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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 symmetrical mirror pixels are almost similar. As a result, the symmetry information can be used to enhance results of brain segmentation methods. In this paper, I introduced a new version of the Fuzzy C-Mean (FCM) segmentation method which is called Genetic Spatial Possibilistic Fuzzy C-Mean (GSPFCM). GSPFCM integrates symmetry information with SPFCM. It is an extension of Possibilistic Fuzzy C-Mean (PFCM) on 3D Magnetic Resonance (MR) images. GSPFCM uses the spatial information and fuzzy membership values. Spatial and possibilistic information were added in order to solve the noise sensibility defect of FCM. To integrate the symmetry information, I first extracted the Mid-Sagittal Surface using a proposed genetic algorithm. According to this algorithm, inside each axial slice, a Thin-Plate Spline (TPS) surface was constructed and a genetic algorithm was applied to fit this TPS surface to the brain data. Then, the symmetry degree of each symmetry pair voxels was calculated. Finally, the membership values in SPFCM were updated based on the corresponding symmetrical values. The efficiency of GSPFCM, was evaluated using both simulated and real Magnetic Resonance Images (MRI), and was compared to the state-of-the-art methods. My results showed images with different degrees of Intensity Non-Uniformity (INU) and different levels of noise were segmented efficiently by the GSPFCM.

Keywords

3D brain MR segmentation Mid-Sagittal Surface Fuzzy C-Mean genetic algorithm fractal dimension possibilistic information spatial information

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Spatial possibilistic fuzzy C-Mean segmentation method integrated with brain Mid-Sagittal Surface information extracted by an evolutionary algorithm

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