CN110148090B - Automatic optimization method and device for B-type image gain - Google Patents
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Abstract
The invention provides a method and a device for automatically optimizing B-type image gain, which comprises the following steps: s1: judging a noise area and a non-noise area of the image, S2: acquiring an image tissue region, S3: calculating an image gain compensation curve, S4: the method has the advantages of simple principle, low calculation complexity and capability of adaptively calculating the longitudinal and transverse gain compensation of the image aiming at different individuals and different positions.
Description
Technical Field
The invention discloses a method and a device for automatically optimizing B-type image gain, and belongs to the field of medical treatment.
Background
In the prior art, with the continuous development of medical technology and medical diagnosis means, the ultrasonic imaging technology is widely applied in the fields of clinical diagnosis, medical scientific research and the like.
The processing flow of the common ultrasonic imaging B-type system comprises that an emission control module excites a probe according to an instruction, and an electric signal is converted into an acoustic signal. The sound wave is transmitted in the human tissue, the probe receives the ultrasonic echo signal reflected by the human tissue, and the sound signal is converted into an electric signal. The analog echo signals are converted into digital echo signals by an analog signal processing module through amplification, filtering, time Gain Compensation (TGC), analog-to-digital conversion (ADC), and the like. Because the energy of the sound wave can be attenuated in the transmitting and receiving processes, if the echo signal is directly used for processing, the brightness of images at different depths is not consistent, and the diagnosis of a user is difficult, time gain compensation is usually performed at the front end, because Analog Time Gain Compensation (ATGC) is processed at an analog end, a beam synthesizer carries out processing such as delay accumulation focusing on the received multiple AD signals to obtain an RF signal.
After the RF data of the whole image is obtained, the data is subjected to IQ demodulation, low-pass filtering down-sampling and logarithmic compression through a signal processing module, the brightness of the logarithmic compression data at the moment in the depth direction is not uniform, the human eye observation habit is not met, a user needs to manually adjust a potentiometer, the gains at different depths are adjusted, and the image brightness is ensured to be consistent. The signal processed at this time is a digital signal, so called Digital Time Gain Compensation (DTGC), and a knob, i.e. a global gain, needs to be manually adjusted to adjust the proper image brightness.
The gain-adjusted image is sent to an image processing module, which includes dynamic range adjustment, image enhancement, digital scan conversion, etc., and is finally sent to a display for display.
Gain adjustment in an ultrasonic imaging system has important significance for clinical diagnosis, image brightness without gain adjustment is not uniform on ultrasonic equipment, currently, an ultrasonic manufacturer presets gain when leaving a factory, but when a user carries out disease examination on a patient, acoustic impedances of different human bodies are different due to attenuation characteristics of ultrasonic waves, the gain preset by the manufacturer cannot meet all requirements, good results can be achieved only by manually adjusting DTGC and global gain by the user, burden is brought to the user, and sometimes better results cannot be adjusted.
A gain optimization method is created for solving the problems, and the method calculates an axial gain compensation curve by analyzing image data and compensates the axial gain to ensure that the image brightness is uniformly expressed
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method and a device for automatically optimizing the gain of a B-type image so as to solve the problems in the background technology.
