Bleeding detection in Wireless Capsule Endoscopy using adaptive colour histogram model and support vector classification

Mackiewicz, Michal W., Fisher, Mark H. and Jamieson, Crawford (2008) Bleeding detection in Wireless Capsule Endoscopy using adaptive colour histogram model and support vector classification. In: Proc. SPIE Medical Imaging, 2008-02-16 - 2008-02-21.

Full text not available from this repository. (Request a copy)

Abstract

Wireless Capsule Endoscopy (WCE) is a colour imaging technology that enables detailed examination of the interior of the gastrointestinal tract. A typical WCE examination takes ~ 8 hours and captures ~ 40,000 useful images. After the examination, the images are viewed as a video sequence, which generally takes a clinician over an hour to analyse. The manufacturers of the WCE provide certain automatic image analysis functions e.g. Given Imaging offers in their Rapid Reader software: The Suspected Blood Indicator (SBI), which is designed to report the location in the video of areas of active bleeding. However, this tool has been reported to have insufficient specificity and sensitivity. Therefore it does not free the specialist from reviewing the entire footage and was suggested only to be used as a fast screening tool. In this paper we propose a method of bleeding detection that uses in its first stage Hue-Saturation-Intensity colour histograms to track a moving background and bleeding colour distributions over time. Such an approach addresses the problem caused by drastic changes in blood colour distribution that occur when it is altered by gastrointestinal fluids and allow detection of other red lesions, which although are usually "less red" than fresh bleeding, they can still be detected when the difference between their colour distributions and the background is large enough. In the second stage of our method, we analyse all candidate blood frames, by extracting colour (HSI) and texture (LBP) features from the suspicious image regions (obtained in the first stage) and their neighbourhoods and classifying them using Support Vector Classifier into Bleeding, Lesion and Normal classes. We show that our algorithm compares favourably with the SBI on the test set of 84 full length videos.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	From Conference Volume 6914 Medical Imaging 2008: Image Processing Joseph M. Reinhardt; Josien P. W. Pluim San Diego, CA | February 16, 2008
Faculty \ School:	Faculty of Science > School of Computing Sciences
Depositing User:	EPrints Services
Date Deposited:	01 Oct 2010 13:41
Last Modified:	12 Jan 2023 16:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/3131
DOI:	10.1117/12.770510

Actions (login required)

View Item