized parameter, wherein the following conditions are mainly adopted:
(1) the average value and the brightness are in a linear relation.
For example, y is 0.625 × x (the whole y is a luminance value, and x is a mean value) between the preset mean value and the luminance, and the luminance value corresponding to the mean value of the voxel gray-scale values of the three-dimensional volume data is determined by the above linear relationship.
(2) When the large law threshold value is smaller than or equal to the preset threshold value, the large law threshold value and the smoothness are in a linear relation; when the large law threshold is greater than the predetermined threshold, the smoothness is set to a predetermined value.
For example, the linear relationship between the large-law threshold and the smoothness is that y is 0.3 × x (the smoothness is obtained after y is rounded, and x is the large-law threshold), and when the large-law threshold of the voxel gray value of the volume data obtained by statistics is smaller than or equal to a predetermined threshold, a smoothness value is obtained according to the linear relationship; when the greater law threshold is greater than the predetermined threshold, the smoothness is set to a predetermined value (default).
(3) When the large law threshold value is smaller than or equal to the preset threshold value, the large law threshold value and the threshold value are in a linear relation; when the law greater than the predetermined threshold, the threshold is set to a predetermined value.
For example, the linear relationship between the large-law threshold and the threshold is that y is 0.375 × x (y is a threshold step after being rounded, and x is the large-law threshold), and when the large-law threshold of the voxel gray value of the volume data obtained by statistics is smaller than or equal to a predetermined threshold, the threshold is obtained according to the linear relationship; when the greater law threshold is greater than the predetermined threshold, the threshold is set to a predetermined value (default value).
(4) And converting the mean value and the boundary gray value into a power-rate change coefficient of the contrast by using a contrast conversion function.
The contrast transformation function is as follows:
Figure GDA0002412562030000151
where r denotes the input voxel value, s denotes the corresponding output voxel value, i.e. the power-rate change coefficient, E may control the slope of the function, and m is the set threshold value for the transformation. Assuming that the statistically obtained volume data mean value is a, the gray value of the minimum 5% range boundary is b, and the gray value of the maximum 5% range boundary is c, normalization is performed by b to c, m in the contrast transformation function is (a-b)/(c-b), the E value range is 2-20, and the function is substituted to obtain the power-rate transformation coefficient s of the contrast.
(5) And determining a starting point of the transparency curve according to the boundary gray value, and determining an inflection point of the brightness curve according to the large-law threshold value.
For example, the gray scale value of the minimum 5% range boundary may be set as the start point of the transparency curve, the gray scale value of the maximum 5% range boundary may be set as the end point of the transparency curve, and the large law threshold may be set as the inflection point of the transparency curve.
And 840, optimizing the three-dimensional image data by using the optimized parameter values and the rotation light angle to obtain an optimized three-dimensional image.
For example, in a rendering mode such as gradient light or high-resolution imaging, the light is adjusted to about 40 degrees on the left and right front sides, so that the current three-dimensional image has a better stereoscopic effect.
By the method for optimizing the parameters of the three-dimensional image data, parameters such as contrast, brightness, smoothness and threshold of the three-dimensional image can be adjusted, and the three-dimensional image with better three-dimensional effect is obtained.
Fig. 9 is a block diagram of a four-dimensional ultrasound image optimization system according to an embodiment of the present invention, and specifically, as shown in fig. 9, the system specifically includes the following components: an obtaining module 910, a gray scale optimizing module 920, an enhancement processing module 930 and a parameter optimizing module 940, wherein the obtaining module 910 is configured to obtain slice image data and three-dimensional image data of a target object; a gray scale optimization module 920, configured to perform gray scale optimization on each frame of the slice image data; an enhancement processing module 930, configured to perform enhancement processing on the slice image data after gray scale optimization to obtain a processed slice image; and a parameter optimization module 940 for performing parameter optimization on the three-dimensional image data to obtain an optimized three-dimensional image.
