CN110148090A - A kind of Type B image gain automatic optimization method and device - Google Patents
A kind of Type B image gain automatic optimization method and device Download PDFInfo
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T2207/10—Image acquisition modality
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Abstract
The present invention provides a kind of Type B image gain automatic optimization method and device, include the following steps: S1: judging picture noise region and non-noise region, S2: image organizational region is obtained, S3: image gain compensated curve calculates, S4: image gain optimization improves robustness, the principle of the invention is simple, computation complexity is low, can be for the vertical and horizontal gain compensation of the adaptive calculating image of Different Individual different parts.
Description
Technical field
The present invention is a kind of Type B image gain automatic optimization method and device, belongs to medical field.
Background technique
In the prior art, with the continuous development of medical technology and medical diagnosis means, ultrasonic imaging technique is examined in clinic
The fields such as disconnected and medical research are widely used.
Common ultrasonic imaging Type B system process flow includes emission control module according to instruction incentive probe, electric signal
It is converted into acoustical signal.Sound wave is propagated and is emitted in tissue, and probe receives the ultrasound echo signal reflected through tissue,
Electric signal is converted by acoustical signal.In analog signal processing module by amplification, filtering, time gain compensation TGC (time
Gain compensation, TGC) and Analog-digital Converter (ADC) etc. analog echo signal is converted into digital echo signal.
Since energy can generate decaying to sound wave in transmitting, receive process, handled if directlying adopt this echo-signal, in difference
The brightness performance of depth image is not consistent, brings difficulty to user's diagnosis, therefore usually will do it time gain in front end and mend
Repay, due to be analog end processing also cry simulated time gain compensation (analog time gain compensation,
ATGC), the multi-channel A/D signal received is obtained a RF signal by processing such as delay accumulation focusing by beam synthesizer.
After obtaining entire image RF data, data include that IQ is demodulated, low-pass filtering drop is adopted by signal processing module
Sample, log-compressed, log-compressed data at this time do not meet eye-observation habit, need to use in depth direction brightness irregularities
Family manually adjusts potentiometer, adjusts the gain under different depth, guarantees that brightness of image is consistent.The signal handled at this time is digital letter
Number, therefore it is also digit time gain compensation (digital time gain compensation, DTGC), while needing hand
Dynamic adjusting knob, that is, entire gain (global gain), adjusts suitable brightness of image.
Image after gain adjustment is admitted to image processing module, which includes dynamic range adjustment, image increasing
By force, digital scan conversion etc. is ultimately sent to display and is shown.
Gain adjustment has great importance for clinical diagnosis in ultrasonic image-forming system, not by gain adjustment
Brightness of image shows unevenly on ultrasonic device, and ultrasound producer has preset gain when leaving the factory at present, but user is to trouble
When person carries out disease examination, due to the attenuation characteristic of ultrasonic wave, the acoustic impedance of different human body is also variant, the gain of factory pre-sets
All demands are not able to satisfy, at this moment need user manually adjusts DTGC, global gain can be only achieved as a result, to using
Family use brings burden, can not also adjust out preferable result sometimes.
Be born a kind of gain optimization method regarding to the issue above, and this method calculates one by analysis image data
On-axis gain compensated curve keeps brightness of image performance uniform by compensating to on-axis gain.
Summary of the invention
In view of the deficienciess of the prior art, it is an object of the present invention to provide a kind of Type B image gain automatic optimization method and
Device, to solve the problems mentioned in the above background technology, the present invention are improved using the judgment method of characteristic value in structure tensor
Robustness.
