Integration of Hyperbolic Tangent and Gaussian Kernels for Fuzzy C-means Algorithm with Spatial Information for MRI Segmentation

In this paper, a new segmentation algorithm by integrating the hyperbolic tangent and Gaussian kernels for fuzzy c-means (HGFCM) algorithm with spatial information is proposed for medical image segmentation. The proposed method uses the combined kernels of hyperbolic tangent function and Gaussian kernel with the spatial information of neighboring pixels 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.