In order to achieve the purpose, the invention is realized by the following technical scheme: a B-type image gain automatic optimization method comprises the following steps:
s1: judging a noise area and a non-noise area of the image:
acquiring an ultrasonic logarithmic compression image I, wherein the size of the image is M x N, M is the image depth, and N is the image width, and judging an image noise area and a non-noise area according to ultrasonic noise data;
s2: acquiring an image tissue region:
the structure tensor of the two-dimensional image is a symmetric and semi-positive two-dimensional matrix, so that I has two eigenvalues λ 1 、λ 2 Respectively representing the maximum and minimum characteristic values of the image pixel points:
when lambda is 1 ≈λ 2 When the gray value of the pixel point is approximately equal to 0, the gray value of the pixel point is less changed in the neighborhood range, and the pixel point is located in a flat area;
when lambda is 1 ＞＞λ 2 When the value is approximately equal to 0, the gray value change in a certain direction is strong, and the pixel point is positioned at the edge;
when lambda is 1 ≥λ 2 >When 0, the characteristic vector in the specified direction has strong change, and the pixel point is positioned at the corner point; constructing a reliable function P by using the expression form of the characteristic value, wherein the reliable function P can be used for judging the tissue area in the ultrasonic image;
the specific method comprises the following steps:
a) In order to accelerate the calculation, respectively carrying out horizontal and longitudinal smoothing on the I and simultaneously carrying out down-sampling processing, and specifically, carrying out two-dimensional Gaussian filtering on the image I, and simultaneously extracting odd rows and odd columns to obtain an image I';
b) Image I' structure tensor S (I) computation
Wherein
* Represents convolution, delta is the variance, 0.5 is taken,
first, the transverse and longitudinal gradients, I, of the image I' are calculated x 、I y By means of I x 、I y Calculating a structure tensor, and then performing two-dimensional Gaussian smoothing on the structure tensor, wherein in order to accelerate calculation, the two-dimensional Gaussian smoothing can be converted into one-dimensional longitudinal Gaussian smoothing and one-dimensional transverse Gaussian smoothing;
c) Image I' feature value calculation
d) Image tissue region TisReg judgment
According to the expression form of the characteristic value, constructing a function P:
or
In the present application P takes the second form, λ 1 、λ 2 Belonging to a non-noise region:
when lambda is 1 ≈λ 2 When the value is approximately equal to 0, P is close to 0, and the pixel point is located in a flat area and is regarded as an organization area;
when lambda is 1 ＞＞λ 2 0 or lambda 1 ≥λ 2 >0, considered to be a non-tissue region;
providing a threshold TisThr for judging the tissue region, and when P < TisThr, considering the tissue region as the tissue region, wherein TisReg is 1, the rest are non-tissue regions, and TisReg is 0;
s3: calculating an image gain compensation curve:
judging whether to participate in the calculation of the axial average signal Axismean and the transverse average signal Lateralmean, namely:
TisPreThr is a threshold value which is set according to experience and used for judging whether the tissue is uniform or non-uniform, 0.9 is taken in the scheme, when L is taken as N, an axial average signal Axismean is calculated, when L is taken as M, a transverse average signal Lateralmean is calculated, and adjacent average signal interpolation processing is adopted for 0 point; the final axial gain compensation curve, gainAxis, is: gainAxis = DstGain-AxisMean, dstGain being the desired gain;
since the gain compensation of the GainAxis is performed for each row during the longitudinal adjustment process, in order to achieve the target of the adjusted brightness DstGain on the basis, the lateral gain compensation curve GainLateral is:
carrying out up-sampling treatment on GainAxis and GainLateral to obtain a final gain compensation curve;
s4: optimizing image gain:
and performing transverse and longitudinal gain optimization on the image according to a formula Iout = I + GainAxis + GainLateral, wherein Iout is the image after gain optimization.
Further: the image noise area and the non-noise area are distinguished, noise data are obtained by closing emission, a longitudinal noise curve 1. M is obtained by transversely taking an average value, the average value is repeatedly taken for M times, in order to reduce the response time of the system, the noise curve is prestored in a file, the noise curve data is directly used for processing during each optimization, when the logarithm compression data is smaller than the noise curve data, the image noise area and the non-noise area are considered, and otherwise, the image noise area is the noise area.