To achieve the goals above, the present invention is to realize by the following technical solutions: a kind of Type B image gain is automatic
Optimization method includes the following steps:
S1: judge picture noise region and non-noise region:
Ultrasound log-compressed image I (M*N, M are picture depth, and N is picture traverse) is obtained to be sentenced according to ultrasonic noise data
Disconnected picture noise region and non-noise region；
S2: image organizational region is obtained:
The structure tensor of two dimensional image is symmetrical and positive semi-definite two-dimensional matrix, therefore there are two eigenvalue λs by I1、λ2, respectively
The minimum and maximum characteristic value of representative image pixel:
Work as λ1≈λ2When ≈ 0, it is smaller to indicate that pixel gray value changes in this contiguous range, pixel is located at flat region
Domain；
Work as λ1> > λ2When ≈ 0, indicate that upper gray-value variation is strong in a certain direction, pixel is located at edge；
Work as λ1≥λ2When > 0, indicate that there are very strong variations, pixel to be located at angle point for feature vector in the direction indicated；Benefit
With the form of expression of features described above value, a reliability function P is constructed, can be used to judge the tissue regions in ultrasound image；
The specific method is as follows:
A) it to accelerate calculating, carries out lateral, longitudinal smooth to I respectively while carrying out down-sampled processing, specific method is pair
Image I carries out 2-d gaussian filters, while extracting odd-numbered line, odd column, obtains image I '；
B) image I ' structure tensor S (I) is calculated
Wherein* convolution is represented, δ is variance, 0.5 is taken,
The horizontal and vertical gradient of image I ', I are calculated firstx、Iy, utilize Ix、IyStructure tensor is calculated, then to structure
Amount does dimensional Gaussian smoothing processing, and in order to accelerate to calculate, dimensional Gaussian smoothing processing can be converted into an one-dimensional longitudinal Gauss
Smooth and one-dimensional lateral Gaussian smoothing；
C) image I ' characteristic value calculates
D) image organizational region TisReg judges
According to the form of expression of features described above value, constructor P:
Or
P ∈ (0,1), λ1、λ2Belong to non-noise region
P takes the second way, λ in the present invention1、λ2Belong to non-noise region:
Work as λ1≈λ2When ≈ 0, for P close to 0, pixel is located at flat site, it is believed that is tissue regions；
Work as λ1> > λ2≈ 0 or λ1≥λ2When > 0, it is believed that be non-tissue regions；
The threshold value TisThr for judging tissue regions is provided here, as P < TisThr, it is believed that it is tissue regions, this
When TisReg be 1, remaining be non-tissue regions, TisReg 0；
S3: image gain compensated curve calculates
Judge whether to participate in axial average signal AxisMean, transverse direction average signal LateralMean calculating, it may be assumed that
TisPreThr can value according to the actual situation, take 0.9 in this scheme, when L takes N, calculate axial average letter
Number AxisMean calculates lateral average signal LateralMean when L takes M, to 0 point using adjacent average signal interpolation at
Reason；Final on-axis gain compensated curve GainAxis are as follows: GainAxis=DstGain-xisMean, DstGain are that expectation increases
Benefit,
Since during longitudinally adjusted, each row has carried out the gain compensation of GainAxis, so wanting on this basis
Reach the target that brightness after adjusting is DstGain, lateral gain compensation curve GainLateral are as follows:
GainLateral=LateralMean- ∑ LateralMean
Up-sampling treatment is carried out to GainAxis and GainLateral, obtains final gain compensating curve；
S4: image gain optimization
According to formula Iout=I+GainAxis+GainLateral, transverse direction and longitudinal direction gain optimization is carried out to image,
Iout is image after gain optimization.
Further: picture noise region and non-noise region should be distinguished, obtains noise data especially by closing to emit,
It is laterally being averaged the longitudinal noise curve 1-M of acquisition one, Repeated m time takes its average value, for the response time for reducing system,
Noise curve prestores hereof, and when every suboptimization directly uses noise curve data processing, makes an uproar when log-compressed data are less than
When acoustic curve data, it is believed that be herein non-noise region, be otherwise noise region.
