Top-Down Segmentation of Histological Images Using a Digital Deformable Model

Authors:
F. Vieilleville;J. -O. Lachaud;P. Herlin;O. Lezoray;B. Plancoulaine
Affiliations:
Laboratoire de Mathématiques, UMR CNRS 5127, Université de Savoie, Le-Bourget-du-Lac, France 73776;Laboratoire de Mathématiques, UMR CNRS 5127, Université de Savoie, Le-Bourget-du-Lac, France 73776;GREYCAN, Centre François Baclesse, Caen cedex 5, France 14076;GREYC 6 Boulevard du Maréchal Juin, Caen Cedex, France 14050;GREYCAN, Centre François Baclesse, Caen cedex 5, France 14076
Venue:
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Year:
2009

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Abstract

This paper presents a straightforward top-down segmentation method based on a contour approach on histological images. Our approach relies on a digital deformable model whose internal energy is based on the minimum length polygon and that uses a greedy algorithm to minimise its energy. Experiments on real histological images of breast cancer yields results as good as that of classical active contours.