Automatic MS Lesion Segmentation by Outlier Detection and Information Theoretic RegionPartitioning

Multiple Sclerosis (MS) is a neurodegenerative disease that is associated with brain tissue damage primarily observed as white matter abnormalities such as lesions. We present a novel, fully automatic segmentation method for MS lesions in brain MRI that combines outlier detection and region partition-ing. The method is based on an atlas of healthy subjects and detects lesions as outliers, without requiring the use of training data with segmented lesions. In order to segment lesions as spatially coherent objects and avoid spurious lesion detection, we perform classification on regions (connected groups of voxels) instead of individual voxels. Each voxel location is assigned to a region that would maximize overall relative entropy or Kullback-Leibler divergence between neighboring regions. Our proposed method is fully automatic and does not require manual selection or outlining of specific brain regions. The method can also be adapted to MR images obtained from different scanners and scanning parameters a