A B-mode image gain device using the method of any one of claims 1-2, wherein: the ultrasonic echo signal processing device comprises a probe, a transmitting control module, a receiving control module, an analog signal processing module, a beam synthesizer, a signal processing module, a B-type image gain automatic optimization module, an image processing module and a display module, wherein the transmitting control module excites the probe according to an instruction, an electric signal is converted into an acoustic signal, the probe receives an ultrasonic echo signal reflected by tissue and converts the acoustic signal into the electric signal, and the analog echo signal is converted into a digital echo signal by the analog signal processing module through amplification, filtering, time gain compensation, analog-to-digital conversion (ADC) and the like;
the wave beam synthesizer carries out processing such as delay accumulation focusing on the received multiple AD signals to obtain an RF signal;
after RF data are obtained, the system sends the data into a B-type image gain automatic optimization module through a signal processing module including IQ demodulation, low-pass filtering down-sampling and logarithmic compression, the automatic optimization module is started by setting a shortcut button on a keyboard, when the device is started, a proper gain adjustment curve is calculated through the device, the gain of the B-type image is adjusted, and when a change button is closed, the default gain curve of the system is used for adjustment;
the gain-adjusted image is sent to an image processing module, which includes dynamic range adjustment, image enhancement, digital scan conversion, etc., and is finally sent to a display for display.
The B-type image gain automatic optimization module comprises a data storage module, an image gain curve calculation module, an image gain curve output module and an image gain optimization processing module: the data storage module stores parameters required by the image gain curve calculation module, including noise curve data and a tissue region judgment threshold, in order to reduce the response time of the system and facilitate calculation, the noise data is calculated and stored in the data storage module before the image gain is optimized and is directly used in the image gain calculation module, and the values in the data storage module can be modified according to the actual situation; the image gain curve calculation module receives the noise curve data and the image data in the data storage module, calculates the transverse and longitudinal gain compensation curves by the automatic gain optimization method, and finally inputs the compensation curves into the image gain optimization processing module; and the image gain optimization processing module receives the gain compensation curve and the image data, performs gain optimization processing on the original image data according to an image gain optimization method, and finally outputs an optimized image, wherein the image data comprises fundamental wave or harmonic wave data in an ultrasonic B mode.
The invention has the beneficial effects that: the invention discloses a method and a device for automatically optimizing B-type image gain, which adopt a method for judging eigenvalues in structure tensor, improve robustness, have simple principle and low calculation complexity, and can adaptively calculate longitudinal and transverse gain compensation of images aiming at different parts of different individuals.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a diagram of a B-mode image gain parameter optimization system in a B-mode image gain device according to the present invention;
FIG. 2 is a flow chart of the optimization of B-mode gain parameters in the automatic B-mode image gain optimization method of the present invention;
FIG. 3 is a diagram of a system for optimizing B-mode image gain parameters in a B-mode image gain apparatus according to the present invention;
FIG. 4 is a flow chart of tissue region discrimination in an automatic B-mode image gain optimization method according to the present invention;
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
Referring to fig. 1 to 4, the present invention provides a technical solution: a B-type image gain automatic optimization method comprises the following steps:
s1: judging a noise area and a non-noise area of the image:
an ultrasound log-compressed image I is acquired,size of image is M x NJudging a noise area and a non-noise area of the image according to the ultrasonic noise data, wherein M is the image depth, and N is the image width;
s2: acquiring an image tissue region:
the structure tensor of the two-dimensional image is a symmetric and semi-positive two-dimensional matrix, so that I has two eigenvalues λ 1 、λ 2 Respectively representing the maximum and minimum characteristic values of the image pixel points:
when lambda is 1 ≈λ 2 When the gray value of the pixel point is approximately equal to 0, the gray value of the pixel point is less changed in the neighborhood range, and the pixel point is located in a flat area;
when lambda is 1 ＞＞λ 2 When the value is approximately equal to 0, the gray value change in a certain direction is strong, and the pixel point is positioned at the edge;
