The Algorithm On MRI Image Denoising And Segmentation Based On Wavelet Transform And Fuzzy Cluster

The technique of MRI image processing is different from that of other sorts of images because of its unique imaging methods. In order to achieve diverse aims, in this paper, the algorithm on MRI images denoising and segmentation are proposed, respectively.First, a new adaptive wavelet-based Magnetic Resonance images denoising algorithm is proposed. A Rician distribution for background-noise modelling is introduced and a Maximum-Likelihood method for the parameter estimation procedure is used. Further discrimination between edge- and noise-related coefficients is achieved by updating the shrinkage function along consecutive scales and applying spatial constraints.We also develop an algorithm on MRI images robust segmentation based on fuzzy clustering. The algorithm is formulated by modifying the objective function of the standard fuzzy C-means (FCM) method to compensate for intensity inhomogeneities. An additional term is injected into the objective function to constrain the behavior of membership functions with the neighborhood effect. Meanwhile, we also describe an adaptive K-means clustering algorithm that initializes the centroids.The efficacies of the algorithms are demonstrated on both simulated and real Magnetic Resonance images. The results are shown to be promising and outperform previous related algorithms.