Ultrasonic image processing method based on directional weighted median filter

Abstract

The invention discloses an ultrasonic image processing method based on a directional weighted median filter. According to the method, on the basis of the median filter, edge directions and edge amplitude information of an image are considered, a specific pixel point is selected as a current point, a point which is relevant to the edge direction of the current point is selected in a neighborhood window as a median filter, and the sequencing selection of a median is performed on the basis of pixel values weighted by the edge amplitude. According to the ultrasonic image processing method based on the directional weighted median filter, noises and edge information of ultrasonic images can be distinguished, so that the denoising is achieved, edge detail information of the ultrasonic images can be effectively protected, and later diagnosis and analysis can be facilitated; the basic algorithm applied to the method is the median filter, and the algorithm is simple and low in time complexity and in processing time, and accords with the requirement that an ultrasonic imaging system real-timely processes display images.

Description

technical field [0001] The present invention relates to data post-processing technology in ultrasonic imaging, in particular to image processing technology for improving the quality of ultrasonic images, and more specifically, to a direction-weighted median filter method for ultrasonic image denoising and enhancement. Background technique [0002] In the process of ultrasonic image formation, when the ultrasonic wave length is comparable to the surface roughness of the irradiated object, speckle noise will be generated, and this phenomenon can be explained by the random scattering model. The existence of these noises makes the definition of ultrasound images not high, which is also one of the main disadvantages of ultrasound imaging. Speckle noise unique to ultrasound images not only makes the quality of ultrasound images poor, especially covers and reduces some details of the images, but also makes it more difficult to identify and analyze image details. For future disease...

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Problems solved by technology

However, the SRAD model also has obvious defects: the scale function in the model is calculated from the largest possible uniform area in the initial image, and the key to the model is how to select a suitable uniform area as large as possible in the image. The selection of often affects the diffusion results to a great extent, which brings greater chance to the experimental results.

Moreover, anisotropic diffusion requires multiple iterations to achieve better results, and iterations are very time-consuming, so this algorithm is difficult to meet the needs of real-time ultrasound processing.

[0006] To sum up, the existing methods for removing speckle noise in ultrasound images have two key deficiencies that need to be improved: First, while smoothing the image and removing noise, the blurring of details and edges is serious, resulting in the blurring of some edge details. The loss affects the later diagnosis and analysis; the second is that the processing time complexity is relatively high, and it is difficult to meet the real-time processing and display image requirements of the ultrasound imaging system

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