CN106618632B - Automatic optimized ultrasonic imaging system and method - Google Patents
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Abstract
The invention relates to an automatic optimized ultrasonic imaging system and method, which is characterized in that: the method comprises the following steps: a probe; the beam forming module is used for performing beam forming on the ultrasonic echo signals received by the probe to form signal line data; the signal processing module is used for processing the signal line data to obtain an ultrasonic image; an automatic optimization module for optimizing the ultrasonic image; a scan conversion module; an image processing module; and, a display; the automatic optimization module comprises: the image monitor analyzes the difference between the current frame ultrasonic image and the previous frame ultrasonic image in real time; the parameter calculation module is used for calculating imaging parameters to obtain parameters of subsequent imaging; the gain compensator calculates a gain compensation image according to the output result of the parameter calculation module; and the noise suppressor is used for calculating a noise suppression image according to the output result of the parameter calculation module. The invention can monitor the change of the imaging state in real time in the imaging process and realize the automatic optimization of the image.
Description
Technical Field
The invention relates to an automatic optimization ultrasonic imaging system and method, and belongs to the technical field of ultrasonic imaging.
Background
Ultrasonic waves propagate in a human body with attenuation which is different with different imaging individuals and examination parts, so that doctors need to manually adjust imaging parameters such as time gain compensation, global gain, dynamic range, gray scale mapping curve and the like to obtain the best imaging effect in the diagnosis process to finish the confident diagnosis. The process increases extra work irrelevant to diagnosis for doctors, reduces working efficiency, and therefore, imaging parameters need to be automatically configured in ultrasonic imaging, better images are quickly obtained, and the accuracy and efficiency of diagnosis are improved.
Compensation of image Gain, usually Compensation in the Depth direction, in a general ultrasonic diagnostic apparatus, is called DGC (Depth Gain Compensation); DGC compensation values are preset in an ultrasonic imaging system according to different imaging frequencies and inspection positions, but the preset compensation values are difficult to adapt to different individuals due to the fact that the individual difference of a human body is large. The segmented toggle button is arranged on the control panel of the ultrasonic equipment to compensate the gray values of different depths, and the setting of the toggle button needs a doctor to manually adjust according to different individuals and different examination parts, so that the adjustment not only increases the workload of the doctor, but also needs proper skill. However, the adjustment of the images is obviously not the job of the doctor, and only increases the burden and reduces the efficiency. Therefore, an automatic gain adjustment function is required to simplify the work of the doctor and to allow the doctor to obtain a high quality image with certainty quickly.
At present, an automatic optimization method for ultrasonic imaging used by ultrasonic equipment in the market generally divides an ultrasonic image into blocks, then classifies each image block to judge whether the image block is a soft tissue, and performs gain compensation on the soft tissue to obtain a uniform image. The disadvantages of this are however evident: the size of the image blocks can influence the calculation accuracy, the size of the image blocks is small and is easily interfered by noise, the robustness of the algorithm is reduced, and the size of the image blocks can include some structural information such as tissue boundaries and the like, so that the calculation result is also influenced; secondly, the compensation values of different image blocks are different, and the unnatural transition between the compensation values of adjacent image blocks finally causes the mosaic phenomenon of the compensated image. These problems may render the optimized image less than ideal, or even add some artifacts, leading to a degradation of the image quality.
In addition, some existing ultrasonic imaging optimization methods require a doctor to press an optimized start key according to actual conditions, and although the workload of the doctor is reduced compared with a machine without the function, the existing ultrasonic imaging optimization methods require the interaction operation of the doctor and are still semi-automatic.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide an automatic optimization ultrasonic imaging system and method, which can monitor the change of an imaging state in real time in the imaging process and realize the automatic optimization of an image.
According to the technical scheme provided by the invention, the automatic optimization ultrasonic imaging system is characterized in that: the method comprises the following steps:
a probe for transmitting and receiving ultrasonic signals; the beam synthesis module is used for performing beam synthesis on the ultrasonic echo signal A received by the probe to form signal line data A1; a signal processing module for processing the signal line data A1 to obtain an ultrasonic image B; an automatic optimization module for optimizing the ultrasonic image B; a scan conversion module; an image processing module; and, a display;
the automatic optimization module comprises:
the image monitor analyzes the difference between the current frame ultrasonic image and the previous frame ultrasonic image in real time and outputs a trigger signal to the parameter calculation module;
the parameter calculation module is used for calculating imaging parameters to obtain parameters of subsequent imaging;
the gain compensator calculates a gain compensation image according to the output result of the parameter calculation module; and the number of the first and second groups,
and the noise suppressor is used for calculating a noise suppression image according to the output result of the parameter calculation module.
