Research On Brain MR Image Segmentation Based On Modified Fuzzy Clustering Algorithm

Image segmentation is a technique of medical image process which extracts the interested object from target image in assisting image processing and analyzing. In the methods of image segmentation, the most commonly used method is Fuzzy C-mean (FCM) clustering, which doesn’t need setting any threshold or get people involved. Since the clustering accuracy of the traditional FCM algorithm is low and susceptible to noise in image segmentation, this thesis makes improvements on it.(1) Gaussian kernel function is introduced to the FCM algorithm to instead Euclidean distance of the traditional FCM algorithm, converting low dimensional nonlinear systems into high dimensional linear ones, which reduces the complexity of the problem.(2) To avoid the problem proposed by Dave that the noise distance value is constant, the function of is improved in this dissertation to change with the pixels.(3) The uncertainty of the membership degree makes clustering result less effective. Therefore, in this thesis, information entropy is introduced as a method to measure the quality of clustering.For comparison, the experiments of the synthesis image and brain magnetic resonance image are designed to verify the effectiveness and practicability of the improved algorithm proposed in this dissertation. It is shown that the algorithm proposed in this dissertation can improve the clustering accuracy and has a good performance of noise reduction compared with the traditional clustering algorithm.