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Second order DTMR image segmentation using random walker

Image segmentation is a method of separating an image into regions of interest, such as separating an object from the background. The random walker image segmentation technique has been applied extensively to scalar images and has demonstrated robust results. In this paper we propose a novel method to apply the random walker method to segmenting non-scalar diffusion tensor magnetic resonance imaging (DT-MRI) data. Moreover, we used a non-parametric probability density model to provide estimates of the regional distributions enabling the random walker method to successfully segment disconnected objects. Our approach utilizes all the information provided by the tensors by using suitable dissimilarity tensor distance metrics. The method uses hard constraints for the segmentation provided interactively by the user, such that certain tensors are labeled as object or background. Then, a graph structure is created with the tensors representing the nodes and edge weights computed using the dissimilarity tensor distance metrics. The distance metrics used are the Log-Euclidean and the J-divergence. The results of the segmentations using these two different dissimilarity metrics are compared and evaluated. Applying the approach to both synthetic and real DT-MRI data yields segmentations that are both robust and qualitatively accurate.