A kind of Type B image gain device, including probe, emission control module, reception control module, analog signal processing mould
Block, beam synthesizer, signal processing module, Type B image gain Automatic Optimal module, image processing module, display module, specifically
For emission control module according to instruction incentive probe, electric signal switch to acoustical signal, probe receives the ultrasound through Tissue reflectance and returns
Acoustical signal is converted electric signal by wave signal, in analog signal processing module by amplification, filtering, time gain compensation and mould
Analog echo signal is converted to digital echo signal by quasi- number conversion (ADC) etc.；
The multi-channel A/D signal received is obtained a RF signal by processing such as delay accumulation focusing by beam synthesizer；
After obtaining RF data, system includes that IQ is demodulated, low-pass filtering is down-sampled, logarithm pressure by signal processing module
Data are finally sent into Type B image gain Automatic Optimal module by contracting, and Automatic Optimal module is quick by the way that one is arranged on keyboard
Button is started, and is calculated suitable gain adjustment curve by the device when starting the device, is increased to Type B image
Benefit is adjusted, and is adjusted when closing the gain curve for changing button Shi Zeyong system default；
Image after gain adjustment is admitted to image processing module, which includes dynamic range adjustment, image increasing
By force, digital scan conversion etc. is ultimately sent to display and is shown.
Type B image gain Automatic Optimal module, including data memory module, image gain curve computing module, image increase
Beneficial curve output module, image gain optimization processing module: data memory module saves image gain curve computing module and needs
The parameter used, including noise curve data and tissue regions judgment threshold facilitate meter to reduce the response time of system
It calculates, noise data has calculated before image gain optimization and completed and be stored in data memory module, calculates mould in image gain
It is directly used in block, the value in data memory module can modify according to the actual situation；Image gain curve computing module
Noise curve data, the image data in data memory module are received, is calculated by above-mentioned Automatic Optimal gain method lateral, vertical
To gain compensating curve, compensated curve is finally input to image gain optimization processing module；Image gain optimization processing module
Reception gain compensated curve and image data carry out gain optimization processing to original digital image data according to image optimization gain method,
Image after finally output optimization, image data includes fundamental wave or harmonic data under ultrasonic B-mode.
Beneficial effects of the present invention: a kind of Type B image gain automatic optimization method of the invention and device, the present invention use
The judgment method of characteristic value in structure tensor, improves robustness, and the principle of the invention is simple, and computation complexity is low, can be directed to
The vertical and horizontal gain compensation of the adaptive calculating image of Different Individual different parts.
Detailed description of the invention
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, other feature of the invention,
Objects and advantages will become more apparent upon:
Fig. 1 is Type B image gain Parameter Optimization System structure chart in a kind of Type B image gain device of the present invention；
Fig. 2 is Type B gain parameter optimized flow chart in a kind of Type B image gain automatic optimization method of the present invention；
Fig. 3 is Type B image gain Parameter Optimization System structure chart in a kind of Type B image gain device of the present invention；
Fig. 4 is that tissue regions differentiate flow chart in a kind of Type B image gain automatic optimization method of the present invention；
Specific embodiment
To be easy to understand the technical means, the creative features, the aims and the efficiencies achieved by the present invention, below with reference to
Specific embodiment, the present invention is further explained.
