Ultrasonic image processing method and processing system

Abstract

The invention provides an ultrasonic image processing method and a processing system. The method comprises the steps that S1 an original ultrasonic image is acquired; S2 the original ultrasonic image is subjected to non-linear coherence diffusion filtering denoising edge-preserving pre-processing to acquire a pre-processed ultrasonic image; S3 the edge of the pre-processed ultrasonic image and the non-edge region are enhanced to generate an enhanced ultrasonic image; and S4 the enhanced ultrasonic image is subjected to sharpening and deblurring to produce a final desired ultrasonic image. According to the invention, the image quality can be obviously improved, and especially the effects of noise removing and edge enhancing are obvious; and the algorithm is very fast and can be widely applied in various situations where the ultrasonic image needs to be processed in real time.

Description

technical field [0001] The invention is mainly applied in the technical field of medical ultrasonic diagnosis, and in particular relates to an ultrasonic image processing method and processing system. Background technique [0002] In ultrasound images, noise, especially speckle noise, will cover up and reduce certain details of the image, seriously affecting the quality of ultrasound images; suppressing speckle noise and enhancing edges in ultrasound images can greatly improve image quality. [0003] The existing ultrasonic image denoising algorithms mainly include local statistical filtering algorithm in spatial domain, filtering algorithm based on multi-scale transformation, and anisotropic diffusion filtering algorithm. [0004] In the patent document "A Method of Ultrasonic Image Denoising and Median Filtering Method" with the publication number CN 104299191 A, multiple directional filters are used to determine the direction of the local area of ​​each pixel, and then me...

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Problems solved by technology

[0005] The patent authorization document "Speckle noise filtering inultrasound imaging" with the publication number US5497777A performs multi-level wavelet transformation on ultrasonic images, then performs threshold correction on the coefficients, and finally performs wavelet inverse transformation; the wavelet threshold method can be divided into soft threshold method and hard threshold method , but in the hard threshold method, it is easy to cause discontinuity of wavelet data, thus affecting the details of the image, while in the soft threshold method, the total number of large wavelet coefficients is reduced, resulting in an image that is too smooth

[0006] Authorized announcement number CN 102073994 B patent document "Multi-scale Anisotropic Diffusion-Based Speckle Noise Suppression Method for Ultrasonic Medical Image" proposes to decompose the image by wavelet, and determine the diffusion threshold according to the mean value of the noise area at each scale to calculate the diffusion Parameters, use the diffusion parameter to weight the wavelet coefficients and then wavelet inverse transform; in this method, the wavelet coefficients are processed by the linear diffusion parameters, which is equivalent to anisotropic diffusion; this method suppresses the speckle noise and enhances the edge. , but the sharpness of the too smooth substantive area is not enough

[0007] In summary, the traditional spatial filtering method can suppress speckle noise but cannot enhance the edge or even blur the edge; the wavelet threshold method has the problem of affecting the image characteristics whether it is a soft threshold or a hard threshold; although anisotropic diffusion filtering can achieve The effect of denoising and edge protection, but it is prone to over-smoothing and insufficient definition of some substantial areas

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