CN106725612A - Four-dimensional ultrasound image optimization method and system - Google Patents
Four-dimensional ultrasound image optimization method and system Download PDFInfo
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Abstract
The embodiment of the invention discloses a kind of four-dimensional ultrasound image optimization method and system, the four-dimensional ultrasound image includes the sectioning image and 3-D view of destination object, and methods described includes：Obtain the slice image data and 3 d image data of destination object；GTG optimization is carried out to slice image data described in every frame, and enhancing treatment is carried out to the slice image data after GTG optimization, the sectioning image after being processed；Parameter optimization is carried out to the 3 d image data, the 3-D view after optimization is obtained.A kind of four-dimensional ultrasound image optimization method and system, realize and four-dimensional ultrasound image are optimized disclosed in the embodiment of the present invention, have obtained that image property is more excellent, the more preferable four-dimensional ultrasound image of stereoeffect.
Description
Technical field
The present embodiments relate to ultrasonoscopy technical field, more particularly to a kind of four-dimensional ultrasound image optimization method and it is
System.
Background technology
Due to ultrasonic wave, (such as heart, head etc.) propagates the decay for producing, reflection and pseudomorphism feelings in different tissues
Condition is different, therefore, different tissues is imaged according to same set of default ultrasonic four-dimensional imaging parameter, it is impossible to meet not
With the imaging demand of tissue.The feature difference of the ultrasonoscopy of the fetus at such as different pregnant weeks and different diagnosis positions is brighter
It is aobvious, the demand of imaging can not be met according to same set of default ultrasonic four-dimensional imaging parameter.However, only rely on doctor adjusting manually
All kinds of imaging parameters are saved, not only real-time is low, cumbersome, and the preferable imaging effect that surely reach that differs, and then it is larger
Have impact on recall rate to abnormal focus.
The content of the invention
A kind of four-dimensional ultrasound image optimization method and system are the embodiment of the invention provides, is realized to four-dimensional ultrasound image
Optimize, obtained that image property is more excellent, the more preferable four-dimensional ultrasound image of stereoeffect.
In a first aspect, a kind of four-dimensional ultrasound image optimization method is the embodiment of the invention provides, the four-dimensional ultrasound image
Sectioning image and 3-D view including destination object, the method include：
Obtain the slice image data and 3 d image data of destination object；
GTG optimization is carried out to slice image data described in every frame, and the slice image data after GTG optimization is increased
Strength is managed, the sectioning image after being processed；
Parameter optimization is carried out to 3 d image data, the 3-D view after optimization is obtained.
Further, it is described the step of GTG optimizes is carried out to sectioning image described in every frame to include：
Demodulation back panel degrees of data is obtained from the ultrasonic echo data of destination object；
Calculate the Standard Eigenvalue of demodulation back panel degrees of data；
The typical value of Standard Eigenvalue is calculated along depth direction；
The difference between Standard Eigenvalue and typical value is calculated, gain compensating curve is obtained；
Gain compensating curve is acted on into slice image data, the slice image data after GTG optimization is obtained.
Further, the slice image data after the optimization to the GTG carries out enhancing treatment, after being processed
The step of sectioning image, includes：
Slice image data after GTG is optimized carries out pyramid decomposition, obtains the image sublayer of multiple radio-frequency components；
Anisotropy parameter treatment is carried out respectively to each sublayer image, the son after multiple anisotropy parameter treatment is obtained
Tomographic image；
Sublayer image after the treatment of the multiple anisotropy parameter is carried out, based on pyramidal Image Reconstruction, to obtain everywhere
Sectioning image after reason, wherein, described is the inverse process of the pyramid decomposition based on pyramidal Image Reconstruction.
Further, the step of pyramid decomposition includes：
LPF is carried out to input image data；
Filtered input image data is carried out according to transverse and longitudinal direction down-sampled, down-sampled image is obtained, wherein every layer
Input image data for last layer it is down-sampled after data；
Down-sampled image is up-sampled according to transverse and longitudinal direction, obtains up-sampling image；
LPF is carried out to up-sampling image；
Input image data is subtracted into filtered up-sampling image, the sublayer image of preset frequency composition is obtained.
Further, anisotropy parameter treatment is carried out to sublayer image each described, obtains multiple anisotropy parameters
The step of sublayer image after treatment, includes：
LPF is carried out to sub- tomographic image；
Calculate the horizontal gradient and vertical gradient of filtered sublayer image；
According to horizontal gradient and vertical gradient texture tensor, and computation organization's tensor characteristic value；
According to tissue tensor sum characteristic value, expansion tensor is calculated；
Discrete anisotropy parameter is carried out to sub- tomographic image according to expansion tensor, the son after anisotropy parameter treatment is obtained
Tomographic image.
