CN106618632A - Ultrasonic imaging system and method with automatic optimization - Google Patents
Ultrasonic imaging system and method with automatic optimization Download PDFInfo
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Abstract
The invention relates to an ultrasonic imaging system and method with automatic optimization. The system is characterized by comprising a probe, a wave beam combining module, a signal processing module, an automatic optimization module, a scanning exchange module, an image processing module and a displayer. Ultrasonic echo signals received by the probe are subjected to wave beam combining to form signal line data through the wave beam combining module; signal treatment is carried out on the signal line data to obtain an ultrasonic image through the signal processing module; optimization treatment is carried out on the ultrasonic image through the automatic optimization module. The automatic optimization module comprises an image monitoring device, a parameter calculating module, a gain compensator and a noise suppressor. The image monitoring device analyzes the difference between a current frame ultrasonic image and a previous frame ultrasonic image in real time; the parameter calculating module calculates imaging parameters to obtain follow-up imaging parameters; the gain compensator calculates a gain compensation image according to an output result of the parameter calculating module; the noise suppressor calculates a noise suppressing image according to the output result of the parameter calculating module. The ultrasonic imaging system and method can monitor the change of the imaging state in real time during imaging to achieve automatic optimization of the image.
Description
Technical field
The present invention relates to the ultrasonic image-forming system and method for a kind of Automatic Optimal, belong to ultrasonic imaging technique field.
Background technology
Ultrasonic wave is propagated in human body and has decay, and this decay is different with different imaging individualities, check point,
Therefore doctor needs to manually adjust imaging parameters such as time gain compensation, global gain, dynamic range, GTG during diagnosis
Mapping curve etc. is come the diagnosis that obtains optimal imaging effect to complete to firmly believe.It is unrelated with diagnosis that this process increased doctor
Extra work, reduces operating efficiency, is that this needs to automatically configure imaging parameters in ultrasonic imaging, it is quick obtain compared with
Good image, improves the degree of accuracy and the efficiency of diagnosis.
To the compensation of image gain in common ultrasonic diagnostic equipment, usually compensate on depth direction, referred to as DGC
(Depth Gain Compensation, depth gain compensation)；Can be according to imaging frequency and inspection portion in ultrasonic image-forming system
The difference of position pre-sets DGC offsets, but because human body individual difference is than larger, the offset for pre-setting is difficult to be adapted to
Different Individual.It is provided with segmentation toggle on the control panel of ultrasonic device to compensate the gray value of different depth, and this group
The setting of button needs doctor according to Different Individual and different check points to be adjusted manually, and this regulation not only increases doctor
Raw workload, and need appropriate skill.But the one's work of the regulation apparently not doctor to image, only can increase
Burden, reduces efficiency.Therefore to simplify the work of doctor, allow doctor to obtain the qualitative picture firmly believed, need automatic
Gain-adjusted function.
At present, the ultrasonic imaging automatic optimization method that the ultrasonic device on market is used is usually that first ultrasonoscopy is carried out
Piecemeal, then carries out classification and determines whether soft tissue to each image block, and gain compensation is carried out to soft tissue so as to obtain
Even image.It is done so that shortcoming it is obvious：The size of image block can produce impact, image block to the degree of accuracy for calculating
Size is little easily to be disturbed by noise, reduces the robustness of algorithm, and image block size is big to be tied some organizational boundaries etc. again
Structure information is included into, equally have impact on result of calculation；Secondly the offset of different image blocks is different, adjacent image block compensation
The unnatural image ultimately resulted in after compensation of transition between value is possible to mosaic phenomenon occur.These problems all can cause excellent
Image after change is unsatisfactory, or even increases some pseudomorphisms, brings the deterioration of picture quality.
The startup that in addition at present existing some ultrasonic imaging optimization methods need doctor that optimization is pressed according to actual conditions
Key, although compare the machine without this function and alleviate the workload of doctor, but need the interactive operation of doctor, still fall within half from
Dynamic function.