when lambda is 1 ≥λ 2 >When 0, the characteristic vector in the specified direction has strong change, and the pixel point is positioned at the corner point; constructing a reliable function P by using the expression form of the characteristic value, wherein the reliable function P can be used for judging the tissue area in the ultrasonic image;
the specific method comprises the following steps:
a) In order to accelerate the calculation, respectively carrying out horizontal and longitudinal smoothing on the I and simultaneously carrying out down-sampling processing, and specifically, carrying out two-dimensional Gaussian filtering on the image I, and simultaneously extracting odd rows and odd columns to obtain an image I';
b) Image I' structure tensor S (I) computation
Wherein
* Representing convolution, delta is the variance, 0.5 is taken,
first, the transverse and longitudinal gradients, I, of the image I' are calculated x 、I y By means of I x 、I y Calculating a structure tensor, and then performing two-dimensional Gaussian smoothing on the structure tensor, wherein in order to accelerate calculation, the two-dimensional Gaussian smoothing can be converted into one-dimensional longitudinal Gaussian smoothing and one-dimensional transverse Gaussian smoothing;
c) Image I' feature value calculation
d) Image tissue region TisReg judgment
According to the expression form of the characteristic value, constructing a function P:
or
In the present application P takes the second form, λ 1 、λ 2 Belonging to a non-noise region:
when lambda is 1 ≈λ 2 When the value is approximately equal to 0, P is close to 0, and the pixel point is located in a flat area and is regarded as an organization area;
when lambda is 1 ＞＞λ 2 0 or lambda 1 ≥λ 2 >0, considered to be a non-tissue region;
providing a threshold TisThr for judging the tissue region, and when P < TisThr, considering the tissue region as the tissue region, wherein TisReg is 1, the rest are non-tissue regions, and TisReg is 0;
s3: calculating an image gain compensation curve:
judging whether to participate in the calculation of the axial average signal Axismean and the transverse average signal Lateralmean, namely:
TisPreThr is a threshold value which is set according to experience and used for judging whether the tissue is uniform or non-uniform, 0.9 is taken in the scheme, when L is taken as N, an axial average signal Axismean is calculated, when L is taken as M, a transverse average signal Lateralmean is calculated, and adjacent average signal interpolation processing is adopted for 0 point; the final axial gain compensation curve, gainAxis, is: gainxis = DstGain-AxisMean, dstGain being the desired gain;
since the gain compensation of the GainAxis is performed for each row during the longitudinal adjustment process, in order to achieve the target of the adjusted brightness DstGain on the basis, the lateral gain compensation curve GainLateral is:
carrying out up-sampling treatment on GainAxis and GainLaterial to obtain a final gain compensation curve;
s4: optimizing image gain:
and performing transverse and longitudinal gain optimization on the image according to a formula Iout = I + GainAxis + GainLateral, wherein Iout is the image after the gain optimization.
Further: the method includes the steps that an image noise area and a non-noise area are distinguished, noise data are obtained by closing emission, a longitudinal noise curve 1 & M is obtained by taking an average value in the transverse direction, the average value is obtained by repeating M times, in order to reduce the response time of a system, the noise curve is prestored in a file, the noise curve data are directly used for processing each time of optimization, when logarithmic compression data are smaller than the noise curve data, the noise area is considered to be the non-noise area, and otherwise the noise area is the noise area.
A B-mode image gain device, characterized by: the ultrasonic echo signal processing device comprises a probe, a transmitting control module, a receiving control module, an analog signal processing module, a beam synthesizer, a signal processing module, a B-type image gain automatic optimization module, an image processing module and a display module, wherein the transmitting control module excites the probe according to an instruction, an electric signal is converted into an acoustic signal, the probe receives an ultrasonic echo signal reflected by tissue and converts the acoustic signal into the electric signal, and the analog echo signal is converted into a digital echo signal by the analog signal processing module through amplification, filtering, time gain compensation, analog-to-digital conversion (ADC) and the like;
the wave beam synthesizer carries out processing such as delay accumulation focusing on the received multiple AD signals to obtain an RF signal;
after RF data are obtained, the system sends the data into a B-type image gain automatic optimization module through a signal processing module including IQ demodulation, low-pass filtering down-sampling and logarithmic compression, the automatic optimization module is started by setting a shortcut button on a keyboard, when the device is started, a proper gain adjustment curve is calculated through the device, the gain of the B-type image is adjusted, and when a change button is closed, the default gain curve of the system is used for adjustment;
the gain-adjusted image is sent to an image processing module, which includes dynamic range adjustment, image enhancement, digital scan conversion, etc., and is finally sent to a display for display.