Further, it is described to include the step of carry out parameter optimization to 3 d image data：
Histogram distribution statistics is carried out to 3 d image data, and adaptive equalization is performed to histogram, after being equalized
3 d image data；
The average of the voxel gray values of the 3 d image data after statistical equilibrium, big rule threshold value and boundary gray value；
Respectively according to average, big rule threshold value and the mapping relations between boundary gray value and Optimal Parameters, it is determined that optimization
Parameter value；
3 d image data is optimized using Optimal Parameters value and rotating light angle, to obtain the three-dimensional after optimization
Image.Second aspect, the embodiment of the present invention additionally provides a kind of four-dimensional ultrasound image optimization system, the four-dimensional ultrasound image bag
The sectioning image and 3-D view of destination object are included, the system includes：
Acquisition module, slice image data and 3 d image data for obtaining destination object；
GTG optimization module, for carrying out GTG optimization to every frame slice image data；
Enhancing processing module, for optimizing to GTG after slice image data carry out enhancing treatment, after being processed
Sectioning image；
Parameter optimization module, for carrying out parameter optimization to 3 d image data, obtains the 3-D view after optimization.
Further, the GTG optimization module includes：
Acquiring unit, for obtaining demodulation back panel degrees of data from the ultrasonic echo data of destination object；
Standard Eigenvalue computing unit, the Standard Eigenvalue for calculating demodulation back panel degrees of data；
Typical value computing unit, the typical value for calculating Standard Eigenvalue along depth direction；
Gain compensating curve acquiring unit, for calculating the difference between Standard Eigenvalue and typical value, obtains gain benefit
Repay curve；
GTG optimizes unit, for gain compensating curve to be acted on into slice image data, obtains cutting after GTG optimization
Picture data.
Further, the enhancing processing module includes：
Resolving cell, for GTG to be optimized after slice image data carry out pyramid decomposition, obtain multiple frequencies into
The image sublayer of part；
Anisotropy parameter processing unit, for carrying out anisotropy parameter treatment respectively to each sublayer image, obtains
Sublayer image after multiple anisotropy parameter treatment；
Image reconstruction unit, for carrying out the sublayer image after the treatment of the multiple anisotropy parameter based on pyramid
Image Reconstruction, the sectioning image after being processed, wherein, described is the pyramid decomposition based on pyramidal Image Reconstruction
Inverse process.
Further, the resolving cell includes：
First filtering subunit, for carrying out LPF to input image data；
Down-sampled subelement, it is down-sampled for being carried out according to transverse and longitudinal direction to filtered input image data, dropped
Sampled images, wherein every layer of input image data be last layer it is down-sampled after data；
Up-sampling subelement, for up-sampling down-sampled image according to transverse and longitudinal direction, obtains up-sampling image；
First filtering subunit, is additionally operable to carry out LPF to up-sampling image；
Sublayer image obtains subelement, for input image data to be subtracted into filtered up-sampling image, is made a reservation for
The sublayer image of radio-frequency component.
Further, the anisotropy parameter processing unit includes：
Second filtering subunit, for carrying out LPF to sub- tomographic image；
Gradient calculation subelement, horizontal gradient and vertical gradient for calculating filtered sublayer image；
Tissue tensor construction subelement, for according to horizontal gradient and vertical gradient texture tensor, and computation organization's tensor
Characteristic value；
Expansion tensor computation subelement, for according to tissue tensor sum characteristic value, being calculated expansion tensor；
Discrete anisotropy parameter subelement, for carrying out discrete anisotropy expansion to sub- tomographic image according to expansion tensor
Dissipate, obtain the sublayer image after anisotropy parameter treatment.
Further, the parameter optimization module includes：
Execution unit, for carrying out histogram distribution statistics to 3 d image data, and it is equal to perform self adaptation to histogram
Weighing apparatus, the 3 d image data after being equalized；
Statistic unit, average for the voxel gray values of the 3 d image data after statistical equilibrium, big rule threshold value and
Boundary gray value；
Optimal Parameters value determining unit, for respectively according to average, big rule threshold value and boundary gray value and Optimal Parameters
Between mapping relations, determine Optimal Parameters value；
Optimization unit, for being optimized to 3 d image data using Optimal Parameters value and rotating light angle, to obtain
Take the 3-D view after optimization.
A kind of four-dimensional ultrasound imaging optimization method provided in an embodiment of the present invention, obtains the sectioning image of destination object first
Data and 3 d image data；Then GTG optimization is carried out to slice image data described in every frame, and cutting after optimizing to GTG
Picture data carry out enhancing treatment, the sectioning image after being processed；Parameter optimization finally is carried out to 3 d image data, is obtained
Take the 3-D view after optimization；Realized by above-mentioned technological means and four-dimensional ultrasound image is optimized, obtained image
Can the more preferable four-dimensional ultrasound image of more excellent, stereoeffect.
Brief description of the drawings
Fig. 1 is a kind of four-dimensional ultrasound image optimization method schematic flow sheet provided in an embodiment of the present invention；
Fig. 2 is that a kind of method flow that GTG optimization is carried out to every frame sectioning image provided in an embodiment of the present invention is illustrated
Figure；
Fig. 3 be it is provided in an embodiment of the present invention it is a kind of to GTG optimize after slice image data carry out strengthen treatment side
Method schematic flow sheet；
Fig. 4 is a kind of schematic flow sheet that slice image data is carried out pyramid decomposition provided in an embodiment of the present invention；
Fig. 5 is another flow signal that slice image data is carried out pyramid decomposition provided in an embodiment of the present invention
Figure；
Fig. 6 is that one kind provided in an embodiment of the present invention carries out anisotropy parameter treatment to each sublayer image, obtains many
The schematic flow sheet of the sublayer image after individual anisotropy parameter treatment；
Fig. 7 is a kind of schematic flow sheet that Pyramid Reconstruction is carried out to sub- tomographic image provided in an embodiment of the present invention；
Fig. 8 is that a kind of method flow that parameter optimization is carried out to 3 d image data provided in an embodiment of the present invention is illustrated
Figure；
Fig. 9 is a kind of four-dimensional ultrasound image optimization system structure diagram provided in an embodiment of the present invention.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining the present invention, rather than limitation of the invention.It also should be noted that, in order to just
Part rather than entire infrastructure related to the present invention is illustrate only in description, accompanying drawing.
