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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This work was supported by the Spanish Government through the funded project iVENDIS (DPI2015-65286-R), by the FSEED, by the Secretaria d’Universitats i Recerca de la Generalitat de Catalunya, 2014-SGR-1470 and 2014-SGR-135 and by CERCA Programme / Generalitat de Catalunya.
Appendix: Bright spot regions segmentation algorithms
Appendix: Bright spot regions segmentation algorithms
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Sánchez, F.J., Bernal, J., Sánchez-Montes, C. et al. Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos. Machine Vision and Applications 28, 917–936 (2017). https://doi.org/10.1007/s00138-017-0864-0
- Specular highlights
- Bright spot regions segmentation
- Region classification