The B-type image gain automatic optimization module comprises a data storage module, an image gain curve calculation module, an image gain curve output module and an image gain optimization processing module: the data storage module stores parameters required by the image gain curve calculation module, including noise curve data and a tissue region judgment threshold, in order to reduce the response time of the system and facilitate calculation, the noise data is calculated and stored in the data storage module before the image gain is optimized and is directly used in the image gain calculation module, and the values in the data storage module can be modified according to the actual situation; the image gain curve calculation module receives the noise curve data and the image data in the data storage module, calculates the transverse and longitudinal gain compensation curves by the automatic gain optimization method, and finally inputs the compensation curves into the image gain optimization processing module; and the image gain optimization processing module receives the gain compensation curve and the image data, performs gain optimization processing on the original image data according to an image optimization gain method, and finally outputs an optimized image, wherein the image data comprises fundamental wave or harmonic wave data in an ultrasonic B mode.
The embodiment is as follows: s1: judging a noise area and a non-noise area of the image:
acquiring an ultrasonic logarithmic compression image I, wherein the size of the image is M x N, M is the image depth, and N is the image width, and judging an image noise area and a non-noise area according to ultrasonic noise data;
s2: acquiring an image tissue region:
the structure tensor of the two-dimensional image is a symmetric and semi-positive two-dimensional matrix, so that I has two eigenvalues λ 1 、λ 2 Respectively representing the maximum and minimum characteristic values of the image pixel points:
when lambda is 1 ≈λ 2 When the gray value is approximately equal to 0, the gray value of the pixel point is represented, and the change of the gray value in the neighborhood range is smallThe pixel points are positioned in the flat area;
when lambda is 1 ＞＞λ 2 When the value is approximately equal to 0, the gray value change in a certain direction is strong, and the pixel point is positioned at the edge;
when lambda is 1 ≥λ 2 >When 0, the characteristic vector in the specified direction has strong change, and the pixel point is positioned at the corner point; constructing a reliable function P by using the expression form of the characteristic value, wherein the reliable function P can be used for judging the tissue area in the ultrasonic image;
the specific method comprises the following steps:
a) In order to accelerate the calculation, respectively performing horizontal and longitudinal smoothing and downsampling processing on the image I, specifically performing two-dimensional Gaussian filtering on the image I, and simultaneously extracting odd rows and odd columns to obtain an image I';
b) Image I' structure tensor S (I) computation
Wherein
* Represents convolution, delta is the variance, 0.5 is taken,
first, the transverse and longitudinal gradients, I, of the image I' are calculated x 、I y By means of I x 、I y Calculating a structure tensor, and then performing two-dimensional Gaussian smoothing on the structure tensor, wherein in order to accelerate calculation, the two-dimensional Gaussian smoothing can be converted into one-dimensional longitudinal Gaussian smoothing and one-dimensional transverse Gaussian smoothing;
c) Image I' feature value calculation
d) Image tissue region TisReg judgment
According to the expression form of the characteristic value, constructing a function P:
or
In the present application P takes the second form, λ 1 、λ 2 Belonging to a non-noise region:
when lambda is 1 ≈λ 2 When the value is approximately equal to 0, P is close to 0, and the pixel point is located in a flat area and is considered as an organization area;
when lambda is 1 ＞＞λ 2 0 or lambda 1 ≥λ 2 >0, considered to be a non-tissue region;
providing a threshold TisThr for judging the tissue region, and when P < TisThr, considering the tissue region as the tissue region, wherein TisReg is 1, the rest are non-tissue regions, and TisReg is 0;
s3: calculating an image gain compensation curve:
judging whether to participate in the calculation of the axial average signal Axismean and the transverse average signal Lateralmean, namely:
TisPreThr is a threshold value which is set according to experience and used for judging whether the tissue is uniform or non-uniform, in the scheme, 0.9 is taken, when L is taken as N, an axial average signal Axismean is calculated, when L is taken as M, a transverse average signal Lateralmean is calculated, and adjacent average signal interpolation processing is adopted for 0 point; the final axial gain compensation curve, gainAxis, is: gainAxis = DstGain-AxisMean, dstGain being the desired gain;
since the gain compensation of the GainAxis is performed for each row during the longitudinal adjustment process, in order to achieve the target of the adjusted brightness DstGain on the basis, the lateral gain compensation curve GainLateral is:
carrying out up-sampling treatment on GainAxis and GainLateral to obtain a final gain compensation curve;
s4: optimizing image gain:
and performing transverse and longitudinal gain optimization on the image according to a formula Iout = I + GainAxis + GainLateral, wherein Iout is the image after the gain optimization.