It should be mentioned that some exemplary embodiments are described as before exemplary embodiment is discussed in greater detail
The treatment described as flow chart or method.Although every step to be described as flow chart the treatment of order, therein to be permitted
Multi-step can be implemented concurrently, concomitantly or simultaneously.Additionally, the order of every step can be rearranged.When it
The treatment can be terminated when step is completed, it is also possible to have the additional step being not included in accompanying drawing.The treatment
Can correspond to method, function, code, subroutine, subprogram etc..
Embodiment
Fig. 1 is a kind of four-dimensional ultrasound image optimization method flow chart provided in an embodiment of the present invention, and the method is applicable to
Scanning is carried out to different tissue and/or organ, to gather the situation of ultrasonoscopy, can be held by four-dimensional ultrasound picture system
OK, the system can be realized by way of hardware and/or software.The four-dimensional ultrasound image includes the sectioning image of destination object
And 3-D view, the method specifically includes following steps：
Step 110, the slice image data and 3 d image data that obtain destination object.
Specifically, the destination object can be different tissues (such as heart, head etc.), or different pregnant week with
And the fetus at different diagnosis positions.Each width four-dimensional ultrasound image all includes A, B and C (sagittal plane, coronal-plane and cross section) three
The sectioning image of individual plane and a 3-D view.The slice image data and 3 d image data for obtaining destination object can
Obtained with carrying out scanning to destination object by ultrasonic image-forming system.
Step 120, GTG optimization is carried out to every frame slice image data, and slice image data after optimizing to GTG is entered
Row enhancing is processed, the sectioning image after being processed.
GTG optimization is specifically carried out to every frame slice image data can be by calculating the gain of slice image data first
Compensated curve, the gain compensating curve for being then based on being calculated is processed slice image data, you can obtain GTG excellent
Slice image data after change.
It is specifically the sectioning image number after optimizing to GTG that slice image data after optimizing to GTG carries out enhancing treatment
Processed according to the enhancing for carrying out signal to noise ratio and marginal definition, in order to obtain that signal to noise ratio is larger and marginal definition is higher
Slice image data.
To GTG optimize after slice image data carry out enhancing treatment can by optimizing to GTG after sectioning image
Data carry out pyramid decomposition, obtain the image sublayer of multiple radio-frequency components；Then each sublayer image carried out again each to different
Property DIFFUSION TREATMENT, obtain the sublayer image after the treatment of multiple anisotropy parameters；After finally processing multiple anisotropy parameters
Sublayer image carry out based on pyramidal Image Reconstruction, obtain the sectioning image after enhancing treatment.
Step 130, parameter optimization is carried out to 3 d image data, obtain the 3-D view after optimization.
For Optimal Parameters such as 3 d image data self-adaptative adjustment contrast, brightness, smoothness, threshold value, transparencies, and
Adjustment gradient light etc. renders the light shadow positions under mode, thus obtain detail resolution and contrast more preferably, edge clear,
To-noise ratio high, the image that gain is moderate, uniformity is good and third dimension is strong.
A kind of four-dimensional ultrasound imaging optimization method that the present embodiment is provided, obtains the slice image data of destination object first
And 3 d image data；Then GTG optimization is carried out to every frame slice image data, and to the sectioning image number after GTG optimization
According to carrying out enhancing treatment, the sectioning image after being processed；Parameter optimization is carried out to the 3 d image data, after obtaining optimization
3-D view.Realized by above-mentioned technological means carries out rapid Optimum to four-dimensional ultrasound image, and it is more excellent to have obtained performance, stands
The more preferable four-dimensional ultrasound image of body effect.
On the basis of above-described embodiment, further optimized, Fig. 2 is one kind provided in an embodiment of the present invention to every frame
Sectioning image carries out the method flow schematic diagram of GTG optimization, and referring specifically to shown in Fig. 2, the GTG optimization method includes：
Step 210, the acquisition demodulation back panel degrees of data from the ultrasonic echo data of destination object.
Step 220, the Standard Eigenvalue for calculating demodulation back panel degrees of data.
It is reverse to calculate demodulation amplitude according to setting formula by systemic presupposition standard pixel value standPixel (such as 70)
The Standard Eigenvalue standValue of data, reverse calculating can only consider the shadows such as the dynamic range transform of LOG changes and system
Ring the calculating factor of gain compensating curve.
Specifically, it is described set formula as：
Wherein, standValue represents Standard Eigenvalue, and standPixel is standard pixel value, and DRMax is ultrasonic system
The maximum dynamic range value that can be supported, DrtDB is user's dynamic range values, and round () represents the function of round-off-function.