Further, the gray level optimizing module 920 includes:
the acquisition unit is used for acquiring demodulated amplitude data from the ultrasonic echo data of the target object;
the standard characteristic value calculating unit is used for calculating a standard characteristic value of the demodulated amplitude data;
a representative value calculation unit for calculating a representative value of the standard feature values in a depth direction;
a gain compensation curve obtaining unit for calculating a difference between the standard characteristic value and the representative value to obtain a gain compensation curve;
and the gray scale optimization unit is used for applying the gain compensation curve to the slice image data to obtain the slice image data after gray scale optimization.
Further, the enhancement processing module 930 includes:
the decomposition unit is used for carrying out pyramid decomposition on the slice image data after the gray scale optimization to obtain image sublayers of a plurality of frequency components;
the anisotropic diffusion processing unit is used for respectively carrying out anisotropic diffusion processing on each sublayer image to obtain a plurality of sublayer images after the anisotropic diffusion processing;
and the image reconstruction unit is used for carrying out pyramid-based image reconstruction on the plurality of anisotropic diffusion processed sub-layer images to obtain processed slice images, wherein the pyramid-based image reconstruction is the inverse process of the pyramid decomposition.
Further, the decomposition unit includes:
a first filtering subunit, configured to perform low-pass filtering on input image data;
the down-sampling subunit is used for down-sampling the filtered input image data according to the horizontal and vertical directions to obtain a down-sampled image, wherein the input image data of each layer is the down-sampled data of the previous layer;
the up-sampling sub-unit is used for up-sampling the down-sampled image according to the horizontal and vertical directions to obtain an up-sampled image;
the first filtering subunit is further configured to perform low-pass filtering on the up-sampled image;
and the sub-layer image acquisition sub-unit is used for subtracting the filtered up-sampling image from the input image data to obtain a sub-layer image with a preset frequency component.
Further, the anisotropic diffusion processing unit includes:
the second filtering subunit is used for performing low-pass filtering on the sub-layer image;
the gradiometer unit is used for calculating the transverse gradient and the longitudinal gradient of the filtered sublayer image;
the tissue tensor constructing subunit is used for constructing a tissue tensor according to the transverse gradient and the longitudinal gradient and calculating an eigenvalue of the tissue tensor;
the expansion tensor calculation operator unit is used for calculating and obtaining an expansion tensor according to the tissue tensor and the characteristic value;
and the discrete anisotropic diffusion subunit is used for performing discrete anisotropic diffusion on the sublayer image according to the expansion tensor to obtain the sublayer image after the anisotropic diffusion processing.
Further, the parameter optimization module 940:
the execution unit is used for carrying out histogram distribution statistics on the three-dimensional image data and executing self-adaptive equalization on the histogram to obtain equalized three-dimensional image data;
the statistical unit is used for counting the mean value, the large-law threshold value and the boundary gray value of the voxel gray value of the balanced three-dimensional image data;
the optimization parameter value determining unit is used for determining an optimization parameter value according to the mean value, the law-wide threshold value and the mapping relation between the boundary gray value and the optimization parameter;
and the optimization unit is used for optimizing the three-dimensional image data by using the optimization parameter value and the rotation light angle so as to obtain an optimized three-dimensional image.
In the four-dimensional ultrasonic imaging optimization system provided by the embodiment, slice image data and three-dimensional image data of a target object are acquired at first; then, carrying out gray scale optimization on each frame of slice image data, and carrying out enhancement processing on the slice image data after the gray scale optimization to obtain a processed slice image; performing parameter optimization on the three-dimensional image data to obtain an optimized three-dimensional image; by the technical means, the four-dimensional ultrasonic image is quickly optimized, and the four-dimensional ultrasonic image with better performance and better three-dimensional effect is obtained.
Those skilled in the art can understand that all or part of the steps in the method of the foregoing embodiments may be implemented by a program to instruct related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, etc.) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (8)

