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A novel approach for quantitative assessment of mucosal damage in inflammatory bowel disease

Ismail I Matalka1, Faruq A Al-Omari2*, Rola M Salama1 and Alia H Mohtaseb1

Author Affiliations

1 Department of Pathology and Laboratory Medicine, Jordan University of Science and Technology, Irbid, Jordan

2 Computer Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan

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Diagnostic Pathology 2013, 8:156  doi:10.1186/1746-1596-8-156

Published: 20 September 2013



One of the main reliable histological features to suggest the diagnosis of inflammatory bowel disease is the presence of significant distortion of the crypt architecture indicating the chronic nature of the disease resulting in mucosal damage. This feature has a considerable intra-observer and inter-observer variability leading to significant subjectivity in colonic biopsy assessment. In this paper, we present a novel automated system to assess mucosal damage and architectural distortion in inflammatory bowel disease (IBD).


The proposed system relies on advanced image understating and processing techniques to segment digitally acquired images of microscopic biopsies, then, to extract key features to quantify the crypts irregularities in shape and distribution. These features were used as inputs to an artificial intelligent classifier that, after a training phase, can carry out the assessment automatically.


The developed system was evaluated using 118 IBD biopsies. 116 out of 118 biopsies were correctly classified as compared to the consensus of three expert pathologists, achieving an overall precision of 98.31%.


An automated intelligent system to quantitatively assess inflammatory bowel disease was developed. The proposed system utilized advanced image understanding techniques together with an intelligent classifier to conduct the assessment. The developed system proved to be reliable, robust, and minimizes subjectivity and inter- and intra-observer variability.

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Inflammatory bowel disease; Mucosal damage assessment; Morphometric features; Medical imaging; Artificial intelligence in medicine; Computer assessment in pathology