Unsupervised Segmentation of Colonic Polyps in NBI Images based on Wasserstein distancesCP2

We propose an automatic and unsupervised method for the segmentation of colonic polyps for in vivo Narrow-Band-Imaging (NBI) data, during optical colonoscopy, aiming at the prevention of colon cancer. The method is based on the Chan & Vese segmentation model and involves the sum of different Wasserstein distances, relying on histograms of suitable image descriptors, such as the intensity, texture, scale and orientation.

This presentation is part of Contributed Presentation “CP2 - Contributed session 2

Isabel Figueiredo (University of Coimbra)
Luís Pinto (Department of Mathematics, University of Coimbra)
Pedro Figueiredo (Faculty of Medicine, University of Coimbra and Department of Gastroenterology, CHUC, Coimbra)
Richard Tsai (Department of Mathematics, University of Texas at Austin, and KTH Royal Institute of Technology, Sweden.)
image segmentation