1. A four-dimensional ultrasound image optimization method, the four-dimensional ultrasound image including a slice image and a three-dimensional image of a target object, the method comprising the steps of:
acquiring slice image data and three-dimensional image data of a target object;
performing gray scale optimization on each frame of slice image data, and performing enhancement processing on the slice image data after the gray scale optimization to obtain a processed slice image;
performing parameter optimization on the three-dimensional image data to obtain an optimized three-dimensional image;
the step of performing gray scale optimization on each frame of the slice image comprises:
obtaining demodulated amplitude data from ultrasonic echo data of a target object;
calculating a standard characteristic value of the demodulated amplitude data;
calculating a representative value of the standard feature values in a depth direction;
calculating the difference between the standard characteristic value and the representative value to obtain a gain compensation curve;
and applying the gain compensation curve to the slice image data to obtain the slice image data with optimized gray scale.
2. The method of claim 1, wherein the step of performing enhancement processing on the gray scale optimized slice image data to obtain a processed slice image comprises:
carrying out pyramid decomposition on the slice image data after gray scale optimization to obtain image sublayers of a plurality of frequency components;
respectively carrying out anisotropic diffusion processing on each sublayer image to obtain a plurality of sublayer images subjected to anisotropic diffusion processing;
and performing pyramid-based image reconstruction on the plurality of anisotropic diffusion processed sub-layer images to obtain processed slice images, wherein the pyramid-based image reconstruction is an inverse process of the pyramid decomposition.
3. The method of claim 2, wherein the step of pyramidal decomposition comprises:
low-pass filtering the input image data;
down-sampling the filtered input image data according to the horizontal and vertical directions to obtain down-sampled images, wherein the input image data of each layer is the down-sampled data of the previous layer;
up-sampling the down-sampled image according to the horizontal and vertical directions to obtain an up-sampled image;
low-pass filtering the up-sampled image;
and subtracting the filtered up-sampling image from the input image data to obtain a sub-layer image with a preset frequency component.
4. The method of claim 2, wherein the step of performing anisotropic diffusion on each of the sub-layer images to obtain a plurality of anisotropic diffusion processed sub-layer images comprises:
low-pass filtering the sub-layer image;
calculating the horizontal gradient and the vertical gradient of the filtered sub-layer image;
constructing a tissue tensor according to the transverse gradient and the longitudinal gradient, and calculating an eigenvalue of the tissue tensor;
calculating an expansion tensor according to the tissue tensor and the eigenvalue;
and performing discrete anisotropic diffusion on the sublayer image according to the expansion tensor to obtain the sublayer image after the anisotropic diffusion treatment.
5. The method of claim 1, wherein the step of performing parameter optimization on the three-dimensional image data comprises:
performing histogram distribution statistics on the three-dimensional image data, and performing adaptive equalization on the histogram to obtain equalized three-dimensional image data;
counting the mean value, the large-law threshold value and the boundary gray value of the voxel gray value of the balanced three-dimensional image data;
determining an optimization parameter value according to the mean value, the large law threshold value and the mapping relation between the boundary gray value and the optimization parameter;
and optimizing the three-dimensional image data by using the optimized parameter values and the rotating light angle to obtain an optimized three-dimensional image.
6. A four-dimensional ultrasound image optimization system, the four-dimensional ultrasound image including a slice image and a three-dimensional image of a target object, the system comprising:
the acquisition module is used for acquiring slice image data and three-dimensional image data of a target object;
the gray scale optimization module is used for performing gray scale optimization on each frame of slice image data;
the enhancement processing module is used for carrying out enhancement processing on the slice image data after the gray scale optimization to obtain a processed slice image;
the parameter optimization module is used for performing parameter optimization on the three-dimensional image data to obtain an optimized three-dimensional image;
the gray scale optimization module comprises:
the acquisition unit is used for acquiring demodulated amplitude data from the ultrasonic echo data of the target object;
the standard characteristic value calculating unit is used for calculating a standard characteristic value of the demodulated amplitude data;
a representative value calculation unit for calculating a representative value of the standard feature values in a depth direction;
a gain compensation curve obtaining unit for calculating a difference between the standard characteristic value and the representative value to obtain a gain compensation curve;
and the gray scale optimization unit is used for applying the gain compensation curve to the slice image data to obtain the slice image data after gray scale optimization.
7. The system of claim 6, wherein the enhancement processing module comprises:
the decomposition unit is used for carrying out pyramid decomposition on the slice image data after the gray scale optimization to obtain image sublayers of a plurality of frequency components;
the anisotropic diffusion processing unit is used for respectively carrying out anisotropic diffusion processing on each sublayer image to obtain a plurality of sublayer images after the anisotropic diffusion processing;
and the image reconstruction unit is used for carrying out pyramid-based image reconstruction on the plurality of anisotropic diffusion processed sub-layer images to obtain processed slice images, wherein the pyramid-based image reconstruction is the inverse process of the pyramid decomposition.
8. The system of claim 7, wherein the decomposition unit comprises:
a first filtering subunit, configured to perform low-pass filtering on input image data;
the down-sampling subunit is used for down-sampling the filtered input image data according to the horizontal and vertical directions to obtain a down-sampled image, wherein the input image data of each layer is the down-sampled data of the previous layer;
the up-sampling sub-unit is used for up-sampling the down-sampled image according to the horizontal and vertical directions to obtain an up-sampled image;
the first filtering subunit is further configured to perform low-pass filtering on the up-sampled image;
and the sub-layer image acquisition sub-unit is used for subtracting the filtered up-sampling image from the input image data to obtain a sub-layer image with a preset frequency component.
CN201611205838.XA 2016-12-23 2016-12-23 Four-dimensional ultrasonic image optimization method and system Active CN106725612B (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN201611205838.XA CN106725612B (en) 2016-12-23 2016-12-23 Four-dimensional ultrasonic image optimization method and system
PCT/CN2017/093471 WO2018113283A1 (en) 2016-12-23 2017-07-19 Method and system for optimizing four-dimensional ultrasound image