Step 230, the typical value that Standard Eigenvalue is calculated along depth direction.
In the present embodiment, the typical value for calculating Standard Eigenvalue along depth direction is comprised the following steps：
(1) demodulation back panel degrees of data available point in the depth direction is calculated.
Specifically, what the ultrasonic echo data of destination object was made up of many pixels, different pixels are represented
The different parts of the destination object, each destination object is three-dimensional, accordingly, constitutes all pixels of the destination object
Point also spatially shape distribution, and each destination object has certain thickness, referred to as depth, it is seen that each depth direction is by very
Many depth point compositions, while the horizontal direction of each depth point also has many pixels, calculate demodulation back panel degrees of data and exist
Available point on depth direction can take variance small by counting the variance of the range value of the horizontal direction each point of each depth point
It is available point in the point of the setting ratio threshold value of maximum variance, for example, the maximum variance is 50, the setting ratio threshold value is
(0.8) point of horizontal direction of the variance less than 50*0.8=40, is then taken as available point.Or each depth point can also be calculated
Horizontal each point range value average, take range value positioned at the point of a certain proportion of the average (such as 0.8~1.2) to have
Effect point, such as the amplitude equalizing value of the horizontal each point of a certain depth point is 10, and it is effective to take range value and be located in 8-12 a little
Point.
(2) average of above-mentioned available point, the representative using the average as the Standard Eigenvalue on the depth direction are calculated
Value.
Difference between step 240, calculating Standard Eigenvalue and typical value, obtains gain compensating curve.
The difference between Standard Eigenvalue and typical value is calculated, and the difference is normalized to the front end of ultrasonic image-forming system
TGC sets effective dynamic range (DB) domain, obtains with the gain compensating curve of change in depth.
Step 250, gain compensating curve is acted on into slice image data, obtain the sectioning image number after GTG optimization
According to.
The gain compensating curve that above-mentioned steps are obtained is applied to front end TGC modules, the slice map after GTG optimization is obtained
As data.The method that GTG optimization is carried out to sectioning image that the present embodiment is provided, can obtain detail resolution and preferably cut
Picture.
On the basis of above-described embodiment, proceed optimization, Fig. 3 is provided in an embodiment of the present invention a kind of excellent to GTG
Slice image data after change strengthen the method flow schematic diagram for the treatment of, and referring specifically to shown in Fig. 3, methods described includes：
Step 310, GTG is optimized after slice image data carry out pyramid decomposition, obtain the figure of multiple radio-frequency components
As sublayer.
Slice image data ImgCom after optimizing to GTG carries out pyramid decomposition, for example, sequentially passing through Gauss gold word
Tower is decomposed and Laplacian pyramid, if being divided into the image sublayer of dried layer (such as 4 layers) different frequency composition.
Step 320, anisotropy parameter treatment is carried out respectively to each sublayer image, obtained at multiple anisotropy parameters
Sublayer image after reason.
Anisotropy parameter treatment is carried out respectively to each sublayer image, the sublayer after multiple anisotropy parameter treatment is obtained
Image.
Step 330, the sublayer image after the treatment of the multiple anisotropy parameter is carried out based on pyramidal image weight
Structure, the sectioning image after being processed.
Pyramid Reconstruction is carried out to each diffusion result, it is high to obtain signal to noise ratio, the enhanced sectioning image in edge.In the present embodiment
In, it is the inverse process of pyramid decomposition based on pyramidal Image Reconstruction.
Exemplarily, the slice image data after the GTG is optimized as shown in Figures 4 and 5 carries out the step of pyramid decomposition
Suddenly include：
Step 402, LPF is carried out to input image data.
View data Img to being input into carries out low-pass filtering treatment, eliminates the noise in image, anti-aliasing.
Step 404, carries out down-sampled to filtered input image data according to transverse and longitudinal direction, obtains down-sampled image,
Wherein every layer of input image data be last layer it is down-sampled after data.
It is N down-sampled (N desirable 2,3,4 etc.) to carry out extraction yield according to transverse and longitudinal direction to filtered input image data,
Down-sampled image is obtained, this image represents the low-frequency component of input artwork.For two dimensional image, each tomographic image is by last layer point
The pixel composition of N*N/mono- of resolution.
Step 406, up-samples to down-sampled image according to transverse and longitudinal direction, obtains up-sampling image.
The up-sampling (N values are corresponding with down-sampling) that row interpolation is N is entered in down-sampled image transverse and longitudinal direction, obtains up-sampling figure
Picture, its size is big with input image data etc..
Step 408, LPF is carried out to up-sampling image.
Low pass filter is carried out to up-sampling image to smooth, anti-mirror image.
Step 410, filtered up-sampling image is subtracted by input image data, obtains the sublayer figure of preset frequency composition
Picture.
Input image data Img subtracts filtered up-sampling image, obtains the sublayer image of preset frequency composition.
According to step 402 to step 410, multiple pyramid decomposition is carried out to input image data, can obtained a series of
HP1, HP2, HP3, the LPD3 of the image of different frequency bands, such as Fig. 5, the total frequency range covering input image data of all images
Frequency range.