The content of the invention
The purpose of the present invention is to overcome the deficiencies in the prior art, there is provided a kind of ultrasonic image-forming system of Automatic Optimal
And method, can in imaging process real-time monitoring image formation state change, realize the Automatic Optimal of image.
According to the technical scheme that the present invention is provided, the ultrasonic image-forming system of the Automatic Optimal is characterized in that：Including：
The probe of transmitting received ultrasonic signal；Ultrasonic echography echo-signal A that probe is received is entered into Beam synthesis shape
Into the Beam synthesis module of holding wire data A1；Signal transacting is carried out to holding wire data A1 to obtain at the signal of ultrasonoscopy B
Reason module；The Automatic Optimal module of process is optimized to ultrasonoscopy B；Scan conversion module；Image processing module；And,
Display；
The Automatic Optimal module includes：
Image monitor, analyzes in real time the difference of present frame ultrasonoscopy and former frame ultrasonoscopy, and exports triggering letter
Number to parameter calculating module；
Parameter calculating module, carries out the parameter for being calculated follow-up imaging of imaging parameters；
Gain compensator, according to the output result of parameter calculating module, calculates gain compensation image；And,
Noise suppressor, according to the output result of parameter calculating module, calculates noise suppressed image.
Further, described image watch-dog includes：
Status register, for preserving the image formation state of previous frame image；
Feature Calculator, calculates features of ultrasound pattern value；
Status comparator, obtain front end link input ultrasonoscopy characteristic value and with status register in currency
It is compared；And,
Optimal Parameters calculate trigger, for trigger parameter computing module.
Further, the parameter calculating module includes：
Pixels statisticses device, carries out current ultrasonoscopy statistics and obtains Pixel Information；
Pixel classifier, Pixel Information is classified；And,
Image dissector, using the result of classification region analysis are carried out, and analysis result is exported to gain compensator
And noise suppressor.
Further, the probe connection Beam synthesis module, the output end connection signal transacting mould of Beam synthesis module
Block, the output end connection Automatic Optimal module of signal processing module, the output end connection scan conversion module of Automatic Optimal module,
The output end connection image processing module of scan conversion module, the output end connection display of image processing module.
Further, the probe connection Beam synthesis module, the output end connection signal transacting mould of Beam synthesis module
Block, the output end connection scan conversion module of signal processing module, the output end connection Automatic Optimal module of scan conversion module,
The output end connection image processing module of Automatic Optimal module, the output end connection display of image processing module.
Further, the gain compensator calculates gain compensation image, and noise suppressor calculates noise suppressed figure
The computational methods of picture are as follows：
GainCompI (i, j)=TValue-MeanI (i, j),
Wherein, GainCompI (i, j) is gain compensation image, and Tvalue is that uniform formation compensates desired value, and MeanI (i, j) is pixel
Tissue intensity image；NoiseSupI (i, j) is noise suppressed image, and RI (i, j) is mark image, and SupressFactor is to set
Fixed inhibiting factor, i, j are pixel point coordinates；
Optimal Parameters obtained above are applied in follow-up imaging, parameter application process is as follows：
OptI (i, j)=NoiseSupI (i, j) * [I (i, j)+GainCompI (i, j)], wherein, OptI (i, j) is excellent
Image after change, I (i, j) is image before optimization.