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201611205838.XA CN106725612B (en) 2016-12-23 2016-12-23 Four-dimensional ultrasonic image optimization method and system

Publications (2)

Publication Number Publication Date
CN106725612A CN106725612A (en) 2017-05-31
CN106725612B true CN106725612B (en) 2020-10-23

Family

ID=58919861

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201611205838.XA Active CN106725612B (en) 2016-12-23 2016-12-23 Four-dimensional ultrasonic image optimization method and system

Country Status (2)

Country Link
CN (1) CN106725612B (en)
WO (1) WO2018113283A1 (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106725612B (en) * 2016-12-23 2020-10-23 深圳开立生物医疗科技股份有限公司 Four-dimensional ultrasonic image optimization method and system
CN109754817A (en) * 2017-11-02 2019-05-14 北京三星通信技术研究有限公司 signal processing method and terminal device
CN111358492A (en) * 2020-02-28 2020-07-03 深圳开立生物医疗科技股份有限公司 Four-dimensional contrast image generation method, device, equipment and storage medium
CN112826483B (en) * 2021-01-08 2022-03-08 中国科学院自动化研究所 Fingertip video-based heart rate detection method, system and device

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN86107496A (en) * 1985-10-31 1987-06-10 通用电气公司 The adaptive time gain compensation system that is used for ultrasonic imaging
CN1317293A (en) * 2000-03-22 2001-10-17 Ge医疗系统环球技术有限公司 Image forming method and device thereof and supersonic imaging device
CN101452574A (en) * 2008-12-19 2009-06-10 深圳市恩普电子技术有限公司 Method for enhancing ultrasonograph quality
CN104490418A (en) * 2014-09-25 2015-04-08 深圳市恩普电子技术有限公司 Automatic ultrasonic-image optimization method based on signal statistic analysis
CN104680495A (en) * 2015-03-17 2015-06-03 武汉倍尔生生物科技有限公司 Adaptive denoising method for ultrasonic image