Exemplarily, as shown in fig. 6, carrying out anisotropy parameter treatment to each sublayer image, obtain multiple each to different
Sublayer image after property DIFFUSION TREATMENT, including：
Step 602, LPF is carried out to sub- tomographic image.
Sublayer image carries out low-pass filtering treatment, to remove the speckle noise of each sublayer image.
Step 604, the horizontal gradient and vertical gradient that calculate filtered sublayer image.
Horizontal, the vertical gradient I of filtered sublayer image is calculated respectivelyxAnd Iy.In the present embodiment, gradient calculation can be adopted
With centered difference, front and rear difference or sobel operators are obtained, and can also be calculated using other algorithms, are repeated no more here.
Step 606, according to horizontal gradient and the vertical gradient texture tensor, and computation organization's tensor characteristic value.
By transverse and longitudinal gradient texture tensor, tissue tensor is defined asWherein Ixx, IxyRespectively horizontal gradient
IxX, y direction gradients, IyyIt is vertical gradient IyY direction gradients.
Using the characteristic vector V for organizing tensor1, V2And corresponding characteristic value μ1, μ2The partial structurtes for characterizing sublayer image are special
Levy.According to linear algebra, the characteristic value for organizing tensor is：
Wherein,
Step 608, according to tissue tensor sum characteristic value, be calculated expansion tensor.
Further, expansion tensor is calculated by tissue signature's valueAssuming that the eigenvalue λ of expansion tensor1, λ2
For：
λ1=alpha
λ2=alpha+ (1-alpha) * exp (- c./di.^ (2*m)).
Further calculate expansion tensor：
Di=μ1-μ2
Tmp=sqrt ((Ixx-Iyy).^2+4*Ixy.^2)
V2x=2*Ixy
V2y=Iyy-Ixx+tmp
Mag=sqrt (v2x*v2x+v2y*v2y),
V2x=v2x/mag
V2y=v2y/mag
V1x=-v2y
V1y=v2x
Wherein, alpha, c, m are preset constant, then expand tensor and be：
Dxx=λ1.*v1x.^2+λ2.*v2x.^2
Dxy=λ1.*v1x*v1y+λ2.*v2x*v2y,
Dyy=λ1.*v1y.^2+λ2.*v2y.^2
Step 610, according to expansion tensor discrete anisotropy parameter is carried out to sub- tomographic image, obtain at anisotropy parameter
Sublayer image after reason.
Expansion tensor according to obtained by calculating, discrete anisotropy parameter is carried out to filtered sublayer image.Will diffusion
It can be 8 directions that coefficient is discrete, for example：Broadcast algorithm can be using support
Maas fast algorithm.
Exemplarily, the sublayer image after the treatment of the multiple anisotropy parameter is carried out based on pyramidal image weight
Structure is the inverse process of pyramid decomposition.
Specific Image Reconstruction flow may refer to the Pyramid Reconstruction schematic diagram shown in Fig. 7, wherein, the image in Fig. 7
LPD3Proc, HP3Proc, HP2Proc and HP1Proc are represented respectively to be carried out at anisotropy parameter to above-mentioned each image sublayer
The image obtained after reason, it is shown in Figure 7.
Image LPD3Proc is up-sampled and low-pass filtering treatment first, is obtained image ImgCmp4, it is right by being multiplied by
The weight w 4 answered up-sampled with to image HP3Proc and low-pass filtering treatment after image be weighted and merge, obtain
Image up-sampled with to image HP2Proc again and low-pass filtering treatment after image be weighted and merge, finally and image
HP1Proc is weighted fusion, and wherein weighted value w4, w3, w2 and w1 can be according to actually being set.
The method for carrying out enhancing treatment to the slice image data after GTG optimization provided by the present embodiment, can obtain
Meet pre-conditioned slice image data to signal to noise ratio and marginal definition.
In one embodiment, a kind of method that parameter optimization is carried out to 3 d image data for also providing, such as Fig. 8 institutes
Show, the method is specifically included：
Step 810, histogram distribution statistics is carried out to 3 d image data, and adaptive equalization is performed to histogram, obtained
3 d image data after to equilibrium.
Specifically, the 3 d image data after the equilibrium refers to that gray-scale distribution is uniform, contrast meets the three-dimensional of preset value
View data.
The average of the voxel gray values of the 3 d image data after step 820, statistical equilibrium, big rule threshold value and border ash
Angle value.
Specifically, the boundary gray value can be the gray value of minimum 5% scope or maximum 5% scope, for example, institute
Gray value is stated for 1,2 ... 98,99,100, totally 100 numerical value, 5%*100 is equal to 5, and the gray value of minimum 5% scope is 5,
The gray value of maximum 5% scope is 95, i.e., the number in all gray values less than or equal to the boundary gray value has 5, is more than
There are 5 equal to the number of the boundary gray value.
Step 830, respectively according to average, big rule threshold value and the mapping relations between boundary gray value and Optimal Parameters,
Determine Optimal Parameters value.
Average, big rule threshold value and the mapping relations between boundary gray value and Optimal Parameters include linear relationship, border
Gray value includes the gray value of minimum 5% range boundary and the gray value of maximum 5% range boundary, and Optimal Parameters include but do not limit
In brightness, threshold value, smoothness, contrast and transparency.
