Exploiting spatial information to enhance DTI segmentations via spatial fuzzy c-means with covariance matrix data and non-Euclidean metrics

A diffusion tensor models the covariance of the Brownian motion of water at a voxel and is required to be symmetric and positive semi-definite. Therefore, image processing approaches, designed for linear entities, are not effective for diffusion tensor data manipulation, and the existence of artifacts in diffusion tensor imaging acquisition makes diffusion tensor data segmentation even more challenging. In this study, a spatial fuzzy c-means clustering method for diffusion tensor data which effectively segments diffusion tensor images by accounting for the noise, partial voluming, magnetic field inhomogeneity and other imaging artifacts, is developed. To retain the symmetry and positive semi-definiteness of diffusion tensors, the log and root Euclidean metrics are used to estimate the mean diffusion tensor for each cluster. The method exploits spatial contextual information and provides uncertainty information in segmentation decisions by calculating the membership values for assigning a diffusion tensor at one voxel to different clusters. A regularisation model which allows the user to integrate their prior knowledge into the segmentation scheme or to highlight and segment local structures is also proposed. Experiments on simulated images and real brain datasets from healthy and Spinocerebellar ataxia 2 subjects show that the new method is more effective than conventional segmentation methods.