Two Different Multi-Kernels for Fuzzy C-Means Algorithm for Medical Image Segmentation

Abstract

A new image segmentation using multi-hyperbolic and multi-Gaussian kernel based fuzzy c-means algorithm (KFCM) is proposed for medical magnetic resonance image (MRI) segmentation. The integration of two hyperbolic tangent kernels and two Gaussian kernels are used in the proposed algorithm for clustering of images. The performance of the proposed algorithm is tested on OASIS-MRI image dataset. The performance is tested in terms of score, number of iterations (NI) and execution time (TM) under different Gaussian noises on OASIS-MRI dataset. The results after investigation, the proposed method shows a significant improvement as compared to other existing methods in terms of score, NI and TM under different Gaussian noises on OASIS-MRI dataset.