Respectively according to average, big rule threshold value and the mapping relations between boundary gray value and Optimal Parameters, it is determined that optimization
Parameter value, mainly there is following several situations：
(1) it is linear relationship between average and brightness.
For example, between the default average and brightness be y=0.625*x (y round after for brightness value, x is average),
The corresponding brightness value of the average of three-dimensional data voxel gray values is determined by above-mentioned linear relationship.
(2) it is linear relationship between big rule threshold value and smoothness when big rule threshold value is less than or equal to predetermined threshold；When
When big rule threshold value is more than predetermined threshold, smoothness is set as predetermined value.
For example, the linear relationship greatly between rule threshold value and smoothness is that (y is smoothness after rounding to y=0.3*x, and x is big rule
Threshold value), when the big rule threshold value of the voxel gray values of the volume data that statistics is obtained is less than or equal to predetermined threshold, by upper
State linear relationship and obtain smoothness value；When big rule threshold value is more than predetermined threshold, smoothness is set to predetermined value (default value).
(3) it is linear relationship between big rule threshold value and threshold value when big rule threshold value is less than or equal to predetermined threshold；When big
When rule threshold value is more than predetermined threshold, threshold value is set as predetermined value.
For example, the linear relationship greatly between rule threshold value and threshold value is that (y is threshold value gear after rounding to y=0.375*x, and x is big
Rule threshold value), when the big rule threshold value of the voxel gray values of the volume data that statistics is obtained is less than or equal to predetermined threshold, press
Above-mentioned linear relationship obtains threshold value；When big rule threshold value is more than predetermined threshold, predetermined value (default value) is set a threshold to.
(4) contrast variation's function is utilized, average and boundary gray value is transformed to the power rate variation coefficient of contrast.
Contrast variation's function is as follows：
Wherein, what r was represented is the voxel value of input, and s is the corresponding voxel value for representing output, i.e., described power rate change system
Number, E can control the slope of the function, threshold value when m is the conversion of setting.It is minimum if the volume data average that statistics is obtained is a
The gray value of 5% range boundary is b, and the gray value of maximum 5% range boundary is c, then be normalized with b to c, and contrast becomes
M=(a-b)/(c-b) in exchange the letters number, E values scope is 2-20, and the power rate transition of contrast is can obtain in the above-mentioned function of substitution
Coefficient s.
(5) starting point of transparency profile is determined according to boundary gray value, turning for lightness curve is determined according to big rule threshold value
Point.
For example, the gray value of minimum 5% range boundary can be set to the starting point of transparency profile, maximum 5% scope
The gray value on border is set to the terminal of transparency profile, and big rule threshold value is set to the flex point of transparency profile.
Step 840,3 d image data is optimized using Optimal Parameters value and rotating light angle, to obtain optimization
3-D view afterwards.
For example, rendered under mode in gradient light or high-resolution imaging etc., adjustment light to about 40 ° of left and right front side makes
When forward three-dimensional viewing stereoeffect is more preferable.
The method that parameter optimization is carried out to 3 d image data provided by the present embodiment, can adjust 3-D view
The parameters such as contrast, brightness, smoothness, threshold value, obtain the more preferable 3-D view of stereoeffect.
Fig. 9 is a kind of structured flowchart of four-dimensional ultrasound image optimization system provided in an embodiment of the present invention, referring specifically to Fig. 9
Shown, the system specifically includes as follows：Acquisition module 910, GTG optimization module 920 strengthens processing module 930 and parameter optimization
Module 940, wherein, acquisition module 910, slice image data and 3 d image data for obtaining destination object；GTG is excellent
Change module 920, for carrying out GTG optimization to slice image data described in every frame；Enhancing processing module 930, for excellent to GTG
Slice image data after change carries out enhancing treatment, the sectioning image after being processed；Parameter optimization module 940, for three
Dimensional data image carries out parameter optimization, obtains the 3-D view after optimization.
Further, GTG optimization module 920 includes：
Acquiring unit, for obtaining demodulation back panel degrees of data from the ultrasonic echo data of destination object；
Standard Eigenvalue computing unit, the Standard Eigenvalue for calculating demodulation back panel degrees of data；
Typical value computing unit, the typical value for calculating Standard Eigenvalue along depth direction；
Gain compensating curve acquiring unit, for calculating the difference between Standard Eigenvalue and typical value, obtains gain benefit
Repay curve；
GTG optimizes unit, for gain compensating curve to be acted on into slice image data, obtains cutting after GTG optimization
Picture data.
Further, processing module 930 is strengthened, including：
Resolving cell, for GTG to be optimized after slice image data carry out pyramid decomposition, obtain multiple frequencies into
The image sublayer of part；
Anisotropy parameter processing unit, for carrying out anisotropy parameter treatment respectively to each sublayer image, obtains
Sublayer image after multiple anisotropy parameter treatment；
Image reconstruction unit, for carrying out the sublayer image after the treatment of the multiple anisotropy parameter based on pyramid
Image Reconstruction, the sectioning image after being processed, wherein, described is the pyramid decomposition based on pyramidal Image Reconstruction
Inverse process.
