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A supervised learning approach for Crohn's disease detection using higher-order image statistics and a novel shape asymmetry measure.

Authors
Type
Published Article
Journal
Journal of Digital Imaging
1618-727X
Publisher
Springer-Verlag
Publication Date
Volume
26
Issue
5
Pages
920–931
Identifiers
DOI: 10.1007/s10278-013-9576-9
PMID: 23392736
Source
Medline

Abstract

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 while magnetic resonance imaging (MRI) has emerged as the preferred noninvasive procedure over colonoscopy. Current MRI approaches assess rate of contrast enhancement and bowel wall thickness, and rely on extensive manual segmentation for accurate analysis. We propose a supervised learning method for the identification and localization of regions in abdominal magnetic resonance images that have been affected by CD. Low-level features like intensity and texture are used with shape asymmetry information to distinguish between diseased and normal regions. Particular emphasis is laid on a novel entropy-based shape asymmetry method and higher-order statistics like skewness and kurtosis. Multi-scale feature extraction renders the method robust. Experiments on real patient data show that our features achieve a high level of accuracy and perform better than two competing methods.

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