New data model for graph-cut segmentation: application to automatic melanoma delineation

Razmig Kéchichian 1 Hao Gong 1 Marinette Revenu 2 Olivier Lézoray 2 Michel Desvignes 1
GIPSA-DIS - Département Images et Signal
2 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : We propose a new data model for graph-cut image segmentation, defined according to probabilities learned by a classification process. Unlike traditional graph-cut methods, the data model takes into account not only color but also texture and shape information. For melanoma images, we also introduce skin chromophore features and automatically derive "seed" pixels used to train the classifier from a coarse initial segmentation. On natural images, our method successfully segments objects having similar color but different texture. Its application to melanoma delineation compares favorably to manual delineation and related graph-cut segmentation methods.
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Submitted on : Tuesday, November 4, 2014 - 12:34:53 PM
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Razmig Kéchichian, Hao Gong, Marinette Revenu, Olivier Lézoray, Michel Desvignes. New data model for graph-cut segmentation: application to automatic melanoma delineation. 21st IEEE International Conference on Image Processing (ICIP 2014), Oct 2014, Paris, France. ⟨hal-01080049⟩



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