- Fig. 4 refering to fig. 1, the present invention provide a kind of technical solution: a kind of Type B image gain automatic optimization method, including such as
Lower step:
S1: judge picture noise region and non-noise region:
Ultrasound log-compressed image I (M*N, M are picture depth, and N is picture traverse) is obtained to be sentenced according to ultrasonic noise data
Disconnected picture noise region and non-noise region；
S2: image organizational region is obtained:
The structure tensor of two dimensional image is symmetrical and positive semi-definite two-dimensional matrix, therefore there are two eigenvalue λs by I1、λ2, respectively
The minimum and maximum characteristic value of representative image pixel:
Work as λ1≈λ2When ≈ 0, it is smaller to indicate that pixel gray value changes in this contiguous range, pixel is located at flat region
Domain；
Work as λ1> > λ2When ≈ 0, indicate that upper gray-value variation is strong in a certain direction, pixel is located at edge；
Work as λ1≥λ2When > 0, indicate that there are very strong variations, pixel to be located at angle point for feature vector in the direction indicated；Benefit
With the form of expression of features described above value, a reliability function P is constructed, can be used to judge the tissue regions in ultrasound image；
The specific method is as follows:
A) it to accelerate calculating, carries out lateral, longitudinal smooth to I respectively while carrying out down-sampled processing, specific method is pair
Image I carries out 2-d gaussian filters, while extracting odd-numbered line, odd column, obtains image I '；
B) image I ' structure tensor S (I) is calculated
Wherein* convolution is represented, δ is variance, 0.5 is taken,
The horizontal and vertical gradient of image I ', I are calculated firstx、Iy, utilize Ix、IyStructure tensor is calculated, then to structure
Amount does dimensional Gaussian smoothing processing, and in order to accelerate to calculate, dimensional Gaussian smoothing processing can be converted into an one-dimensional longitudinal Gauss
Smooth and one-dimensional lateral Gaussian smoothing；
C) image I ' characteristic value calculates
D) image organizational region TisReg judges
According to the form of expression of features described above value, constructor P:
Or
P ∈ (0,1), λ1、λ2Belong to non-noise region
P takes the second way, λ in the present invention1、λ2Belong to non-noise region:
Work as λ1≈λ2When ≈ 0, for P close to 0, pixel is located at flat site, it is believed that is tissue regions；
Work as λ1> > λ2≈ 0 or λ1≥λ2When > 0, it is believed that be non-tissue regions；
The threshold value TisThr for judging tissue regions is provided here, as P < TisThr, it is believed that it is tissue regions, this
When TisReg be 1, remaining be non-tissue regions, TisReg 0；
S3: image gain compensated curve calculates
Judge whether to participate in axial average signal AxisMean, transverse direction average signal LateralMean calculating, it may be assumed that
TisPreThr can value according to the actual situation, take 0.9 in this scheme, when L takes N, calculate axial average letter
Number AxisMean calculates lateral average signal LateralMean when L takes M, to 0 point using adjacent average signal interpolation at
Reason；Final on-axis gain compensated curve GainAxis are as follows: GainAxis=DstGain-xisMean, DstGain are that expectation increases
Benefit
Since during longitudinally adjusted, each row has carried out the gain compensation of GainAxis, so wanting on this basis
Reach the target that brightness after adjusting is DstGain, lateral gain compensation curve GainLateral are as follows:
GainLateral=LateralMean- ∑ LateralMean
Up-sampling treatment is carried out to GainAxis and GainLateral, obtains final gain compensating curve；
S4: image gain optimization
According to formula Iout=I+GainAxis+GainLateral, transverse direction and longitudinal direction gain optimization is carried out to image,
Iout is image after gain optimization.
Further: picture noise region and non-noise region should be distinguished, obtains noise data especially by closing to emit,
It is laterally being averaged the longitudinal noise curve 1-M of acquisition one, Repeated m time takes its average value, for the response time for reducing system,
Noise curve prestores hereof, and when every suboptimization directly uses noise curve data processing, makes an uproar when log-compressed data are less than
When acoustic curve data, it is believed that be herein non-noise region, be otherwise noise region.
A kind of Type B image gain device, including probe, emission control module, reception control module, analog signal processing mould
Block, beam synthesizer, signal processing module, Type B image gain Automatic Optimal module, image processing module, display module, specifically
For emission control module according to instruction incentive probe, electric signal switch to acoustical signal, probe receives the ultrasound through Tissue reflectance and returns
Acoustical signal is converted electric signal by wave signal, in analog signal processing module by amplification, filtering, time gain compensation and mould
Analog echo signal is converted to digital echo signal by quasi- number conversion (ADC) etc.；
The multi-channel A/D signal received is obtained a RF signal by processing such as delay accumulation focusing by beam synthesizer；
After obtaining RF data, system includes that IQ is demodulated, low-pass filtering is down-sampled, logarithm pressure by signal processing module
Data are finally sent into Type B image gain Automatic Optimal module by contracting, and Automatic Optimal module is quick by the way that one is arranged on keyboard
Button is started, and is calculated suitable gain adjustment curve by the device when starting the device, is increased to Type B image
Benefit is adjusted, and is adjusted when closing the gain curve for changing button Shi Zeyong system default；
Image after gain adjustment is admitted to image processing module, which includes dynamic range adjustment, image increasing
By force, digital scan conversion etc. is ultimately sent to display and is shown.