Further, the image monitor includes:
the state memory is used for saving the imaging state of the previous frame image;
a feature calculator for calculating an ultrasonic image feature value;
the state comparator is used for obtaining the characteristic value of the ultrasonic image input by the front-end node and comparing the characteristic value with the current value in the state memory; and the number of the first and second groups,
and the optimization parameter calculation trigger is used for triggering the parameter calculation module.
Further, the parameter calculation module comprises:
the pixel statistics device is used for carrying out statistics on the current ultrasonic image to obtain pixel information;
a pixel classifier for classifying the pixel information; and the number of the first and second groups,
and an image analyzer for performing image area analysis by using the classified result and outputting the analysis result to the gain compensator and the noise suppressor.
Furthermore, the probe is connected with the beam forming module, the output end of the beam forming module is connected with the signal processing module, the output end of the signal processing module is connected with the automatic optimization module, the output end of the automatic optimization module is connected with the scanning conversion module, the output end of the scanning conversion module is connected with the image processing module, and the output end of the image processing module is connected with the display.
Furthermore, the probe is connected with the beam forming module, the output end of the beam forming module is connected with the signal processing module, the output end of the signal processing module is connected with the scanning conversion module, the output end of the scanning conversion module is connected with the automatic optimization module, the output end of the automatic optimization module is connected with the image processing module, and the output end of the image processing module is connected with the display.
Further, the gain compensator calculates a gain compensation image, and the noise suppressor calculates a noise suppression image by the following calculation method:
GainCompI(i,j)＝TValue-MeanI(i,j)，
wherein, GainCompi (i, j) is a gain compensation image, Tvalue is a uniform tissue compensation target value, and Meani (i, j) is a pixel tissue brightness image; NoiseSupI (i, j) is a noise suppression image, RI (i, j) is a marking image, supresFactor is a set suppression factor, and i, j are pixel point coordinates;
applying the obtained optimized parameters to subsequent imaging, wherein the parameter application method comprises the following steps:
OptI (I, j) ═ noises supi (I, j) × [ I (I, j) + GainCompI (I, j) ], where OptI (I, j) is the post-optimization image and I (I, j) is the pre-optimization image.
The automatic optimization ultrasonic imaging method is characterized by comprising the following steps of:
(1) ultrasonic echo signals A received by the probe are subjected to beam forming through a beam forming module to form signal line data A1, and a signal processing module is used for carrying out signal processing on the signal line data A1 to obtain an ultrasonic image B;
(2) the processing of the ultrasonic image B by the automatic optimization module to obtain the optimized ultrasonic image B1 specifically comprises the following steps:
a. firstly, an ultrasonic imaging system inputs an ultrasonic image B processed by a signal processing module into an automatic optimization module, an image monitor in the automatic optimization module analyzes the difference between a current frame ultrasonic image and a previous frame ultrasonic image in real time, and if the difference exceeds a set threshold value, a parameter calculation module is automatically operated to calculate imaging parameters;
b. the parameter calculation module calculates parameters for subsequent imaging;
c. according to the output result of the parameter calculation module, the gain compensator calculates a gain compensation image, and the noise suppressor calculates a noise suppression image; finally, the automatic optimization module outputs an automatically optimized ultrasound image B1;
(3) the optimized ultrasonic image B1 is processed by the scan conversion module and the image processing module, and finally transmitted to the display for image display.
Further, the operation process of the image monitor in the step (2) a is as follows: the state memory stores the imaging state of the previous frame image, the feature calculator calculates the feature value of the ultrasonic image, the state comparator obtains the feature value of the ultrasonic image input by the front-end node and compares the feature value with the current value in the state memory, and if the feature value exceeds a set threshold value, the optimization parameter calculation trigger triggers the parameter calculation module.
Further, the parameter calculating module in the step (3) b is operated: inputting the current ultrasonic image into a pixel statistics device for statistics to obtain pixel information; inputting the pixel information into a pixel classifier for classification; after the classification, the image analyzer performs image area analysis using the result of the classification.
