A supervised learning based approach to detect Crohn's disease in abdominal MR volumes

Abstract

Accurate diagnosis of Crohn's disease (CD) has emerged as an important medical challenge. Because current Magnetic resonance imaging (MRI) analysis approaches rely on extensive manual segmentation for an accurate analysis, we propose a method for the automatic identification and localization of regions in abdominal MR volumes that have been affected by CD. Our proposed approach will serve to augment results from colonoscopy, the current reference standard for CD diagnosis. Intensity statistics, texture anisotropy and shape asymmetry of the 3D regions are used as features to distinguish between diseased and normal regions. Particular emphasis is laid on a novel entropy based asymmetry calculation method. Experiments on real patient data show that our features achieve a high level of accuracy and perform better than two competing methods. © 2012 Springer-Verlag.