Type B image gain Automatic Optimal module, including data memory module, image gain curve computing module, image increase
Beneficial curve output module, image gain optimization processing module: data memory module saves image gain curve computing module and needs
The parameter used, including noise curve data and tissue regions judgment threshold facilitate meter to reduce the response time of system
It calculates, noise data has calculated before image gain optimization and completed and be stored in data memory module, calculates mould in image gain
It is directly used in block, the value in data memory module can modify according to the actual situation；Image gain curve computing module
Noise curve data, the image data in data memory module are received, is calculated by above-mentioned Automatic Optimal gain method lateral, vertical
To gain compensating curve, compensated curve is finally input to image gain optimization processing module；Image gain optimization processing module
Reception gain compensated curve and image data carry out gain optimization processing to original digital image data according to image optimization gain method,
Image after finally output optimization, image data includes fundamental wave or harmonic data under ultrasonic B-mode.
Embodiment: S1: judge picture noise region and non-noise region:
Ultrasound log-compressed image I (M*N, M are picture depth, and N is picture traverse) is obtained to be sentenced according to ultrasonic noise data
Disconnected picture noise region and non-noise region；
S2: image organizational region is obtained:
The structure tensor of two dimensional image is symmetrical and positive semi-definite two-dimensional matrix, therefore there are two eigenvalue λs 1, λ 2 by I, divide
The other minimum and maximum characteristic value of representative image pixel:
Work as λ1≈λ2When ≈ 0, it is smaller to indicate that pixel gray value changes in this contiguous range, pixel is located at flat region
Domain；
Work as λ1> > λ2When ≈ 0, indicate that upper gray-value variation is strong in a certain direction, pixel is located at edge；
Work as λ1≥λ2When > 0, indicate that there are very strong variations, pixel to be located at angle point for feature vector in the direction indicated；Benefit
With the form of expression of features described above value, a reliability function P is constructed, can be used to judge the tissue regions in ultrasound image；
The specific method is as follows:
A) it to accelerate calculating, carries out lateral, longitudinal smooth to I respectively while carrying out down-sampled processing, specific method is pair
Image I carries out 2-d gaussian filters, while extracting odd-numbered line, odd column, obtains image I '；
B) image I ' structure tensor S (I) is calculated
Wherein* convolution is represented, δ is variance, 0.5 is taken,
The horizontal and vertical gradient of image I ', I are calculated firstx、Iy, utilize Ix、IyStructure tensor is calculated, then to structure
Amount does dimensional Gaussian smoothing processing, and in order to accelerate to calculate, dimensional Gaussian smoothing processing can be converted into an one-dimensional longitudinal Gauss
Smooth and one-dimensional lateral Gaussian smoothing；
C) image I ' characteristic value calculates
D) image organizational region TisReg judges
According to the form of expression of features described above value, constructor P:
Or
P ∈ (0,1), λ1、λ2Belong to non-noise region
P takes the second way, λ in the present invention1、λ2Belong to non-noise region:
Work as λ1≈λ2When ≈ 0, for P close to 0, pixel is located at flat site, it is believed that is tissue regions；
Work as λ1> > λ2≈ 0 or λ1≥λ2When > 0, it is believed that be non-tissue regions；
The threshold value TisThr for judging tissue regions is provided here, as P < TisThr, it is believed that it is tissue regions, this
When TisReg be 1, remaining be non-tissue regions, TisReg 0；
S3: image gain compensated curve calculates
Judge whether to participate in axial average signal AxisMean, transverse direction average signal LateralMean calculating, it may be assumed that
TisPreThr can value according to the actual situation, take 0.9 in this scheme, when L takes N, calculate axial average letter
Number AxisMean calculates lateral average signal LateralMean when L takes M, to 0 point using adjacent average signal interpolation at
Reason；Final on-axis gain compensated curve GainAxis are as follows: GainAxis=DstGain-xisMean, DstGain are that expectation increases
Benefit
Since during longitudinally adjusted, each row has carried out the gain compensation of GainAxis, so wanting on this basis
Reach the target that brightness after adjusting is DstGain, lateral gain compensation curve GainLateral are as follows:
GainLateral=LateralMean- ∑ LateralMean
Up-sampling treatment is carried out to GainAxis and GainLateral, obtains final gain compensating curve；
S4: image gain optimization
According to formula Iout=I+GainAxis+GainLateral, transverse direction and longitudinal direction gain optimization is carried out to image,
Iout is image after gain optimization.