Further, the resolving cell includes：
First filtering subunit, for carrying out LPF to input image data；
Down-sampled subelement, it is down-sampled for being carried out according to transverse and longitudinal direction to filtered input image data, dropped
Sampled images, wherein every layer of input image data be last layer it is down-sampled after data；
Up-sampling subelement, for up-sampling down-sampled image according to transverse and longitudinal direction, obtains up-sampling image；
First filtering subunit, is additionally operable to carry out LPF to up-sampling image；
Sublayer image obtains subelement, for input image data to be subtracted into filtered up-sampling image, is made a reservation for
The sublayer image of radio-frequency component.
Further, the anisotropy parameter processing unit includes：
Second filtering subunit, for carrying out LPF to sub- tomographic image；
Gradient calculation subelement, horizontal gradient and vertical gradient for calculating filtered sublayer image；
Tissue tensor construction subelement, for according to horizontal gradient and vertical gradient texture tensor, and computation organization's tensor
Characteristic value；
Expansion tensor computation subelement, for according to tissue tensor sum characteristic value, being calculated expansion tensor；
Discrete anisotropy parameter subelement, for carrying out discrete anisotropy expansion to sub- tomographic image according to expansion tensor
Dissipate, obtain the sublayer image after anisotropy parameter treatment.
Further, parameter optimization module 940：
Execution unit, for carrying out histogram distribution statistics to 3 d image data, and it is equal to perform self adaptation to histogram
Weighing apparatus, the 3 d image data after being equalized；
Statistic unit, average for the voxel gray values of the 3 d image data after statistical equilibrium, big rule threshold value and
Boundary gray value；
Optimal Parameters value determining unit, for respectively according to average, big rule threshold value and boundary gray value and Optimal Parameters
Between mapping relations, determine Optimal Parameters value；
Optimization unit, for being optimized to 3 d image data using Optimal Parameters value and rotating light angle, to obtain
Take the 3-D view after optimization.
A kind of four-dimensional ultrasound imaging optimization system that the present embodiment is provided, obtains the slice image data of destination object first
And 3 d image data；Then GTG optimization is carried out to slice image data described in every frame, and to the slice map after GTG optimization
As data carry out enhancing treatment, the sectioning image after being processed；And parameter optimization is carried out to 3 d image data, obtain optimization
3-D view afterwards；Realized by above-mentioned technological means carries out rapid Optimum to four-dimensional ultrasound image, obtained performance it is more excellent,
The more preferable four-dimensional ultrasound image of stereoeffect.
It will be appreciated by those skilled in the art that all or part of step in realizing above-described embodiment method can be by
Program is completed to instruct the hardware of correlation, and the program storage is in a storage medium, including some instructions are used to so that one
Individual equipment (can be single-chip microcomputer, chip etc.) or processor (processor) perform the application each embodiment methods described
All or part of step.And foregoing storage medium includes：USB flash disk, mobile hard disk, read-only storage (ROM, Read-Only
Memory), random access memory (RAM, Random Access Memory), magnetic disc or CD etc. are various can store journey
The medium of sequence code.
Note, above are only presently preferred embodiments of the present invention and institute's application technology principle.It will be appreciated by those skilled in the art that
The invention is not restricted to specific embodiment described here, can carry out for a person skilled in the art various obvious changes,
Readjust and substitute without departing from protection scope of the present invention.Therefore, although the present invention is carried out by above example
It is described in further detail, but the present invention is not limited only to above example, without departing from the inventive concept, also
More other Equivalent embodiments can be included, and the scope of the present invention is determined by scope of the appended claims.
Claims (10)
1. a kind of four-dimensional ultrasound image optimization method, the four-dimensional ultrasound image includes the sectioning image and graphics of destination object
Picture, it is characterised in that the described method comprises the following steps：
Obtain the slice image data and 3 d image data of destination object；
GTG optimization is carried out to slice image data described in every frame, and the slice image data after GTG optimization is carried out at enhancing
Reason, the sectioning image after being processed；
Parameter optimization is carried out to 3 d image data, the 3-D view after optimization is obtained.
2. method according to claim 1, it is characterised in that described that GTG optimization is carried out to sectioning image described in every frame
Step includes：
Demodulation back panel degrees of data is obtained from the ultrasonic echo data of destination object；
Calculate the Standard Eigenvalue of demodulation back panel degrees of data；
The typical value of Standard Eigenvalue is calculated along depth direction；
The difference between Standard Eigenvalue and typical value is calculated, gain compensating curve is obtained；
Gain compensating curve is acted on into slice image data, the slice image data after GTG optimization is obtained.
3. method according to claim 1, it is characterised in that the slice image data after optimizing to GTG is carried out at enhancing
The step of reason, sectioning image after being processed, includes：
Slice image data after GTG is optimized carries out pyramid decomposition, obtains the image sublayer of multiple radio-frequency components；
Anisotropy parameter treatment is carried out respectively to each sublayer image, the sublayer figure after multiple anisotropy parameter treatment is obtained
Picture；
Sublayer image after the treatment of the multiple anisotropy parameter is carried out based on pyramidal Image Reconstruction, after being processed
Sectioning image, wherein, described is the inverse process of the pyramid decomposition based on pyramidal Image Reconstruction.
