Adaptive SUSAN diffusion and denoising method of medical ultrasonic image

Abstract

The invention relates to an adaptive SUSAN diffusion and denoising method of a medical ultrasonic image. The method comprises the following steps; (1), adaptively selecting an image all-speckle and noise area; (2), carrying out image edge detection based on an SUSAN algorithm; and (3), carrying out medical ultrasonic image denoising based on SUAN diffusion. Compared with the prior art, the technology employed in the invention enables the method to have the following characteristics: the workload of manually setting of threshold parameters can be reduced; robustness of the method can be improved; and the method is suitable for popularization and application and the like.

Description

technical field [0001] The invention belongs to the field of biomedical signal processing, and in particular relates to an adaptive SUSAN diffusion denoising method for medical ultrasonic images. Background technique [0002] Compared with other medical imaging technologies, medical ultrasound imaging technology has the advantages of no damage, no radiation, low cost, real-time performance, and repeatability. It has become one of the four modern medical imaging technologies and is currently playing an important role in medical clinical examination and diagnosis. role. However, there is a large amount of speckle noise in medical ultrasound images, which greatly reduces the readability of ultrasound images and seriously affects the accuracy of image post-processing (such as segmentation, registration, fusion, and feature extraction). The further popularization and application of medical ultrasound imaging technology is limited. [0003] In order to make full use of the advan...

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Problems solved by technology

[0004] (1) The edge detection algorithm cannot distinguish the noise and the edge in the image well;

[0005] (2) The suppression effect on multiplicative speckle noise needs to be improved. For example, the PM model has a better suppression effect on additive noise, but the suppression effect on multiplicative noise It is worse; the SRAD model will retain some mutation points after denoising; the NCD model will also introduce some false edges while denoising;

However, this technology is difficult to be popularized and applied due to the need for manual adjustment of many parameters and the unstable denoising effect

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