The ultrasonic imaging method of the Automatic Optimal, is characterized in that, comprise the following steps：
(1) ultrasonic echography echo-signal A that probe is received forms letter after Beam synthesis module carries out Beam synthesis
, according to A1, signal processing module carries out obtaining ultrasonoscopy B after signal transacting to holding wire data A1 for number line number；
(2) ultrasonoscopy B1s of the ultrasonoscopy B Jing after Automatic Optimal resume module is optimized, specifically includes following step
Suddenly：
Ultrasonoscopy B after signal processing module is processed is input to Automatic Optimal mould by a, first ultrasonic image-forming system
Block, the image monitor in Automatic Optimal module analyzes in real time the difference of present frame ultrasonoscopy and former frame ultrasonoscopy, such as
Fruit difference exceedes the threshold values of setting, and automatic running parameter calculating module carries out the calculating of imaging parameters；
B, parameter calculating module calculate the parameter for follow-up imaging；
C, according to the output result of parameter calculating module, gain compensator calculates gain compensation image, noise suppressor meter
Calculate noise suppressed image；Finally, Automatic Optimal module exports the ultrasonoscopy B1 through Automatic Optimal；
(3) the ultrasonoscopy B1 after optimizing is processed by scan conversion module and image processing module, is finally delivered to show
Show and carry out on device that image shows.
Further, in step (2) a image monitor the course of work：Status register preserves previous frame image
Image formation state, Feature Calculator calculate features of ultrasound pattern value, status comparator obtain front end link input ultrasonoscopy
Characteristic value and be compared with the currency in status register, if beyond setting threshold value, cross Optimal Parameters calculate
Trigger trigger parameter computing module.
Further, in step (3) b parameter calculating module the course of work：Current ultrasonoscopy is input to
Enter statistics in pixels statisticses device, obtain Pixel Information；Pixel Information is input in pixel classifier and is classified；Carry out point
After class, image dissector carries out region analysis using the result of classification.
Further, the method that described image analyzer carries out region analysis is：In a neighborhood of current pixel
Inside it is analyzed, counts the classification situation of pixel in neighborhood, the neighborhood of neighbor is partly overlapping.
Further, according to the output result of image dissector, gain compensation image is calculated in gain compensator, is being made an uproar
Acoustic suppression equipment calculates noise suppressed image；Computational methods are as follows：
GainCompI (i, j)=TValue-MeanI (i, j),
Wherein, GainCompI (i, j) is gain compensation image, and Tvalue is that uniform formation compensates desired value, and MeanI (i, j) is pixel
Tissue intensity image；NoiseSupI (i, j) is noise suppressed image, and RI (i, j) is mark image, and SupressFactor is to set
Fixed inhibiting factor, i, j are pixel point coordinates；
Optimal Parameters obtained above are applied in follow-up imaging, parameter application process is as follows：
OptI (i, j)=NoiseSupI (i, j) * [I (i, j)+GainCompI (i, j)], wherein, OptI (i, j) is excellent
Image after change, I (i, j) is image before optimization.
The ultrasonic image-forming system and method for Automatic Optimal of the present invention, it being capable of the real-time monitoring imaging in imaging process
The change of state, according to the change of image imaging parameters are calculated automatically, and image pixel is classified, and calculate increase on this basis
Benefit compensation image and noise suppressed image, so as to realize the Automatic Optimal of image.Do not need doctor Jing often press optimization function by
Button.After starting guide, different tissues classification is divided into by the statistic of gray value, refines compensation value calculation, so as to
To the ultrasonoscopy of a brightness uniformity.
Description of the drawings
Fig. 1 is the schematic diagram of the ultrasonic image-forming system of Automatic Optimal of the present invention.
Fig. 2 is the statistic histogram that pixel classifier is adopted.
Fig. 3 is the schematic diagram of the Automatic Optimal module.
Fig. 4 is the schematic diagram of described image watch-dog.
Fig. 5 is the schematic diagram of the parameter calculating module.
Fig. 6 is traditional picture portion method schematic diagram.
Fig. 7 is picture portion method schematic diagram of the present invention.
Fig. 8 is another kind of embodiment schematic diagram of the ultrasonic image-forming system of Automatic Optimal of the present invention.
Specific embodiment
With reference to concrete accompanying drawing, the invention will be further described.