Further, the method for analyzing the image area by the image analyzer is as follows: and analyzing in a neighborhood of the current pixel, and counting the classification condition of the pixels in the neighborhood, wherein the neighborhoods of the adjacent pixels are partially overlapped.
Further, according to the output result of the image analyzer, calculating a gain compensation image in a gain compensator, and calculating a noise suppression image in a noise suppressor; the calculation method is as follows:
GainCompI(i,j)＝TValue-MeanI(i,j)，
wherein, GainCompi (i, j) is a gain compensation image, Tvalue is a uniform tissue compensation target value, and Meani (i, j) is a pixel tissue brightness image; NoiseSupI (i, j) is a noise suppression image, RI (i, j) is a marking image, supresFactor is a set suppression factor, and i, j are pixel point coordinates;
applying the obtained optimized parameters to subsequent imaging, wherein the parameter application method comprises the following steps:
OptI (I, j) ═ noises supi (I, j) × [ I (I, j) + GainCompI (I, j) ], where OptI (I, j) is the post-optimization image and I (I, j) is the pre-optimization image.
The automatic optimization ultrasonic imaging system and method can monitor the change of the imaging state in real time in the imaging process, automatically calculate the imaging parameters according to the change of the image, classify the image pixels, and calculate the gain compensation image and the noise suppression image on the basis, thereby realizing the automatic optimization of the image. The physician is not required to frequently press the optimize function button. After the optimization is started, different tissue types are divided through the statistics of the gray value, and the compensation value is refined and calculated, so that an ultrasonic image with uniform brightness is obtained.
Drawings
FIG. 1 is a schematic diagram of an automatically optimized ultrasound imaging system of the present invention.
Fig. 2 is a statistical histogram employed by the pixel classifier.
Fig. 3 is a schematic diagram of the automatic optimization module.
Fig. 4 is a schematic view of the image monitor.
Fig. 5 is a schematic diagram of the parameter calculation module.
Fig. 6 is a schematic diagram of a conventional image partitioning method.
FIG. 7 is a diagram illustrating an image partition method according to the present invention.
Fig. 8 is a schematic diagram of another embodiment of the automatically optimized ultrasound imaging system of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, in the embodiment of the present invention, an ultrasound host controls a probe 100 to transmit and receive an ultrasound signal, an ultrasound echo signal a received by the probe 100 is beam-synthesized by a beam synthesis module 200 to form signal line data a1, a signal processing module 300 performs signal processing such as filtering, envelope detection, log compression on the signal line data a1 to obtain an ultrasound image B, the ultrasound image B is processed by an automatic optimization module 400 to obtain an optimized ultrasound image B1, and the optimized ultrasound image B1 is processed by a scan conversion module 500 and an image processing module 600 and finally transmitted to a display 700 to display an image.
As shown in fig. 3, the present invention adds an automatic optimization module in the conventional ultrasound imaging system, and the automatic optimization module monitors the real-time ultrasound image, automatically adjusts parameters, and automatically optimizes the image, thereby obtaining a high-quality diagnostic image. The automatic optimization module 400 includes: an image monitor 401, a parameter calculation module 402, a gain compensator 403, a noise suppressor 404, and the like. Firstly, the ultrasonic imaging system inputs the ultrasonic image B processed by the signal processing module 300 into the automatic optimization module 400, the image monitor 401 in the automatic optimization module 400 analyzes the difference between the current frame ultrasonic image B and the previous frame ultrasonic image B' in real time, if the difference exceeds a set threshold, it indicates that the imaging object has changed, the change may be the change of the examination part or the change of the patient, in this case, the doctor does not need to manually adjust the imaging parameters or start the automatic optimization function, and the ultrasonic imaging system automatically operates the parameter calculation module 402 to calculate the imaging parameters. The parameter calculation module 402 calculates parameters for subsequent imaging, such as gain compensation image parameters and noise suppression image parameters, the parameter calculation module 402 applies these parameters to the unprocessed ultrasound image B, and finally the automatic optimization module 400 outputs the automatically optimized ultrasound image B1.
