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Published in: Machine Vision and Applications 8/2017

05-08-2017 | Original Paper

Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos

Authors: F. Javier Sánchez, Jorge Bernal, Cristina Sánchez-Montes, Cristina Rodríguez de Miguel, Gloria Fernández-Esparrach

Published in: Machine Vision and Applications | Issue 8/2017

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Abstract

A novel specular highlights detection method in colonoscopy videos is presented. The method is based on a model of appearance defining specular highlights as bright spots which are highly contrasted with respect to adjacent regions. Our approach proposes two stages: segmentation and then classification of bright spot regions. The former defines a set of candidate regions obtained through a region growing process with local maxima as initial region seeds. This process creates a tree structure which keeps track, at each growing iteration, of the region frontier contrast; final regions provided depend on restrictions over contrast value. Non-specular regions are filtered through a classification stage performed by a linear SVM classifier using model-based features from each region. We introduce a new validation database with more than 25, 000 regions along with their corresponding pixel-wise annotations. We perform a comparative study against other approaches. Results show that our method is superior to other approaches, with our segmented regions being closer to actual specular regions in the image. Finally, we also present how our methodology can also be used to obtain an accurate prediction of polyp histology.

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Appendix
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Metadata
Title
Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos
Authors
F. Javier Sánchez
Jorge Bernal
Cristina Sánchez-Montes
Cristina Rodríguez de Miguel
Gloria Fernández-Esparrach
Publication date
05-08-2017
Publisher
Springer Berlin Heidelberg
Published in
Machine Vision and Applications / Issue 8/2017
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-017-0864-0

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