Probabilistic Brain Lesion Segmentation in DT-MRI

Lesion segmentation in MRI scans is used for lesion quantification as pertaining to various medical conditions. We propose a novel technique for chronic stroke lesion segmentation based on multiple modalities including T1-weighted and T2-weighted images as well as diffusion tensor-based modalities. The proposed approach is based on a mixture-parametric probabilistic model whereas the model parameters are optimized by maximizing the incomplete-data log-likelihood function through expectation maximization. The mixture components are selected to have Cauchy distributions thus facilitating efficient computation and increased robustness to noise. A probabilistic prior is computed by evaluating the feature vectors for a set of registered brain scans in a control set. Experimental results on actual clinical data demonstrate the effectiveness of the proposed approach.