![]() However they may produce unnatural patterns in the resultant images. Adaptive median filters are superior to Gaussian filters in despeckling. They adjusted the variance of the Gaussian filter during the smoothing process so that noises were reduced and edges were better preserved. In, Deng and Cahill proposed an adaptive Gaussian filter to solve this problem. Gaussian filters can suppress speckles and enhance contrast, but they blur edges. Thus essential information for diagnosis is lost. Speckles deteriorate tissue boundaries and make homogeneous regions look rough. These echoes trigger constructive and destructive interferences and generate speckle patterns in the ultrasound images. Nonetheless, smaller structures in relatively homogeneous regions can also produce echoes with random phases. In ultrasound scanning, the reflected sound waves are mainly generated by tissue boundaries. Test results show that the proposed despeckle method reduces speckles in uniform areas and enhances tissue boundaries and spots. Based on the classification result, a feasible filter is selected to suppress speckles and enhance features. Then the eigensystem of the diffusion tensor is calculated and employed to detect and classify the underlying structure. The diffusion tensor of intensity is computed at each pixel at first. This paper presents an innovative despeckle procedure for ultrasound images. However, they may produce artifact patterns in the resulted images and oversmooth nonuniform regions. Median filter based despeckling algorithms produceīetter results. Linear filters can suppress speckles, but they smooth out features. Thus despeckling is a necessity in ultrasound image processing. Speckles blur features which are essential for diagnosis and assessment. Ultrasound images are prone to speckle noises. ![]()
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