While there have been shown and described what are at present considered to be the basic principles and essential features of the invention and advantages thereof, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, but is capable of other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. The claims should not be construed to limit the claims to which they pertain.
Furthermore, it should be understood that although the present specification describes embodiments, not every embodiment includes only a single embodiment, and such description is for clarity purposes only, and it is to be understood that all embodiments may be combined as appropriate by one of ordinary skill in the art to form other embodiments as will be apparent to those of skill in the art from the description herein.
Claims (4)
1. A B-type image gain automatic optimization method is characterized by comprising the following steps:
s1: judging a noise area and a non-noise area of the image:
acquiring an ultrasonic logarithmic compression image I, wherein the size of the image is M x N, M is the image depth, and N is the image width, and judging an image noise area and a non-noise area according to ultrasonic noise data;
s2: acquiring an image tissue region:
the structure tensor of the two-dimensional image is a symmetric and semi-positive two-dimensional matrix, so that I has two eigenvalues λ 1 、λ 2 Respectively representing the maximum and minimum characteristic values of the image pixel points:
when lambda is 1 ≈λ 2 When the gray value of the pixel point is approximately equal to 0, the gray value of the pixel point is less changed in the neighborhood range, and the pixel point is located in a flat area;
when lambda is 1 ＞＞λ 2 When the value is approximately equal to 0, the gray value change in a certain direction is strong, and the pixel point is positioned at the edge;
when lambda is 1 ≥λ 2 >When 0, the characteristic vector in the specified direction has strong change, and the pixel point is positioned at the corner point; constructing a reliable function P by using the expression form of the characteristic value, wherein the reliable function P can be used for judging the tissue area in the ultrasonic image;
the specific method comprises the following steps:
a) In order to accelerate the calculation, respectively performing horizontal and longitudinal smoothing and downsampling processing on the image I, specifically performing two-dimensional Gaussian filtering on the image I, and simultaneously extracting odd rows and odd columns to obtain an image I';
b) Image I' structure tensor S (I) computation
Wherein
* Represents convolution, delta is the variance, 0.5 is taken,
first, the transverse and longitudinal gradients, I, of the image I' are calculated x 、I y By means of I x 、I y Calculating a structure tensor, and then performing two-dimensional Gaussian smoothing on the structure tensor, wherein in order to accelerate the calculation, the two-dimensional Gaussian smoothing is converted into one-dimensional longitudinal Gaussian smoothing and one-dimensional transverse Gaussian smoothing;
c) Image I' feature value calculation
d) Image tissue region TisReg judgment
According to the expression form of the characteristic value, a function P is constructed:
or
In the present application P takes the second form, λ 1 、λ 2 Belonging to a non-noise region:
when lambda is 1 ≈λ 2 When the value is approximately equal to 0, P is close to 0, and the pixel point is located in a flat area and is regarded as an organization area;
when lambda is 1 ＞＞λ 2 0 or lambda 1 ≥λ 2 >0, considered to be a non-tissue region;
providing a threshold TisThr for judging the tissue region, and when P < TisThr, considering the tissue region as the tissue region, wherein TisReg is 1, the rest are non-tissue regions, and TisReg is 0;
s3: calculating an image gain compensation curve:
judging whether to participate in the calculation of the axial average signal Axismean and the transverse average signal Lateralmean, namely:
TisPreThr is a threshold value which is set according to experience and used for judging whether the tissue is uniform or non-uniform, in the scheme, 0.9 is taken, when L is taken as N, an axial average signal Axismean is calculated, when L is taken as M, a transverse average signal Lateralmean is calculated, and adjacent average signal interpolation processing is adopted for 0 point; the final axial gain compensation curve, gainAxis, is: gainAxis = DstGain-AxisMean, dstGain being the desired gain;
since the gain compensation of the GainAxis is performed for each row during the longitudinal adjustment process, in order to achieve the target of the adjusted brightness DstGain on the basis, the lateral gain compensation curve GainLateral is:
carrying out up-sampling treatment on GainAxis and GainLateral to obtain a final gain compensation curve;
s4: optimizing image gain:
and performing transverse and longitudinal gain optimization on the image according to a formula Iout = I + GainAxis + GainLateral, wherein Iout is the image after the gain optimization.
