Segmentation of brain MR images using an adaptively regularized kernel FCM algorithm with spatial constraints

Abstract

FCM algorithm is a popular algorithm for medical image segmentation. The precise process of segmenting brain tissue images becomes more challenging in the presence of noise and other image artifacts. An improved adaptively regularized kernel FCM method is proposed in this paper. The spatial constraint function of membership is introduced to enhance clustering by adjusting the degree of influence between pixels and clustering centers. Experimental results on the brain images with different types and levels of noises demonstrate that the improved algorithm increases the accuracy of segmentation compared with the other soft clustering algorithms.