Analysis of Magnetic Resonance Image Segmentation Using Spatial Fuzzy Clustering Algorithm

Abstract

The paper presents the MRI brain diagnosis support system for structure segmentation and its analysis using spatial fuzzy clustering algorithm. The method is proposed to segment normal tissues such as White Matter, Gray Matter, Cerebrospinal Fluid and abnormal tissue like tumor part from MR images automatically. The most of MR image are corrupted by artifacts which lead to affect the image processing for analysis of brain images. Due to this type of artifacts and noises, sometimes one type of normal tissue in MRI may be misclassified as other type of normal tissue and it leads to error during diagnosis.The proposed method consists of pre-processing using wrapping based curvelet transform to remove noise and modified spatial fuzzy C Means segments normal tissues by considering spatial information because neighboring pixels are highly correlated and also construct initial membership matrix randomly. The system also uses to segment the tumor cells along with this morphological filtering will be used to remove background noises for smoothening of region. The paper results will be presented as segmented tissues with parameter evaluation to show algorithm efficiency.   

Keywords: SFCM, NN, GLCM, DWT, MRI, CT.