4. method according to claim 3, it is characterised in that include the step of the pyramid decomposition：
LPF is carried out to input image data；
Filtered input image data is carried out according to transverse and longitudinal direction down-sampled, obtain down-sampled image, wherein every layer defeated
Enter view data for last layer it is down-sampled after data；
Down-sampled image is up-sampled according to transverse and longitudinal direction, obtains up-sampling image；
LPF is carried out to up-sampling image；
Input image data is subtracted into filtered up-sampling image, the sublayer image of preset frequency composition is obtained.
5. method according to claim 3, it is characterised in that carried out at anisotropy parameter to sublayer image each described
Reason, includes the step of obtain the sublayer image after the treatment of multiple anisotropy parameters：
LPF is carried out to sub- tomographic image；
Calculate the horizontal gradient and vertical gradient of filtered sublayer image；
According to horizontal gradient and vertical gradient texture tensor, and computation organization's tensor characteristic value；
According to tissue tensor sum characteristic value, expansion tensor is calculated；
Discrete anisotropy parameter is carried out to sub- tomographic image according to expansion tensor, the sublayer figure after anisotropy parameter treatment is obtained
Picture.
6. method according to claim 1, it is characterised in that described the step of carry out parameter optimization to 3 d image data
Including：
Histogram distribution statistics is carried out to 3 d image data, and adaptive equalization, after being equalized three are performed to histogram
Dimensional data image；
The average of the voxel gray values of the 3 d image data after statistical equilibrium, big rule threshold value and boundary gray value；
Respectively according to average, big rule threshold value and the mapping relations between boundary gray value and Optimal Parameters, Optimal Parameters are determined
Value；
3 d image data is optimized using Optimal Parameters value and rotating light angle, to obtain the graphics after optimization
Picture.
7. a kind of four-dimensional ultrasound image optimization system, the four-dimensional ultrasound image includes the sectioning image and graphics of destination object
Picture, it is characterised in that the system includes：
Acquisition module, slice image data and 3 d image data for obtaining destination object；
GTG optimization module, for carrying out GTG optimization to every frame slice image data；
Enhancing processing module, for optimizing to GTG after slice image data carry out enhancing treatment, the section after being processed
Image；
Parameter optimization module, for carrying out parameter optimization to 3 d image data, obtains the 3-D view after optimization.
8. system according to claim 7, it is characterised in that the GTG optimization module includes：
Acquiring unit, for obtaining demodulation back panel degrees of data from the ultrasonic echo data of destination object；
Standard Eigenvalue computing unit, the Standard Eigenvalue for calculating demodulation back panel degrees of data；
Typical value computing unit, the typical value for calculating Standard Eigenvalue along depth direction；
Gain compensating curve acquiring unit, for calculating the difference between Standard Eigenvalue and typical value, obtains gain compensation bent
Line；
GTG optimizes unit, for gain compensating curve to be acted on into slice image data, obtains the slice map after GTG optimization
As data.
9. system according to claim 7, it is characterised in that the enhancing processing module includes：
Resolving cell, for GTG to be optimized after slice image data carry out pyramid decomposition, obtain multiple radio-frequency components
Image sublayer；
Anisotropy parameter processing unit, for carrying out anisotropy parameter treatment respectively to each sublayer image, obtains multiple
Sublayer image after anisotropy parameter treatment；
Image reconstruction unit, for carrying out the sublayer image after the treatment of the multiple anisotropy parameter based on pyramidal figure
As reconstructing, the sectioning image after being processed, wherein, described is the inverse of the pyramid decomposition based on pyramidal Image Reconstruction
Process.
10. system according to claim 9, it is characterised in that the resolving cell includes：
First filtering subunit, for carrying out LPF to input image data；
Down-sampled subelement, it is down-sampled for being carried out according to transverse and longitudinal direction to filtered input image data, obtain down-sampled
Image, wherein every layer of input image data be last layer it is down-sampled after data；
Up-sampling subelement, for up-sampling down-sampled image according to transverse and longitudinal direction, obtains up-sampling image；
First filtering subunit, is additionally operable to carry out LPF to up-sampling image；
Sublayer image obtains subelement, for input image data to be subtracted into filtered up-sampling image, obtains preset frequency
The sublayer image of composition.
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| WO2018113283A1 (en) * | 2016-12-23 | 2018-06-28 | 深圳开立生物医疗科技股份有限公司 | Method and system for optimizing four-dimensional ultrasound image |
| 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 |
| WO2021169086A1 (en) * | 2020-02-28 | 2021-09-02 | 深圳开立生物医疗科技股份有限公司 | Method, apparatus and device for generating four-dimensional radiographic image, and storage medium |
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Citations (5)
| 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***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)
| 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***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 |
-
2016
- 2016-12-23 CN CN201611205838.XA patent/CN106725612B/en active Active
-
2017
- 2017-07-19 WO PCT/CN2017/093471 patent/WO2018113283A1/en active Application Filing
Patent Citations (5)
| 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***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 |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018113283A1 (en) * | 2016-12-23 | 2018-06-28 | 深圳开立生物医疗科技股份有限公司 | Method and system for optimizing four-dimensional ultrasound image |
| 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 |
| WO2021169086A1 (en) * | 2020-02-28 | 2021-09-02 | 深圳开立生物医疗科技股份有限公司 | Method, apparatus and device for generating four-dimensional radiographic image, and storage medium |
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