After the ultrasound imaging system automatically turns on the automatic optimization module 400, the image monitor 401 analyzes the ultrasound image to determine the optimized parameters. The image monitor 401 is composed of a state memory 4011, a feature calculator 4012, a state comparator 4013, an optimization parameter calculation trigger 4014 and the like, wherein the state memory 4011 stores an imaging state of a previous frame image, and the imaging state can be an image feature value, such as average brightness, a signal-to-noise ratio, an even tissue ratio and the like, or can be ultrasonic image data; the feature calculator 4012 calculates the feature values of the input ultrasound image, such as average brightness, signal-to-noise ratio, and uniform tissue ratio; the state comparator 4013 obtains a feature value of the ultrasound image input by the front-end node and compares the feature value with a current value in the state memory 4011, and if the feature value exceeds a set threshold, it is determined that the imaging state has changed, and the parameter calculation is triggered by the optimization parameter calculation trigger 4014.
TSignal? 1:0, wherein TSignal is a trigger signal, CurStatus is the imaging state of the current frame image, and CurImageFV is a feature vector of the input image. Compare is a comparison algorithm function, and may be a simple operation such as calculating the similarity of features, e.g. the Euler distance between features, or the sum of absolute values of the differences between features. If the comparison result of the state comparator 4013 is within the set threshold, the parameter calculation module 402 is not triggered, otherwise, the parameter calculation module 402 is triggered.
After the parameter calculating module 402 is triggered, the parameter calculating module 402 calculates new optimized parameters, inputs the current ultrasound image into a pixel statistics 4021 for statistics, where the statistics may be information such as brightness and peripheral gradient of pixels in a time domain, or spectrum information in a frequency domain, inputs the pixel information into a pixel classifier 4022 for classification, and divides the pixels into three types, i.e., uniform tissue pixels, structural pixels, and noise pixels, and the classification method may be classified by using a set statistic threshold value, or may be performed according to a certain proportion value of the statistical histogram, in an embodiment of the present invention, the classification is performed by using a proportion of the statistical histogram, for example, for an abdominal liver image, most of the pixels are liver tissue pixels, pixels within a given range of a histogram peak value are set as tissue pixels, and pixels outside the region are noise pixels or structural pixels, as shown in fig. 2, a peak value of the statistical histogram is set as Summit, pixels below L owThr are set as noise, pixels above up are structural pixels, and pixels between L and throw are uniform tissue pixels, and the proportion of the classification of all the pixels may be determined by using a large amount of image classifier 4022:
Of course, the above is merely an example of one pixel classifier, and a comprehensive classification of a plurality of classifiers may be included in implementing the present invention.
After classification at the image pixel level, we use the classification result to perform image area analysis, and there are many image partitioning methods, such as dividing a plurality of rows into one area in the depth of a scan line in the conventional partitioning method, or dividing a plurality of scans into one area in the scan direction, or dividing an image into a plurality of blocks, as shown in fig. 6.
The image analyzer 4023 of the present invention adopts a pixel-level area analysis method, and analyzes in a neighborhood of the current pixel, and counts the classification condition of the pixels in the neighborhood, if the pixels in the neighborhood exceed the set proportion and belong to the structural pixels, the current pixel is marked as 3, otherwise, if the proportion of the noise pixels in the neighborhood exceeds the set value, the current pixel is marked as 1, otherwise, the current pixel is marked as 2; the pixel tissue intensity value is the average of tissue pixels in the neighborhood for the case labeled 2, and the neighborhood tissue intensity value is the average of tissue intensities of pixels surrounding the pixel for the case labeled 1 or 3. As shown in fig. 7, it can be seen that the neighborhoods of the adjacent pixels are partially overlapped, so that the calculation result does not bring abrupt change and discontinuity phenomena like the conventional partitioning method.
The gain compensation image and the noise suppression image are calculated based on the output result of the image analyzer 4023, that is, the marker image indicated by 1, 2, 3 and the neighborhood tissue mean image. The calculation method is as follows:
GainCompI(i,j)＝TValue-MeanI(i,j)，
wherein, GainCompi (i, j) is a gain compensation image, Tvalue is a uniform tissue compensation target value, and Meani (i, j) is a pixel tissue brightness image; NoiseSupi (i, j) is a noise suppression image, RI (i, j) is a marking image, SuppresFactor is a set suppression factor, and i, j are pixel coordinates.
After the optimization parameters are calculated, they are applied to subsequent imaging until the optimization parameters are recalculated. The parameter application method comprises the following steps:
OptI (I, j) ═ noises supi (I, j) × [ I (I, j) + GainCompI (I, j) ], where OptI (I, j) is the post-optimization image and I (I, j) is the pre-optimization image.
