Loading [MathJax]/extensions/MathMenu.js
Impact of Visual Features on the Segmentation of Gastroenterology Images Using Normalized Cuts | IEEE Journals & Magazine | IEEE Xplore

Impact of Visual Features on the Segmentation of Gastroenterology Images Using Normalized Cuts


Abstract:

Gastroenterology imaging is an essential tool to detect gastrointestinal cancer in patients. Computer-assisted diagnosis is desirable to help us improve the reliability o...Show More

Abstract:

Gastroenterology imaging is an essential tool to detect gastrointestinal cancer in patients. Computer-assisted diagnosis is desirable to help us improve the reliability of this detection. However, traditional computer vision methodologies, mainly segmentation, do not translate well to the specific visual characteristics of a gastroenterology imaging scenario. In this paper, we propose a novel method for the segmentation of gastroenterology images from two distinct imaging modalities and organs: chromoendoscopy (CH) and narrow-band imaging (NBI) from stomach and esophagus, respectively. We have used various visual features individually and their combinations (edgemaps, creaseness, and color) in normalized cuts image segmentation framework to segment ground truth datasets of 142 CH and 224 NBI images. Experiments show that an integration of edgemaps and creaseness in normalized cuts gives the best segmentation performance resulting in high-quality segmentations of the gastroenterology images.
Published in: IEEE Transactions on Biomedical Engineering ( Volume: 60, Issue: 5, May 2013)
Page(s): 1191 - 1201
Date of Publication: 27 November 2012

ISSN Information:

PubMed ID: 23204269

References

References is not available for this document.