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TitleAutomatic segmentation and detection of small bowel angioectasias in WCE images
Author(s)Vieira, Pedro M.
Pinto, Catarina
Costa, Dalila
Vaz, A. Ismael F.
Rolanda, Carla
Lima, Carlos S.
Gastrointestinal Hemorrhage
Image Interpretation, Computer-Assisted
Capsule Endoscopy
Intestine, Small
EM Segmentation
Machine learning
Markov Random Fields
Issue date2019
JournalAnnals of Biomedical Engineering
Abstract(s)Angioectasias are lesions that occur in the blood vessels of the bowel and are the cause of more than 8% of all gastrointestinal bleeding episodes. They are usually classified as bleeding related lesions, however current state-of-the-art bleeding detection algorithms present low sensitivity in the detection of these lesions. This paper proposes a methodology for the automatic detection of angioectasias in wireless capsule endoscopy (WCE) videos. This method relies on the automatic selection of a region of interest, selected by using an image segmentation module based on the Maximum a Posteriori (MAP) approach where a new accelerated version of the Expectation-Maximization (EM) algorithm is also proposed. Spatial context information is modeled in the prior probability density function by using Markov Random Fields with the inclusion of a weighted boundary function. Higher order statistics computed in the CIELab color space with the luminance component removed and intensity normalization of high reflectance regions, showed to be effective features regarding angioectasia detection. The proposed method outperforms some current state of the art algorithms, achieving sensitivity and specificity values of more than 96% in a database containing 800 WCE frames labeled by two gastroenterologists.
AccessRestricted access (Author)
Appears in Collections:ICVS - Artigos em revistas internacionais / Papers in international journals
MEtRICs - Artigos em revistas internacionais/Papers in international journals

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