Localizing and segmenting crohn's disease affected regions in abdominal MRI using novel context features

Increasing incidence of Crohn's disease (CD) in the Western world has made its accurate diagnosis an important medical challenge. The current reference standard for diagnosis, colonoscopy, is time consuming and invasive due to which Magnetic resonance imaging (MRI) has emerged as the preferred non-invasive procedure over colonoscopy. Current MRI approaches rely on extensive manual segmentation for an accurate analysis thus limiting their effectiveness. We propose a supervised learning method for the localization and segmentation of regions in abdominal MR images that have been affected by CD. Higher order statistics from intensity and texture are used with context information to distinguish between diseased and normal regions. Particular emphasis is laid on a novel measure to derive context information. Experiments on real patient data show that our features achieve high sensitivity and can successfully segment out the pixels belonging to CD affected regions.