Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos
Sánchez, F. Javier (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Bernal del Nozal, Jorge (Centre de Visió per Computador)
Sánchez-Montes, Cristina (Hospital Clínic i Provincial de Barcelona)
Rodríguez De Miguel, Cristina (Hospital Clínic i Provincial de Barcelona)
Fernández-Esparrach, Gloria (Hospital Clínic i Provincial de Barcelona)

Date:	2017
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.
Grants:	Ministerio de Economía y Competitividad DPI2015-65286-R Generalitat de Catalunya 2014/SGR-1470 Generalitat de Catalunya 2014/SGR-135
Note:	Altres ajuts: CERCA Programme / Generalitat de Catalunya.
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Language:	Anglès
Document:	Article ; recerca ; Versió acceptada per publicar
Subject:	Bright spot regions segmentation ; Colonoscopy ; Region classification ; Specular highlights
Published in:	Machine vision and applications, Vol. 28, Num. 8 (2017) , p. 917-936, ISSN 1432-1769

DOI: 10.1007/s00138-017-0864-0

Postprint 18 p, 5.0 MB

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