As shown in figure 1, in embodiments of the present invention, ultrasonic main frame carries out sending out for ultrasonic signal by control probe 100
Reception is penetrated, ultrasonic echography echo-signal A that probe 100 is received is formed after Beam synthesis module 200 carries out Beam synthesis
At the signals such as holding wire data A1, signal processing module 300 is filtered to holding wire data A1, envelope detected, log-compressed
Obtain ultrasonoscopy B after reason, ultrasonoscopy B optimized after the process of Automatic Optimal module 400 after ultrasonoscopy B1, it is excellent
Ultrasonoscopy B1 after change is processed by scan conversion module 500 and image processing module 600, is finally delivered to display 700
On carry out image and show.
As shown in figure 3, the present invention increased Automatic Optimal module, Automatic Optimal module mould in conventional ultrasound imaging system
Block is monitored to real-time ultrasonic image, is automatically performed the adjustment of parameter and carries out the Automatic Optimal of image, so as to obtain high-quality
Diagnostic image.Automatic Optimal module 400 is included：Image monitor 401, parameter calculating module 402, gain compensator 403,
Noise suppressor 404 etc..First ultrasonic image-forming system is input to the ultrasonoscopy B after signal processing module 300 is processed
Automatic Optimal module 400, the image monitor 401 in Automatic Optimal module 400 in real time analysis present frame ultrasonoscopy B with it is previous
The difference of frame ultrasonoscopy B ', if difference exceedes the threshold values of setting, illustrates that imaging object has occurred that change, this change
The possibly change of check point, it is also possible to be that patient changes, in such a case it is not necessary to doctor manually adjusts imaging
Parameter starts automatic majorization function, and ultrasonic image-forming system automatic running parameter calculating module 402 carries out the meter of imaging parameters
Calculate.Parameter calculating module 402 calculates the parameter for follow-up imaging, and such as gain compensation image parameter and noise suppressed image is joined
Number, parameter calculating module 402 is applied to these parameters on untreated ultrasonoscopy B, and final Automatic Optimal module 400 is exported
Through the ultrasonoscopy B1 of Automatic Optimal.
After ultrasonic image-forming system automatically turns on Automatic Optimal 400 function of module, image monitor 401 enters to ultrasonoscopy
Row analysis, so that it is determined that Optimal Parameters.Image monitor 401 is by status register 4011, Feature Calculator 4012, epidemic situation comparison
Device 4013 and Optimal Parameters calculate the composition such as trigger 4014, and status register 4011 preserves the image formation state of previous frame image,
This image formation state can be image feature value, such as mean flow rate, signal to noise ratio, uniform formation's accounting, or ultrasound figure
As data；Feature Calculator 4012 calculates the features of ultrasound pattern value of input, such as mean flow rate, signal to noise ratio, uniform formation's accounting
Deng；Status comparator 4013 obtain front end link input ultrasonoscopy characteristic value and with status register 4011 in it is current
Value is compared, if beyond the threshold value of setting, then it is assumed that image formation state changes, and by Optimal Parameters trigger is calculated
4014 trigger parameters are calculated.
TSignal=Compare (CurStatus, CurImageFV)>Threshold1:0, wherein, TSignal is tactile
Signal, CurStatus is the image formation state of current frame image, CurImageFV is the characteristic vector of input picture.Compare
Can be that characteristic similarity is calculated as the Euler's distance, or feature difference between feature is absolute for comparison algorithm function
Value sum, such simple computing.If the comparative result of status comparator 4013 is within given threshold, not trigger parameter meter
Calculate module 402, otherwise trigger parameter computing module 402.
