Multi-channel features based automated segmentation of diffusion tensor imaging using an improved FCM with spatial constraints

Abstract

The aim of this study is to design an improved FCM with constraints algorithm (iFCM_S) for brain tissue segmentation based on diffusion tensor imaging (DTI). The fuzzy c-means clustering algorithm has been widely used in many medical image segmentations. However, the conventionally standard FCM algorithm is sensitive to noise because of not taking into account the membership function information and the spatial contextual information. To overcome this problem, an improved FCM with spatial constraints algorithm for image segmentation is presented in this paper, which is formulated by modifying the objective function and the membership function of the standard fuzzy c-means algorithm to enhance the noise immunity. In addition, due to multi-channel features of DTI data providing more information to the tissue segmentation comparing to single channel feature, we use the proposed algorithm on the multi-channel features of DTI to implement brain tissue segmentation. The experiments on both simulated images and real-world datasets show that our proposed method is more effective than the conventional segmentation methods.