In the above formula I (I, j) + GainCompI (I, j) is implemented in gain compensator 403 and the noises supi (I, j) [ ] part is implemented in noise suppressor 404.
As shown in fig. 8, the present invention may also place the automatic optimization module 400 after the scan conversion module 500 for processing.
Claims (7)
1. An automatically optimized ultrasound imaging system, characterized by: the method comprises the following steps:
a probe (100) for transmitting and receiving an ultrasonic signal; a beam forming module (200) for forming signal data by performing beam forming on the ultrasonic echo signal received by the probe (100); a signal processing module (300) for processing the signal data to obtain an ultrasonic image B; an automatic optimization module (400) for performing optimization processing on the ultrasound image B; a scan conversion module (500); an image processing module (600); and, a display (700);
the automatic optimization module (400) comprises:
the image monitor (401) analyzes the difference between the current frame ultrasonic image and the previous frame ultrasonic image in real time and outputs a trigger signal to the parameter calculation module (402);
the parameter calculation module (402) is used for calculating imaging parameters to obtain parameters of subsequent imaging;
a gain compensator (403) for calculating a gain compensation image based on the output result of the parameter calculation module (402); and the number of the first and second groups,
a noise suppressor (404) for calculating a noise-suppressed image based on the output result of the parameter calculation module (402);
the parameter calculation module (402) comprises:
a pixel statistics device (4021) for performing statistics on the current ultrasound image to obtain pixel information;
a pixel classifier (4022) for classifying the pixel information; dividing the pixels into uniform tissue pixels, structural pixels and noise pixels;
and the number of the first and second groups,
an image analyzer (4023) that performs image area analysis using the result of the classification and outputs the analysis result to the gain compensator (403) and the noise suppressor (404);
the gain compensator (403) calculates a gain compensated image and the noise suppressor (404) calculates a noise suppressed image by the following calculation method:
GainCompI(i,j)＝TValue-MeanI(i,j)，
wherein, GainCompI (i, j) is a gain compensation image, Tvalue is a uniform tissue compensation target value, and MeanI (i, j) is tissue brightness of a pixel; NoiseSupI (i, j) is a noise suppression image, RI (i, j) is a marking pixel, SuppresFactor is a set suppression factor, and i, j are pixel point coordinates;
applying the obtained optimized parameters to subsequent imaging, wherein the parameter application method comprises the following steps:
OptI (I, j) ═ noises supi (I, j) × [ I (I, j) + GainCompI (I, j) ], where OptI (I, j) is the post-optimization image and I (I, j) is the pre-optimization image.
2. The automatically optimized ultrasound imaging system of claim 1, wherein: the image monitor (401) includes:
a state memory (4011) for saving an imaging state of a previous frame image;
a feature calculator (4012) that calculates an ultrasound image feature value;
a state comparator (4013) for obtaining a feature value of the ultrasound image inputted by the feature calculator (4012) and comparing the feature value with a current value in the state memory (4011); and the number of the first and second groups,
an optimization parameter calculation trigger (4014) for triggering the parameter calculation module (402).
3. The automatically optimized ultrasound imaging system of claim 1, wherein: the probe (100) is connected with the beam forming module (200), the output end of the beam forming module (200) is connected with the signal processing module (300), the output end of the signal processing module (300) is connected with the automatic optimization module (400), the output end of the automatic optimization module (400) is connected with the scanning conversion module (500), the output end of the scanning conversion module (500) is connected with the image processing module (600), and the output end of the image processing module (600) is connected with the display (700).
4. The automatically optimized ultrasound imaging system of claim 1, wherein: the probe (100) is connected with the beam forming module (200), the output end of the beam forming module (200) is connected with the signal processing module (300), the output end of the signal processing module (300) is connected with the scanning conversion module (500), the output end of the scanning conversion module (500) is connected with the automatic optimization module (400), the output end of the automatic optimization module (400) is connected with the image processing module (600), and the output end of the image processing module (600) is connected with the display (700).