After parameter calculating module 402 is triggered, parameter calculating module 402 carries out the calculating of new Optimal Parameters, will be current
Ultrasonoscopy be input in pixels statisticses device 4021 and counted, statistic can be the brightness of the pixel of time-domain, periphery
The spectrum information of the information such as gradient, or frequency domain；Pixel Information is input in pixel classifier 4022 and is classified,
Pixel is divided into the three types such as uniform formation's pixel, structure-pixel, noise pixel, the method for classification can be with the statistics of setting
Measure threshold value to classify, it is also possible to carry out according to the certain ratio value of statistic histogram.Embodiments of the invention are counting Nogata
Classifying, such as belly liver is imaged, most of pixel is hepatic tissue pixel to the ratio of figure, then set histogram peak and give
In the range of pixel be tissue pixels, the pixel outside region is noise pixel or structure-pixel.As shown in Fig. 2 brightness Data-Statistics
Histogram peak is Summit, and the pixel for being set lower than LowThr is noise, and the pixel higher than UpThr is structure-pixel,
Between LowThr and UpThr for uniform formation's pixel, wherein uniform formation accounts for the major part of the ratio of all pixels, this ratio
Example can offline be determined by the analysis to great amount of images data.Pixel classifier 4022 can arrange as follows：
Wherein, I (i, j) be current frame image pixel, i, j
For pixel coordinate point, NoiseSet, I (i, j) are noise, and TissueSet, I (i, j) are uniform formation's pixel,
StructureSet, I (i, j) are structure-pixel.
Certainly, the example of an above-mentioned only pixel classifier, when the present invention is realized, can include multiple graders
Compressive classification.
After the other classification of image pixel-class is carried out, we carry out region analysis with the result of classification, image point
Area's method can also have many kinds, and such as traditional partition method multirow in scan line depth is divided into a region, or is sweeping
Retouch multi-strip scanning on direction to be first divided into a region, or divide an image into multiple blocks, as shown in Figure 6.
Image dissector of the present invention 4023 adopts a kind of regional analysis of Pixel-level, in a neighborhood of current pixel
Inside it is analyzed, the classification situation of pixel in neighborhood is counted, if belonging to structure more than setting ratio pixel in pixel in neighborhood
Pixel, then current pixel is labeled as 3, else if noise pixel occupies ratio more than setting value in pixel in neighborhood, then currently
Pixel is labeled as 1, is otherwise labeled as 2；When being labeled as 2, the pixel organization brightness value is the flat of neighborhood inner tissue pixel
Average, during for being labeled as 1 or 3, neighborhood tissue intensity value is the average of the pixel surrounding pixel tissue intensity.Such as Fig. 7,
It can be seen that the neighborhood of neighbor is partly overlapping, therefore the result for calculating will not bring mutation and not connect as traditional partition method
Continuous phenomenon.
According to the output result of image dissector 4023, i.e., with 1, the mark image and neighborhood tissue average of 2,3 signs
Image is calculating gain compensation image and noise suppressed image.Its computational methods is as follows：
GainCompI (i, j)=TValue-MeanI (i, j),
Wherein, GainCompI (i, j) is gain compensation image, and Tvalue is that uniform formation compensates desired value, and MeanI (i, j) is pixel
Tissue intensity image；NoiseSupI (i, j) is noise suppressed image, and RI (i, j) is mark image, and SupressFactor is to set
Fixed inhibiting factor, i, j are pixel point coordinates.
After Optimal Parameters are calculated, in applying it to follow-up imaging, until Optimal Parameters are recalculated.Parameter
Application process is as follows：
OptI (i, j)=NoiseSupI (i, j) * [I (i, j)+GainCompI (i, j)], wherein, OptI (i, j) is excellent
Image after change, I (i, j) is image before optimization.
I (i, j)+GainCompI (i, j) is realized in gain compensator 403 in above-mentioned formula, NoiseSupI (i, j) *
[] is partly realized in noise suppressor 404.
As shown in figure 8, the present invention can also be placed on Automatic Optimal module 400 after scan conversion module 500 being located
Reason.