The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention, for this field skill
For art personnel, it is clear that invention is not limited to the details of the above exemplary embodiments, and without departing substantially from spirit of the invention or
In the case where essential characteristic, the present invention can be realized in other specific forms.Therefore, in all respects, should all incite somebody to action
Embodiment regards exemplary as, and is non-limiting, the scope of the present invention by appended claims rather than on state
Bright restriction, it is intended that including all changes that fall within the meaning and scope of the equivalent elements of the claims in the present invention
It is interior.Claim should not be construed as limiting the claims involved.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiments being understood that.
Claims (4)
1. a kind of Type B image gain automatic optimization method, it is characterised in that include the following steps:
S1: judge picture noise region and non-noise region:
It obtains ultrasound log-compressed image I (M*N, M are picture depth, and N is picture traverse) and judges to scheme according to ultrasonic noise data
As noise region and non-noise region；
S2: image organizational region is obtained:
The structure tensor of two dimensional image is symmetrical and positive semi-definite two-dimensional matrix, therefore there are two eigenvalue λs 1, λ by I2, respectively represent
The minimum and maximum characteristic value of image slices vegetarian refreshments:
Work as λ1≈λ2When ≈ 0, it is smaller to indicate that pixel gray value changes in this contiguous range, pixel is located at flat site；
Work as λ1> > λ2When ≈ 0, indicate that upper gray-value variation is strong in a certain direction, pixel is located at edge；
Work as λ1≥λ2When > 0, indicate that there are very strong variations, pixel to be located at angle point for feature vector in the direction indicated；Using upper
The form of expression for stating characteristic value constructs a reliability function P, can be used to judge the tissue regions in ultrasound image；
The specific method is as follows:
A) it to accelerate calculating, carries out lateral, longitudinal smooth to I respectively while carrying out down-sampled processing, specific method is to image I
2-d gaussian filters are carried out, while extracting odd-numbered line, odd column, obtain image I '；
B) image I ' structure tensor S (I) is calculated
Wherein* convolution is represented, δ is variance, 0.5 is taken,
The horizontal and vertical gradient of image I ', I are calculated firstx、Iy, utilize Ix、IyStructure tensor is calculated, two then are done to structure tensor
Tie up Gaussian smoothing, in order to accelerate to calculate, dimensional Gaussian smoothing processing can be converted into an one-dimensional longitudinal Gaussian smoothing and
One-dimensional transverse direction Gaussian smoothing；
C) image I ' characteristic value calculates
D) image organizational region TisReg judges
According to the form of expression of features described above value, constructor P:
Or
λ1、λ2Belong to non-noise region
P takes the second way, λ in the present invention1、λ2Belong to non-noise region:
Work as λ1≈λ2When ≈ 0, for P close to 0, pixel is located at flat site, it is believed that is tissue regions；
Work as λ1> > λ2≈ 0 or λ1≥λ2When > 0, it is believed that be non-tissue regions；
The threshold value TisThr for judging tissue regions is provided here, as P < TisThr, it is believed that be tissue regions, at this time
TisReg is 1, remaining is non-tissue regions, TisReg 0；
S3: image gain compensated curve calculates
Judge whether to participate in axial average signal AxisMean, transverse direction average signal LateralMean calculating, it may be assumed that
TisPreThr can value according to the actual situation, take 0.9 in this scheme, when L takes N, calculate axial average signal
AxisMean calculates lateral average signal LateralMean when L takes M, to 0 point using adjacent average signal interpolation processing；
Final on-axis gain compensated curve GainAxis are as follows: GainAxis=DstGain-xisMean, DstGain are expected gain；
Since during longitudinally adjusted, each row has carried out the gain compensation of GainAxis, so to reach on this basis
Brightness is the target of DstGain, lateral gain compensation curve GainLateral after adjustment are as follows:
GainLateral=LateralMean- ∑ LateralMean
Up-sampling treatment is carried out to GainAxis and GainLateral, obtains final gain compensating curve；
S4: image gain optimization
According to formula Iout=I+GainAxis+GainLateral, transverse direction and longitudinal direction gain optimization is carried out to image, Iout is
Image after gain optimization.
