Automatic ultrasonic-image optimization method based on signal statistic analysis

Abstract

The invention discloses an automatic ultrasonic-image optimization method based on signal statistic analysis. The method comprises the steps of data acquisition, signal statistic analysis and self-adaptive digital gain optimization. In the step of self-adaptive digital gain optimization, the dynamic range of signals is compressed for gray display. The method provided by the invention realizes automatic optimization of an ultrasonic image of an ultrasonic diagnosis imaging system through the steps of data acquisition, signal statistic analysis, digital gain calculation, dynamic range compression and the like, and can ensure that the finally presented image has higher gray equilibrium and a higher contrast ratio, thereby achieving a better image display effect to help to obtain a better diagnosis and treatment effect during a medical treatment process.

Description

technical field [0001] The invention designs a method for automatically optimizing a pattern in an ultrasound imaging system, and in particular relates to a method for automatically optimizing an ultrasound image based on signal statistical analysis. Background technique [0002] The principle of traditional ultrasound imaging is: the probe emits a short pulse of ultrasound. After the ultrasound beam penetrates the skin and enters the human body, the reflected or backscattered echoes generated by the large and small interfaces composed of tissues with different acoustic characteristic impedances return to the probe and are detected. After the probe is received, it is converted into an echo electrical signal corresponding to the depth interface. After amplification and processing, a series of bright and dark bright spots are displayed on the screen as an ultrasonic information line, indicating the information of each tissue interface along the corresponding depth of the sound ...

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Problems solved by technology

However, these techniques either analyze the entire image without considering the local information, and the optimization effect achieved is not obvious; or when adjusting the local area of ​​the image, the effect of image optimization is affected by the prior knowledge of the selected sub-area, which cannot be achieved. Real-time images are optimized to the best state, and the algorithm implementation process is cumbersome

[0008] In addition, most of the known image optimization techniques only involve the adjustment of the gain in the depth direction, and do not involve the adjustment of the grayscale mapping curve during dynamic range compression.

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