Hybrid Method Segmentation for Medical Image Based on DWT, FCM and HMRF-EM

Abstract

Abstract—Segmentation is fundamental and crucial operation which comes prior to any other operation systems on image processing. We present in this paper a hybrid segmentation method of MRI to aid diagnosis of brain tumors. Our approach is based on the theory of fuzzy subsets and probabilistic models. We are proposing to obtain a tag map to initialize the class number for the Fuzzy C-Means (FCM) algorithm using wavelet transform decomposition. The Fuzzy C-Means algorithm is used as a classification phase; the last step is algorithm of Markov fields which is used as a phase of adjustment to find best partition obtained during the classification step. All of this increases the robustness of our approach to noises and defects specific to MRI images. Finally, we compare our approach to classical segmentation algorithms: Fuzzy C-Means and Markov fields. The proposed approach provides better results with a segmentation error margin 20.15 % against 28.37 % for Markov fields and 31.33 % for Fuzzy C-Mean