Claims (11)
1. a kind of ultrasonic image-forming system of Automatic Optimal, is characterized in that：Including：
The probe (100) of transmitting received ultrasonic signal；The ultrasonic echography echo-signal that probe (100) is received is entered into wave beam to close
It is shaped as the Beam synthesis module (200) of signal data；Signal transacting is carried out to signal data to obtain at the signal of ultrasonoscopy B
Reason module (300)；The Automatic Optimal module (400) of process is optimized to ultrasonoscopy B；Scan conversion module (500)；Image
Processing module (600)；And, display (700)；
The Automatic Optimal module (400) includes：
Image monitor (401), analyzes in real time the difference of present frame ultrasonoscopy and former frame ultrasonoscopy, and exports triggering letter
Number to parameter calculating module (402)；
Parameter calculating module (402), carries out the parameter for being calculated follow-up imaging of imaging parameters；
Gain compensator (403), according to the output result of parameter calculating module (402), calculates gain compensation image；And,
Noise suppressor (404), according to the output result of parameter calculating module (402), calculates noise suppressed image.
2. the ultrasonic image-forming system of Automatic Optimal as claimed in claim 1, is characterized in that：Described image watch-dog (401) is wrapped
Include：
Status register (4011), for preserving the image formation state of previous frame image；
Feature Calculator (4012), calculates features of ultrasound pattern value；
Status comparator (4013), obtains the characteristic value and and status register of the ultrasonoscopy of Feature Calculator (4012) input
(4011) currency in is compared；And,
Optimal Parameters calculate trigger (4014), for trigger parameter computing module (402).
3. the ultrasonic image-forming system of Automatic Optimal as claimed in claim 1, is characterized in that：The parameter calculating module (402)
Including：
Pixels statisticses device (4021), carries out current ultrasonoscopy statistics and obtains Pixel Information；
Pixel classifier (4022), Pixel Information is classified；And,
Image dissector (4023), using the result of classification region analysis are carried out, and analysis result is exported to gain compensation
Device (403) and noise suppressor (404).
4. the ultrasonic image-forming system of Automatic Optimal as claimed in claim 1, is characterized in that：The probe (100) connects wave beam
Synthesis module (200), output end connection signal processing module (300) of Beam synthesis module (200), signal processing module
(300) output end connection Automatic Optimal module (400), the output end connection scan conversion module of Automatic Optimal module (400)
(500), output end connection image processing module (600) of scan conversion module (500), the output of image processing module (600)
End connection display (700).
5. the ultrasonic image-forming system of Automatic Optimal as claimed in claim 1, is characterized in that：The probe (100) connects wave beam
Synthesis module (200), output end connection signal processing module (300) of Beam synthesis module (200), signal processing module
(300) output end connection scan conversion module (500), the output end connection Automatic Optimal module of scan conversion module (500)
(400), output end connection image processing module (600) of Automatic Optimal module (400), the output of image processing module (600)
End connection display (700).
6. the ultrasonic image-forming system of Automatic Optimal as claimed in claim 1, is characterized in that：Gain compensator (403) meter
Calculate gain compensation image, and noise suppressor (404) calculate noise suppressed image computational methods it is as follows：
GainCompI (i, j)=TValue-MeanI (i, j),Its
In, GainCompI (i, j) is gain compensation image, and Tvalue is that uniform formation compensates desired value, and MeanI (i, j) is pixel groups
Knit luminance picture；NoiseSupI (i, j) is noise suppressed image, and RI (i, j) is mark image, and SupressFactor is setting
Inhibiting factor, i, j be pixel point coordinates；
Optimal Parameters obtained above are applied in follow-up imaging, parameter application process is as follows：
OptI (i, j)=NoiseSupI (i, j) * [I (i, j)+GainCompI (i, j)], wherein, after OptI (i, j) is for optimization
Image, I (i, j) is image before optimization.