2. The method of claim 1, wherein the method comprises: the method includes the steps that an image noise area and a non-noise area are distinguished, noise data are obtained by closing emission, a longitudinal noise curve 1 & M is obtained by taking an average value in the transverse direction, the average value is obtained by repeating M times, in order to reduce the response time of a system, the noise curve is prestored in a file, the noise curve data are directly used for processing each time of optimization, when logarithmic compression data are smaller than the noise curve data, the noise area is considered to be the non-noise area, and otherwise the noise area is the noise area.
3. A B-mode image gain device using the method of any one of claims 1-2, wherein: the ultrasonic echo signal processing device comprises a probe, a transmitting control module, a receiving control module, an analog signal processing module, a beam synthesizer, a signal processing module, a B-type image gain automatic optimization module, an image processing module and a display module, wherein the transmitting control module excites the probe according to an operation instruction, an electric signal is converted into an acoustic signal, the probe receives an ultrasonic echo signal reflected by tissue and converts the acoustic signal into the electric signal, and the analog echo signal is converted into a digital echo signal by the analog signal processing module through amplification, filtering, time gain compensation and analog-to-digital conversion (ADC);
the wave beam synthesizer carries out delay accumulation focusing processing on the received multiple paths of AD signals to obtain an RF signal;
after RF data are obtained, the system sends the data into a B-type image gain automatic optimization module through a signal processing module including IQ demodulation, low-pass filtering down-sampling and logarithmic compression, the automatic optimization module is started by setting a shortcut button on a keyboard, when the device is started, a proper gain adjustment curve is calculated through the device, the gain of the B-type image is adjusted, and when a change button is closed, the default gain curve of the system is used for adjustment;
the gain-adjusted image is sent to an image processing module, which includes dynamic range adjustment, image enhancement, digital scan conversion and finally to a display for display.
4. The B-mode image gain device of claim 3, wherein: the B-type image gain automatic optimization module comprises a data storage module, an image gain curve calculation module, an image gain curve output module and an image gain optimization processing module: the data storage module stores parameters required by the image gain curve calculation module, including noise curve data and a tissue region judgment threshold, in order to reduce the response time of a system and facilitate calculation, the noise data is calculated and stored in the data storage module before the image gain is optimized and is directly used in the image gain calculation module, and the value in the data storage module is modified according to the actual situation; the image gain curve calculation module receives the noise curve data and the image data in the data storage module, calculates the transverse and longitudinal gain compensation curves by the automatic gain optimization method, and finally inputs the compensation curves into the image gain optimization processing module; and the image gain optimization processing module receives the gain compensation curve and the image data, performs gain optimization processing on the original image data according to an image gain optimization method, and finally outputs an optimized image, wherein the image data comprises fundamental wave or harmonic wave data in an ultrasonic B mode.
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