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100646715B1 (en) * 2004-10-18 2006-11-23 한국과학기술원 Method for improving a quality of a 2-d ultrasound image by post-processing
US20080081998A1 (en) * 2006-10-03 2008-04-03 General Electric Company System and method for three-dimensional and four-dimensional contrast imaging
CN101190135B (en) * 2006-11-29 2012-05-02 深圳迈瑞生物医疗电子股份有限公司 Method for optimizing ultrasonic image gray level in ultrasonic imaging system
CN102499711B (en) * 2011-09-28 2013-07-10 无锡祥生医学影像有限责任公司 Three-dimensional or four-dimensional automatic ultrasound image optimization and adjustment method
CN103222876B (en) * 2012-01-30 2016-11-23 东芝医疗系统株式会社 Medical image-processing apparatus, image diagnosing system, computer system and medical image processing method
CN106725612B (en) * 2016-12-23 2020-10-23 深圳开立生物医疗科技股份有限公司 Four-dimensional ultrasonic image optimization method and system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN86107496A (en) * 1985-10-31 1987-06-10 通用电气公司 The adaptive time gain compensation system that is used for ultrasonic imaging
CN1317293A (en) * 2000-03-22 2001-10-17 Ge医疗系统环球技术有限公司 Image forming method and device thereof and supersonic imaging device
CN101452574A (en) * 2008-12-19 2009-06-10 深圳市恩普电子技术有限公司 Method for enhancing ultrasonograph quality
CN104490418A (en) * 2014-09-25 2015-04-08 深圳市恩普电子技术有限公司 Automatic ultrasonic-image optimization method based on signal statistic analysis
CN104680495A (en) * 2015-03-17 2015-06-03 武汉倍尔生生物科技有限公司 Adaptive denoising method for ultrasonic image

Also Published As

Publication number Publication date
CN106725612A (en) 2017-05-31
WO2018113283A1 (en) 2018-06-28

Similar Documents

Publication Publication Date Title
Yu et al. Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method
Zhang et al. Nonlinear diffusion in Laplacian pyramid domain for ultrasonic speckle reduction
US8861821B2 (en) Ultrasound diagnosis apparatus
US8435180B2 (en) Gain optimization of volume images for medical diagnostic ultrasonic imaging
KR100830263B1 (en) Method, computer program product and apparatus for enhancing a computerized tomography image
EP2380132B1 (en) Denoising medical images
US9113826B2 (en) Ultrasonic diagnosis apparatus, image processing apparatus, control method for ultrasonic diagnosis apparatus, and image processing method
Khare et al. Despeckling of medical ultrasound images using Daubechies complex wavelet transform
CN106725612B (en) Four-dimensional ultrasonic image optimization method and system
JP5851301B2 (en) Image processing apparatus, image processing method, and magnetic resonance imaging apparatus
CN109961411B (en) Non-downsampling shear wave transformation medical CT image denoising method
Bhuiyan et al. Spatially adaptive thresholding in wavelet domain for despeckling of ultrasound images
US20110262023A1 (en) Method of filtering an image dataset
US8139891B2 (en) System and method for structure enhancement and noise reduction in medical images
US20120108973A1 (en) Ultrasonic diagnostic apparatus and ultrasonic image processing apparatus
KR20130007905A (en) Method and apparatus for processing a ultrasound image
CN109598680B (en) Shear wave transformation medical CT image denoising method based on rapid non-local mean value and TV-L1 model
US11408987B2 (en) Ultrasonic imaging with multi-scale processing for grating lobe suppression
WO2015080006A1 (en) Ultrasonic diagnostic device
Bamber et al. Texture analysis and speckle reduction in medical echography
CN108846813A (en) The medicine CT image denoising method of frame and NSST is decomposed based on MFDF
Loupas et al. Deconvolution in medical ultrasonics: practical considerations
JP4679095B2 (en) Image processing apparatus, image processing method, and program
Duan et al. Assessment of visual quality and spatial accuracy of fast anisotropic diffusion and scan conversion algorithms for real-time three-dimensional spherical ultrasound
CN108416737A (en) Medicine CT image denoising method based on DNST

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant