CN110393549A - A kind of method and device automatically adjusting ultrasound image gain - Google Patents
A kind of method and device automatically adjusting ultrasound image gain Download PDFInfo
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- 238000002604 ultrasonography Methods 0.000 title claims abstract description 43
- 238000000034 method Methods 0.000 title claims abstract description 29
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- 239000000284 extract Substances 0.000 claims abstract description 11
- 239000000523 sample Substances 0.000 claims description 18
- 230000011218 segmentation Effects 0.000 claims description 12
- 238000004422 calculation algorithm Methods 0.000 claims description 9
- 238000010408 sweeping Methods 0.000 claims description 3
- 238000000465 moulding Methods 0.000 claims 1
- 230000003044 adaptive effect Effects 0.000 abstract description 3
- 238000012913 prioritisation Methods 0.000 abstract description 3
- 238000005457 optimization Methods 0.000 description 8
- 238000003709 image segmentation Methods 0.000 description 7
- 230000008901 benefit Effects 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 239000011159 matrix material Substances 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000003384 imaging method Methods 0.000 description 3
- 238000012285 ultrasound imaging Methods 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
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- 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
- A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
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- G06T5/70—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
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- 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
Abstract
The invention discloses a kind of methods for automatically adjusting ultrasound image gain, comprising steps of the noise image that S11. is popped one's head under empty sweep by fitting, obtains the noisy image data in image to be processed；S12. the noisy image data obtained according to denoises image data to be processed；S13. the type of image data to be processed then jumps to step S14 if organization chart picture data after judgement denoising；S14. organization chart picture after the denoising is split, extracts the tissue regions in image；S15. entire gain is calculated according to the tissue regions of the extraction, and is applied to general image.The purpose of the present invention is being further improved the Gain Automatic prioritization scheme of ultrasonic B-mode image, noise, boundary etc. is avoided to enable gain adjustment to be adaptive to different application scenarios the interference of final result while using organizational information.
Description
Technical field
The present invention relates to ultrasonic imaging technique field more particularly to a kind of methods and dress for automatically adjusting ultrasound image gain
It sets.
Background technique
Gain adjustment is always link important in ultrasonic imaging, is all provided with inside general ultrasonic image-forming system solid
Determine gain, this gain usually can be suitably used for most of individuals, but since attenuation degree is not in each patient's body for ultrasonic wave
Together, cause the fixed gain of systemic presupposition to be not fully applicable in, user is at this moment just needed to manually adjust TGC, LGC and whole increasing
Benefit.User's adjustment is not only time-consuming, but also different surely recalls optimal gain curve.
In ultrasound imaging procedure, it is often necessary to which the entire gain of image is adjusted.Suitable image gain can be with
Inhibit picture noise while clearly showing that institutional framework as far as possible.In ultrasound products factory, producer is often directed to
Different application scene sets a fixed value as its entire gain to ultrasound image.However, during actual use,
Since the acoustic impedance of different people is different, the fixed value of factory settings is not most suitable gain, at this moment doctor is needed to adjust manually
Gain knob is saved to obtain optimal yield value.In order to simplify the operation of doctor as far as possible, current most of producers can all be mentioned
For the Automatic Optimal scheme of ultrasound image gain.In existing ultrasonic B-mode image gain optimization method, a part be to point
Ultrasound image after block carries out the classification such as noise image block, organization chart picture block, last basis according to the statistical information of image grayscale
The image mean value of organization chart picture block and the difference of object brightness carry out gain compensation；Another part is the amplitude number to ultrasound image
According to various transformation and threshold decision is carried out, gain compensation is finally carried out.These two kinds of methods have certain limitation, by image point
It is the method for tissue block and noise block when existing simultaneously the structures such as tissue, noise, boundary in image block, can only be classified as
A kind of image block, to have ignored other structures in image block.The entire gain of ultrasound image is in the link of ultrasonic imaging
Usually ultrasound image data is operated, is increased so being reached by being converted to ultrasonic amplitude data and adjusting image
The method of benefit may bring certain error.
It is described if the patent of Publication No. CN109512457A discloses a kind of method for adjusting ultrasound image gain compensation
Method includes: display ultrasound image to be regulated；The adjusting touch signal that detection user applies on the ultrasound image, and root
The area-of-interest on the ultrasound image is determined according to the adjusting touch signal；According to the adjusting touch signal, institute is determined
State the corresponding time gain compensation of area-of-interest and Lateral-Gain Compensation；It is mended according to the time gain compensation and lateral gain
It repays and display adjustment is carried out to the area-of-interest, and show ultrasound image adjusted.The application aim to TGC and
It is adjusted while LGC, to simplify the operation of doctor, improves the efficiency for adjusting ultrasound image gain compensation, and reduce ultrasound inspection
The specification of instrument is surveyed, manufacturing cost and follow-up maintenance cost are saved.Although the effect for adjusting ultrasound image gain compensation can be improved in it
Rate, but still remain the defect for needing doctor to manually adjust.
Summary of the invention
The purpose of the present invention is in view of the drawbacks of the prior art, provide a kind of method for automatically adjusting ultrasound image gain
And device, it is therefore an objective to be further improved the Gain Automatic prioritization scheme of ultrasonic B-mode image, be kept away while using organizational information
Exempt from the interference to final result such as noise, boundary, gain adjustment is enabled to be adaptive to different application scenarios.
In order to achieve the goal above, the invention adopts the following technical scheme:
A method of automatic adjustment ultrasound image gain, comprising steps of
S1. the noise image under being swept by fitting probe sky, obtains the noisy image data in image to be processed；
S2. the noisy image data obtained according to denoises image data to be processed；
S3. the type of image data to be processed then jumps to step S4 if organization chart picture data after judgement denoising；
S4. organization chart picture after the denoising is split, extracts the tissue regions in image；
S5. entire gain is calculated according to the tissue regions of the extraction, and is applied to general image.
Further, the step S1 is specifically included:
S11. the empty noisy image data swept of acquisition different type probe, and obtain being averaged for the noisy image data
Value；
S12. the noise image after the acquirement average value is divided into several image blocks, and records each image block center
Location of pixels and gray value；
S13. the location of pixels and gray value are fitted by noise data fitting formula, obtain fitting coefficient；
S14. noise image is obtained according to the location of pixels of the fitting coefficient and image data to be processed.
Further, in step S2 to image denoising to be processed be subtracted by image data to be processed fitting obtain make an uproar
Acoustic image data are denoised.
Further, the type of image data to be processed is by presetting noise threshold after judgement denoising in the step S3
Judgement, if image data to be processed is less than noise threshold after denoising, the image to be processed is noise image；If after denoising
Image data to be processed is greater than noise threshold, then the image to be processed is organization chart picture.
Further, if the image to be processed is noise image, the entirety using preset yield value as image
Gain.
Further, step S4 is specifically included:
S41. the mean μ and standard deviation δ of the grey scale pixel value of organization chart picture after the denoising are calculated；
S42. piecemeal is carried out to organization chart picture after the denoising, obtains several image subblocks；Gray value is searched in μ ± δ model
The pixel enclosed；
S43. organization chart picture after denoising is split using algorithm of region growing.
Further, the step S5 specifically includes the gray average for obtaining the tissue regions of the extraction, and according to institute
The difference for stating gray average and default tissue brightness of image obtains entire gain.
Correspondingly, also providing a kind of device for automatically adjusting ultrasound image gain characterized by comprising
First fitting module obtains the noise in image to be processed for the noise image under sweeping by fitting probe sky
Image data；
Module is denoised, the noisy image data for obtaining according to denoises image data to be processed；
Judgment module, for judging the type of image data to be processed after denoising；
First segmentation module extracts the tissue regions in image for being split to organization chart picture after the denoising；
First computing module for calculating entire gain according to the tissue regions of the extraction, and is applied to general image.
Further, first fitting module specifically includes:
Acquisition module for acquiring the empty noisy image data swept of different type probe, and obtains the noise image number
According to average value；
Second segmentation module, for the noise image after the average value of the acquirement to be divided into several image blocks, and remembers
Record the location of pixels and gray value at each image block center；
The location of pixels and gray value are fitted by noise data fitting formula, are obtained by the second fitting module
Fitting coefficient；
Module is obtained, noise image is obtained according to the location of pixels of the fitting coefficient and image data to be processed.
Further, the first segmentation module specifically includes:
Second computing module, for calculating the mean μ and standard deviation δ of the grey scale pixel value of organization chart picture after the denoising；
Third divides module, for carrying out piecemeal to organization chart picture after the denoising, obtains several image subblocks；
Searching module, for searching gray value in the pixel of μ ± δ range；
4th segmentation module, for being split using algorithm of region growing to organization chart picture after denoising.
Compared with prior art, the present invention beneficial effect is:
The method and device of ultrasound image entire gain optimization provided by the invention, certainly according to the intensity profile of entire image
The dynamic seed point for determining image segmentation carries out image segmentation after entire image piecemeal respectively, extracts the tissue area in image
Domain, it is last to determine image entire gain value according to the image organizational region extracted with organizational goal brightness and acted on whole
On width image, the organizational information progress whole increasing reported as precisely as possible using image in the case where avoiding boundary, noise is realized
The optimization of benefit.
Detailed description of the invention
Fig. 1 is a kind of method flow diagram for automatic adjustment ultrasound image gain that embodiment one provides；
Fig. 2 is a kind of structure drawing of device for automatic adjustment ultrasound image gain that embodiment two provides；
Fig. 3 is a kind of ultrasonic image-forming system structure chart that embodiment three provides.
Specific embodiment
Illustrate embodiments of the present invention below by way of specific specific example, those skilled in the art can be by this specification
Other advantages and efficacy of the present invention can be easily understood for disclosed content.The present invention can also pass through in addition different specific realities
The mode of applying is embodied or practiced, the various details in this specification can also based on different viewpoints and application, without departing from
Various modifications or alterations are carried out under spirit of the invention.It should be noted that in the absence of conflict, following embodiment and implementation
Feature in example can be combined with each other.
The purpose of the present invention is in view of the drawbacks of the prior art, provide a kind of method for automatically adjusting ultrasound image gain
And device, it is therefore an objective to be further improved the Gain Automatic prioritization scheme of ultrasonic B-mode image, be kept away while using organizational information
Exempt from the interference to final result such as noise, boundary, gain adjustment is enabled to be adaptive to different application scenarios.
Embodiment one
It should be noted that a kind of executing subject of the method for automatic adjustment ultrasound image gain of the present embodiment is ultrasound
Imaging system.
A kind of method automatically adjusting ultrasound image gain provided in this embodiment, as shown in Figure 1, comprising steps of
S11. the noise image under being swept by fitting probe sky, obtains the noisy image data in image to be processed；
S12. the noisy image data obtained according to denoises image data to be processed；
S13. the type of image data to be processed then jumps to step S14 if organization chart picture data after judgement denoising；
S14. organization chart picture after the denoising is split, extracts the tissue regions in image；
S15. entire gain is calculated according to the tissue regions of the extraction, and is applied to general image.
In step s 11, by fitting image to be processed, the noise image of image to be processed is obtained, is specifically included:
S111. the empty noisy image data swept of acquisition different type probe, and obtain being averaged for the noisy image data
Value；
In the present embodiment, the image data under different type probe sky is swept is acquired first, and noise pattern is then calculated
As the average value of data.Wherein, the formula of average value is calculated are as follows:
Wherein, n indicates that different types of probe sum, m indicate the number of different type probe acquisition noise image, AijTable
Show collected noise image matrix,Indicate the average value of noise image.
S112. the noise image after the acquirement average value is divided into several image blocks, and recorded in each image block
The location of pixels and gray value of the heart；
In the present embodiment, the noise image after acquirement average value is averagely divided into multiple size identical image blocks, and
Record the location of pixels and gray value at each image block center；
S113. the location of pixels and gray value are fitted by noise data fitting formula, obtain fitting coefficient；
In the present embodiment, location of pixels and gray value are substituted into the fitting formula of noise data, digital simulation coefficient.Its
In, the fitting formula of noise data are as follows:
B (x, y)=a0+a1x+a2y+a3x2+a4y2+a5xy+a6x3+a7x2y+a8xy2+a9y3；
Wherein, the multinomial that above-mentioned formula is order when being three ranks, (x, y) indicates seat of the pixel in image array
Mark, ai(i=1,2,3 ..., 9) indicate fitting coefficient, and B (x, y) indicates coordinate is the gray value of the pixel of (x, y).Three ranks are more
10 coefficients of Xiang Shiyou, need 10 sampled points that can solve whole coefficients.
It should be noted that in a practical situation, preferably fitting effect and reduce small size to respective pixel in order to obtain
Spend the sensibility of fluctuation, it usually needs using the multinomial of higher order and solve fitting coefficient with more sampled point.Therefore,
Three rank multinomial provided by the present embodiment is not limited only to for the formula of fitting.
S114. noise image is obtained according to the location of pixels of the fitting coefficient and image data to be processed.
In the present embodiment, noise data fitting formula is generated according to fitting coefficient acquired in step S113, it will be wait locate
In the noise data fitting formula that the coordinate (x, y) of pixel substitutes into respectively in reason image, corresponding grey scale pixel value is calculated,
And then obtain the corresponding noise image of organization chart picture.
It in the present embodiment, is to more easily integrate the noise number under different situations using the noise image of fitting
According to, while when scan depths difference, processing is more flexibly.The noise data of acquisition different type probe can make fitting system
Number is preferably suitable for different probe situations, is in order in the feelings for the accuracy for guaranteeing fitting coefficient calculating to image block
Under condition, calculation amount is reduced.
In step s 12, image data to be processed is denoised according to the obtained noise image.
Specifically, being the noise image number for subtracting fitting by image data to be processed and obtaining to image denoising to be processed
According to.Wherein, noise refers to electronic noise or thermal noise, and in the case where sky is swept, picture noise may be considered ultrasonic probe
White noise is distributed among entire image, is influenced subsequent calculating to make it not, is needed first to be denoised.
In step s 13, judge that the type of image data to be processed after denoising then jumps to step if organization chart picture data
Rapid S14；
In the present embodiment, the mean μ by image data to be processed after denoising in step S12 is calculated, wherein calculating is gone
The formula of the mean μ of image data to be processed after making an uproar are as follows:
Wherein, x indicates that the abscissa of pixel in image, y indicate the ordinate of pixel in image, and GVxy indicates coordinate is
The grey scale pixel value of (x, y), n indicate the line number of image data, and m indicates that the columns of image data, μ indicate that the gray scale of image is equal
Value.
It in the present embodiment, further include a default noise threshold, if image data mean μ to be processed is less than noise after denoising
Threshold value, then the image to be processed is noise image；If image data mean μ to be processed is greater than noise threshold, institute after denoising
Stating image to be processed is organization chart picture.
It in the present embodiment, further include a default yield value, if image to be processed is noise image, using preset increasing
Entire gain of the benefit value as image.
If image to be processed is organization chart picture, S14 is thened follow the steps.
In step S14, organization chart picture after the denoising is split, extracts the tissue regions in image, it is specific to wrap
It includes:
S141. the mean μ and standard deviation δ of the grey scale pixel value of organization chart picture after the denoising are calculated；
In the present embodiment, the formula of the mean μ of the grey scale pixel value of organization chart picture after denoising is calculated are as follows:
Wherein, x indicates that the abscissa of pixel in image, y indicate the ordinate of pixel in image, and GVxy indicates coordinate is
The grey scale pixel value of (x, y), n indicate the line number of image data, and m indicates that the columns of image data, μ indicate that the gray scale of image is equal
Value.
It should be noted that the mean μ of the grey scale pixel value of organization chart picture is in step S13 after denoising in step S141
The mean μ of image data to be processed after the denoising being calculated.
In the present embodiment, the formula of the standard deviation δ of the grey scale pixel value of organization chart picture after denoising is calculated are as follows:
Wherein, x indicates that the abscissa of pixel in image, y indicate the ordinate of pixel in image, and GVxy indicates coordinate is
The grey scale pixel value of (x, y), n indicate the line number of image data, and m indicates that the columns of image data, μ indicate organization chart picture after denoising
Grey scale pixel value mean value, δ indicate denoising after organization chart picture grey scale pixel value standard deviation.
S142. piecemeal is carried out to organization chart picture after the denoising, obtains several image subblocks；Gray value is searched in μ ± δ model
The pixel enclosed；
In the present embodiment, organization chart picture after denoising is divided into the identical several image subblocks of size, and from each
The central point of image subblock begins looking for pixel of the gray value within the scope of μ ± δ, if finding, as region growing
Seed point.
It should be noted that the quantity of piecemeal can be set by actual conditions.
The purpose of the present embodiment piecemeal is the robustness in order to increase calculating speed and statistics.The size of image subblock can lead to
Actual conditions are crossed to be set.But, test of many times obtains through the invention, and block count will cause statistical data shakiness too much
It is fixed, it is then possible to influence calculating speed very little.When piecemeal suitable block count can be selected according to the concrete condition of image to be optimized
Amount carries out piecemeal.
S143. organization chart picture after denoising is split using algorithm of region growing.
In the present embodiment, the algorithm of image segmentation is using classical algorithm of region growing, after successively extracting denoising
The tissue regions of organization chart picture, while in order to improve the speed of algorithm of region growing, multiple seed points are had chosen, region growing is calculated
Method specifically:
S1431. centered on the seed point of the region growing got in step S142, in eight centered on seed point
The pixel for being no more than gray difference threshold with initial seed point gray scale difference is searched in neighborhood of pixels to charge in matrix A if finding,
Seed point is updated simultaneously；
S1432. judge whether the region recorded in the matrix A is greater than region threshold, if more than being then stored in matrix B；
If being less than, ignore the region recorded in A；
S1433. step S1431, step S1432 are repeated, until having traversed entire image.
In step S15, entire gain is calculated according to the tissue regions of the extraction, and be applied to general image.
Specifically, obtaining the gray average of the tissue regions of the extraction, and according to the gray average and default tissue
The difference of brightness of image obtains final entire gain.
Compared with prior art, the present invention beneficial effect is:
The method of ultrasound image entire gain optimization provided by the invention, automatically determines according to the intensity profile of entire image
The seed point of image segmentation carries out image segmentation after entire image piecemeal respectively, extracts the tissue regions in image, finally
Image entire gain value is determined according to the image organizational region and organizational goal brightness extracted and is acted on entire image
On, realize that the organizational information reported as precisely as possible using image carries out the excellent of entire gain in the case where avoiding boundary, noise
Change.
Embodiment two
It should be noted that a kind of executing subject of the device of automatic adjustment ultrasound image gain of the present embodiment is ultrasound
Imaging system.
A kind of device automatically adjusting ultrasound image gain provided in this embodiment, as shown in Figure 2, comprising:
First fitting module 11 obtains making an uproar in image to be processed for the noise image under sweeping by fitting probe sky
Acoustic image data；
Module 12 is denoised, the noisy image data for obtaining according to denoises image data to be processed；
Judgment module 13, for judging the type of image data to be processed after denoising；
First segmentation module 14 extracts the tissue regions in image for being split to organization chart picture after the denoising；
First computing module 15 for calculating entire gain according to the tissue regions of the extraction, and is applied to overall diagram
Picture.
First fitting module 11 specifically includes:
Acquisition module for acquiring the empty noisy image data swept of different type probe, and obtains the noise image number
According to average value；
Second segmentation module, for the noise image after the average value of the acquirement to be divided into several image blocks, and remembers
Record the location of pixels and gray value at each image block center；
The location of pixels and gray value are fitted by noise data fitting formula, are obtained by the second fitting module
Fitting coefficient；
Module is obtained, noise image is obtained according to the location of pixels of the fitting coefficient and image data to be processed.
Denoising module 12 be specially to image denoising to be processed be subtracted by image data to be processed fitting obtain make an uproar
Acoustic image data are denoised.
Judgment module 13 is specific further include:
First presetting module, for presetting a noise threshold；
Second presetting module, for presetting a yield value.
In the present embodiment, if image data to be processed is less than noise threshold after denoising, the image to be processed is to make an uproar
Acoustic image；If image data to be processed is greater than noise threshold after denoising, the image to be processed is organization chart picture.
In the present embodiment, if image to be processed is noise image, the entirety using preset yield value as image
Gain.
If image to be processed is organization chart picture, S14 is thened follow the steps.
First segmentation module 14 specifically includes:
Second computing module, for calculating the mean μ and standard deviation δ of the grey scale pixel value of organization chart picture after the denoising；
Third divides module, for carrying out piecemeal to organization chart picture after the denoising, obtains several image subblocks；
Searching module, for searching gray value in the pixel of μ ± δ range；
4th segmentation module, for being split using algorithm of region growing to organization chart picture after denoising.
First computing module 15 is specially the gray average for obtaining the tissue regions of the extraction, and equal according to the gray scale
The difference of value and default tissue brightness of image obtains entire gain.
The present embodiment is a kind of device for automatically adjusting ultrasound image gain corresponding with embodiment one, specific implementation
Mode is similar with embodiment one, and this will not be repeated here.
Compared with prior art, the present invention beneficial effect is:
The device of ultrasound image entire gain optimization provided by the invention, automatically determines according to the intensity profile of entire image
The seed point of image segmentation carries out image segmentation after entire image piecemeal respectively, extracts the tissue regions in image, finally
Image entire gain value is determined according to the image organizational region and organizational goal brightness extracted and is acted on entire image
On, realize that the organizational information reported as precisely as possible using image carries out the excellent of entire gain in the case where avoiding boundary, noise
Change.
Embodiment three
A kind of ultrasonic image-forming system provided in this embodiment, as shown in figure 3, including sequentially connected transmitting module, reception
Module, Beam synthesis module, signal processing module, image processing module, image entire gain module, image display；
Specifically, ultrasonic wave is emitted in imageable target by transmitting module, and is received by receiving module, by wave beam
Synthesis module obtains rf data, is obtaining envelope amplitude data by signal processing module, after image processing module
Data to after image data, that is, log-compressed, image data are re-fed into ultrasonic B-mode image entire gain Automatic Optimal module
After carrying out gain optimization, it is ultimately delivered to image display and is shown.
Wherein, the gain method that image data progress gain optimization is through this embodiment one or two is subjected to gain optimization
's.
Note that the above is only a better embodiment of the present invention and the applied technical principle.It will be appreciated by those skilled in the art that
The invention is not limited to the specific embodiments described herein, be able to carry out for a person skilled in the art it is various it is apparent variation,
It readjusts and substitutes without departing from protection scope of the present invention.Therefore, although being carried out by above embodiments to the present invention
It is described in further detail, but the present invention is not limited to the above embodiments only, without departing from the inventive concept, also
It may include more other equivalent embodiments, and the scope of the invention is determined by the scope of the appended claims.
Claims (10)
1. a kind of method for automatically adjusting ultrasound image gain, which is characterized in that comprising steps of
S1. the noise image under being swept by fitting probe sky, obtains the noisy image data in image to be processed；
S2. the noisy image data obtained according to denoises image data to be processed；
S3. the type of image data to be processed then jumps to step S4 if organization chart picture data after judgement denoising；
S4. organization chart picture after the denoising is split, extracts the tissue regions in image；
S5. entire gain is calculated according to the tissue regions of the extraction, and is applied to general image.
2. a kind of method for automatically adjusting ultrasound image gain according to claim 1, which is characterized in that the step S1
It specifically includes:
S11. the empty noisy image data swept of acquisition different type probe, and obtain the average value of the noisy image data；
S12. the noise image after the acquirement average value is divided into several image blocks, and records the picture at each image block center
Plain position and gray value；
S13. the location of pixels and gray value are fitted by noise data fitting formula, obtain fitting coefficient；
S14. noise image is obtained according to the location of pixels of the fitting coefficient and image data to be processed.
3. a kind of method for automatically adjusting ultrasound image gain according to claim 2, which is characterized in that right in step S2
Image denoising to be processed is to subtract the noisy image data that fitting obtains by image data to be processed to be denoised.
4. a kind of method for automatically adjusting ultrasound image gain according to claim 1 or 2 or 3, which is characterized in that described
The type of image data to be processed judge by default noise threshold after judgement denoising in step S3, if to be processed after denoising
Image data is less than noise threshold, then the image to be processed is noise image；If image data to be processed is greater than and makes an uproar after denoising
Sound threshold value, then the image to be processed is organization chart picture.
5. a kind of method for automatically adjusting ultrasound image gain according to claim 4, which is characterized in that if described wait locate
Reason image is noise image, then the entire gain using preset yield value as image.
6. a kind of method for automatically adjusting ultrasound image gain according to claim 1 or 2 or 3, which is characterized in that step
S4 is specifically included:
S41. the mean μ and standard deviation δ of the grey scale pixel value of organization chart picture after the denoising are calculated；
S42. piecemeal is carried out to organization chart picture after the denoising, obtains several image subblocks；Gray value is searched in μ ± δ range
Pixel；
S43. organization chart picture after denoising is split using algorithm of region growing.
7. a kind of method for automatically adjusting ultrasound image gain according to claim 6, which is characterized in that the step S5
The gray average for obtaining the tissue regions of the extraction is specifically included, and according to the gray average and default tissue brightness of image
Difference obtain entire gain.
8. a kind of device for automatically adjusting ultrasound image gain characterized by comprising
First fitting module obtains the noise image in image to be processed for the noise image under sweeping by fitting probe sky
Data；
Module is denoised, the noisy image data for obtaining according to denoises image data to be processed；
Judgment module, for judging the type of image data to be processed after denoising；
First segmentation module extracts the tissue regions in image for being split to organization chart picture after the denoising；
First computing module for calculating entire gain according to the tissue regions of the extraction, and is applied to general image.
9. a kind of device for automatically adjusting ultrasound image gain according to claim 8, which is characterized in that described first is quasi-
Molding block specifically includes:
Acquisition module for acquiring the empty noisy image data swept of different type probe, and obtains the noisy image data
Average value；
Second segmentation module for the noise image after the average value of the acquirement to be divided into several image blocks, and records every
The location of pixels and gray value at a image block center；
The location of pixels and gray value are fitted by noise data fitting formula, are fitted by the second fitting module
Coefficient；
Module is obtained, noise image is obtained according to the location of pixels of the fitting coefficient and image data to be processed.
10. a kind of device of automatic adjustment ultrasound image gain according to claim 8 or claim 9, which is characterized in that described
One segmentation module specifically includes:
Second computing module, for calculating the mean μ and standard deviation δ of the grey scale pixel value of organization chart picture after the denoising；
Third divides module, for carrying out piecemeal to organization chart picture after the denoising, obtains several image subblocks；
Searching module, for searching gray value in the pixel of μ ± δ range；
4th segmentation module, for being split using algorithm of region growing to organization chart picture after denoising.
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