2. a kind of Type B image gain automatic optimization method according to claim 1, it is characterised in that: image should be distinguished and made an uproar
Sound area domain and non-noise region obtain noise data especially by closing to emit, and make an uproar being laterally averaged one longitudinal direction of acquisition
Acoustic curve 1-M, Repeated m time take its average value, and for the response time for reducing system, noise curve is prestored hereof, every suboptimum
Noise curve data processing is directly used when change, when log-compressed data are less than noise curve data herein, it is believed that make an uproar to be non-
Otherwise sound area domain is noise region.
3. a kind of Type B image gain device, it is characterised in that: including probe, emission control module, receive control module, simulation
Signal processing module, signal processing module, Type B image gain Automatic Optimal module, image processing module, is shown beam synthesizer
Show module, specifically for emission control module according to instruction incentive probe, electric signal switchs to acoustical signal, and probe is received through organizing instead
Acoustical signal is converted electric signal by the ultrasound echo signal penetrated, and increases in analog signal processing module by amplification, filtering, time
Analog echo signal is converted to digital echo signal by benefit compensation and Analog-digital Converter (ADC) etc.；
The multi-channel A/D signal received is obtained a RF signal by processing such as delay accumulation focusing by beam synthesizer；
After obtaining RF data, system by signal processing module include IQ demodulation, low-pass filtering is down-sampled, log-compressed most
Data are sent into Type B image gain Automatic Optimal module afterwards, Automatic Optimal module on keyboard by being arranged a quick botton
Started, suitable gain adjustment curve is calculated by the device when starting the device, gain tune is carried out to Type B image
Section is adjusted when closing the gain curve for changing button Shi Zeyong system default；
Image after gain adjustment is admitted to image processing module, which includes dynamic range adjustment, image enhancement, number
Word scan conversion etc. is ultimately sent to display and is shown.
4. Type B image gain device according to claim 3, it is characterised in that: the Type B image gain Automatic Optimal mould
Block includes data memory module, image gain curve computing module, image gain curve output module, image gain optimization processing
Module: data memory module saves the parameter that image gain curve computing module needs to use, including noise curve data and group
Tissue region judgment threshold facilitates calculating to reduce the response time of system, and noise data has been counted before image gain optimization
It calculates and completes and be stored in data memory module, directly used in image gain computing module, the value in data memory module can
To modify according to the actual situation；Image gain curve computing module receive data memory module in noise curve data,
Image data calculates lateral, longitudinal gain compensating curve by above-mentioned Automatic Optimal gain method, finally inputs compensated curve
To image gain optimization processing module；Image gain optimization processing module reception gain compensated curve and image data, according to figure
As optimized gain method, gain optimization processing is carried out to original digital image data, finally exports the image after optimization, image data includes
Fundamental wave or harmonic data under ultrasonic B-mode.
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