5. An automatic optimized ultrasonic imaging method is characterized by comprising the following steps:
(1) ultrasonic echo signals received by the probe (100) are subjected to beam forming by the beam forming module (200) to form signal data, and the signal processing module (300) is used for processing the signal data to obtain an ultrasonic image;
(2) the method for processing the ultrasonic image B by the automatic optimization module (400) to obtain the optimized ultrasonic image B1 specifically comprises the following steps:
a. firstly, an ultrasonic imaging system inputs an ultrasonic image B processed by a signal processing module (300) into an automatic optimization module (400), an image monitor (401) in the automatic optimization module (400) analyzes the difference between a current frame ultrasonic image and a previous frame ultrasonic image in real time, and if the difference exceeds a set threshold value, an automatic operation parameter calculation module (402) calculates imaging parameters;
b. the parameter calculation module (402) calculates parameters for subsequent imaging;
c. according to the output result of the parameter calculation module (402), the gain compensator (403) calculates a gain compensation image, and the noise suppressor (404) calculates a noise suppression image; finally, the automatic optimization module (400) outputs an automatically optimized ultrasound image B1;
(3) the optimized ultrasonic image B1 is processed by a scan conversion module (500) and an image processing module (600), and finally transmitted to a display (700) for image display;
the working process of the parameter calculation module (402) in the step (2) b is as follows: inputting the current ultrasonic image into a pixel statistics device (4021) for statistics to obtain pixel information; inputting the pixel information into a pixel classifier (4022) for classification, and dividing the pixels into uniform tissue pixels, structural pixels and noise pixels; after the classification, the image analyzer (4023) performs image area analysis using the result of the classification;
calculating a gain compensation image in a gain compensator (403) and a noise suppression image in a noise suppressor (404) based on the output result of the image analyzer (4023); the calculation method is as follows:
GainCompI(i,j)＝TValue-MeanI(i,j)，
wherein, GainCompI (i, j) is a gain compensation image, Tvalue is a uniform tissue compensation target value, and MeanI (i, j) is tissue brightness of a pixel; NoiseSupi (i, j) is a noise suppressed image, and RI (i, j) is a markThe SuppresFactor is a set inhibition factor, and i and j are pixel point coordinates;
applying the obtained optimized parameters to subsequent imaging, wherein the parameter application method comprises the following steps:
OptI (I, j) ═ noises supi (I, j) × [ I (I, j) + GainCompI (I, j) ], where OptI (I, j) is the post-optimization image and I (I, j) is the pre-optimization image.
6. The automatically optimized ultrasound imaging method of claim 5, wherein: the working process of the image monitor (401) in the step (2) a is as follows: the state storage (4011) saves the imaging state of the previous frame image, the feature calculator (4012) calculates the feature value of the ultrasonic image, the state comparator (4013) obtains the feature value of the ultrasonic image input by the front-end node and compares the feature value with the current value in the state storage (4011), and if the feature value exceeds the set threshold value, the over-optimization parameter calculation trigger (4014) triggers the parameter calculation module (402).
7. The automatically optimized ultrasound imaging method of claim 5, wherein: the method for analyzing the image area by the image analyzer (4023) comprises the following steps: analyzing in a neighborhood of the current pixel, and counting the classification condition of the pixels in the neighborhood, wherein the neighborhoods of the adjacent pixels are partially overlapped;
the image analyzer (4023) adopts a pixel-level regional analysis method, analyzes in a neighborhood of the current pixel, counts the classification condition of the pixels in the neighborhood, if the pixels in the neighborhood exceed a set proportion and belong to the structural pixels, the current pixel is marked as 3, otherwise, if the proportion of the noise pixels in the neighborhood exceeds the set value, the current pixel is marked as 1, otherwise, the current pixel is marked as 2; the tissue intensity value of the pixel is the average of the tissue pixels in the neighborhood for the case labeled 2, and the tissue intensity value of the neighborhood is the average of the tissue intensities of the pixels surrounding the pixel for the case labeled 1 or 3.
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| CN1384661A (en) * | 2001-05-01 | 2002-12-11 | 佳能株式会社 | Radiation image processing equipment and method, image processing system, storing medium and program |
| CN101987023A (en) * | 2009-07-31 | 2011-03-23 | 深圳迈瑞生物医疗电子股份有限公司 | Gain compensation and image optimization method and device for ultrasonic imaging and system |
| CN102499711A (en) * | 2011-09-28 | 2012-06-20 | 无锡祥生医学影像有限责任公司 | Three-dimensional or four-dimensional automatic ultrasound image optimization and adjustment method |
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