7. a kind of ultrasonic imaging method of Automatic Optimal, is characterized in that, comprise the following steps：
(1) the ultrasonic echography echo-signal that probe (100) is received shape after Beam synthesis module (200) carries out Beam synthesis
Into signal data, signal processing module (300) carries out obtaining ultrasonoscopy after signal transacting to signal data；
(2) ultrasonoscopy B1s of the ultrasonoscopy B Jing after Automatic Optimal module (400) process is optimized, specifically includes following step
Suddenly：
Ultrasonoscopy B after signal processing module (300) process is input to Automatic Optimal by a, first ultrasonic image-forming system
Module (400), the image monitor (401) in Automatic Optimal module (400) analyzes in real time present frame ultrasonoscopy and former frame
The difference of ultrasonoscopy, if difference exceedes the threshold values of setting, automatic running parameter calculating module (402) carries out imaging parameters
Calculate；
B, parameter calculating module (402) calculate the parameter for follow-up imaging；
C, according to the output result of parameter calculating module (402), gain compensator (403) calculates gain compensation image, noise suppression
Device (404) processed calculates noise suppressed image；Finally, Automatic Optimal module (400) exports the ultrasonoscopy B1 through Automatic Optimal；
(3) the ultrasonoscopy B1 after optimizing is processed by scan conversion module (500) and image processing module (600), is finally passed
It is defeated to carry out image on display (700) and show.
8. the ultrasonic imaging method of Automatic Optimal as claimed in claim 7, is characterized in that：Picture control in step (2) a
The course of work of device (401)：Status register (4011) preserves the image formation state of previous frame image, Feature Calculator (4012) meter
Features of ultrasound pattern value is calculated, status comparator (4013) obtains the characteristic value of the ultrasonoscopy of front end link input and deposits with state
Currency in reservoir (4011) is compared, if beyond the threshold value of setting, crossing Optimal Parameters and calculating trigger (4014)
Trigger parameter computing module (402).
9. the ultrasonic imaging method of Automatic Optimal as claimed in claim 7, is characterized in that：Parameter is calculated in step (3) b
The course of work of module (402)：Current ultrasonoscopy is input in pixels statisticses device (4021) and enters statistics, obtain pixel letter
Breath；Pixel Information is input in pixel classifier (4022) and is classified；After being classified, image dissector (4023) is adopted
Region analysis are carried out with the result of classification.
10. the ultrasonic imaging method of Automatic Optimal as claimed in claim 9, is characterized in that：Described image analyzer (4023)
The method for carrying out region analysis is：It is analyzed in a neighborhood of current pixel, counts the classification of pixel in neighborhood
Situation, the neighborhood of neighbor is partly overlapping.
The ultrasonic imaging method of 11. Automatic Optimals as claimed in claim 9, is characterized in that：According to image dissector (4023)
Output result, gain compensation image is calculated in the gain compensator (403), calculate noise suppressed in noise suppressor (404)
Image；Computational methods are as follows：
GainCompI (i, j)=TValue-MeanI (i, j),Its
In, GainCompI (i, j) is gain compensation image, and Tvalue is that uniform formation compensates desired value, and MeanI (i, j) is pixel groups
Knit luminance picture；NoiseSupI (i, j) is noise suppressed image, and RI (i, j) is mark image, and SupressFactor is setting
Inhibiting factor, i, j be pixel point coordinates；
Optimal Parameters obtained above are applied in follow-up imaging, parameter application process is as follows：
OptI (i, j)=NoiseSupI (i, j) * [I (i, j)+GainCompI (i, j)], wherein, after OptI (i, j) is for optimization
Image, I (i, j) is image before optimization.
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| CN113689424A (en) * | 2021-09-09 | 2021-11-23 | 中国人民解放军陆军军医大学 | Ultrasonic inspection system capable of automatically identifying image characteristics and identification method |
| CN114612462A (en) * | 2022-03-25 | 2022-06-10 | 成都爱迦飞诗特科技有限公司 | Breast ultrasound image acquisition method, device, equipment and storage medium |
| CN115713471A (en) * | 2022-11-23 | 2023-02-24 | 珠海视熙科技有限公司 | Image noise reduction method and device, storage medium and computer equipment |
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| CN115713471A (en) * | 2022-11-23 | 2023-02-24 | 珠海视熙科技有限公司 | Image noise reduction method and device, storage medium and computer equipment |
| CN115713471B (en) * | 2022-11-23 | 2023-08-29 | 珠海视熙科技有限公司 | Image noise reduction method and device, storage medium and computer equipment |
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