Spatial Possibilistic Fuzzy C-Mean Segmentation Algorithm Integrated with Brain Mid-sagittal Surface Information

Abstract

A normal human brain holds a high level of bilateral reflection symmetry. On the sagittal view, the brain can be separated into the left and the right hemispheres with approximately identical anatomical properties, so that symmetric mirror pixels have similar properties. As a result, the symmetry information can be used to enhance results of brain segmentation methods. In this paper, we introduced a new version of the Fuzzy C-Mean segmentation method, Symmetry Spatial Possibilistic FCM. SymSPFCM integrates symmetry information with SPFCM which is an extension of PFCM on 3D MR images, and uses the spatial information to calculate possibilistic values and fuzzy membership values. We added spatial and possibilistic information in order to solve the noise sensibility defect of FCM. To integrate the symmetry information, we first extracted the mid-sagittal surface using our proposed method. According to this method, inside each axial slice, a thin-plate spline surface was constructed and local optima were applied to fit this TPS surface to the brain data. Then, the symmetry degree of each symmetry pair was calculated. Finally, the membership values in SPFCM were updated based on the corresponding symmetric values. The efficiency of the proposed method, SymSPFCM, was evaluated using both simulated and real magnetic resonance images and was compared to the state-of-the-art methods. Our results showed images with different degrees of intensity non-uniformity and different levels of noise were segmented efficiently by the SymSPFCM.