Automatic brain tumor segmentation method using improved fuzzy C-means and fuzzy particle swarm optimization

Abstract

The brain is the most important organ of the human body. It has a complicated structure, and a precise segmentation of brain cerebral tissues plays an important role for tumor detection. Since the manual segmentation is tedious and time-consuming, automatic segmentation becomes a more attractive subject to most researchers. Recently, many automatic segmentation methods have been proposed using clustering algorithms. Nonetheless, there are some remaining issues: noisy images and local optima. This study proposes a hybrid method by combining two clustering methods: FCM-FPSO and IFCM-PSO. In this research, a Gaussian filter is first applied as a pre-processing step to remove noises. Then, the enhanced image is segmented using a modified clustering method called Improved Fuzzy C-Means (IFCM). In IFCM, besides the target pixel intensity, the distance and intensity of the neighbours of the target pixel are used as the segmentation parameters. The presence of these parameters are helpful in case of the segmentation of noisy images. In order to prevent IFCM from falling into local optima, Fuzzy Particle Swarm Optimization (FPSO) is used to improve the parameter initialization step. FPSO is initialized by using a random membership function. The hybrid method is applied on thirty-one MRI brain tumor images collected from MICCAI 2012. The experimental results revealed that the F1-Measure of 79.98%, obtained by proposed hybrid method, is higher than that of the recent segmentation methods.