Texture-Based Multiscale Segmentation: Application to Stromal Compartment Characterization on Ovarian Carcinoma Virtual Slides

Authors:
Nicolas Signolle;Benoît Plancoulaine;Paulette Herlin;Marinette Revenu
Affiliations:
GREYC ENSICAEN UMR 6072, CAEN, France 14050 and GRECAN EA 1772, iFR 146 ICORE, Université de Caen, CLCC François Baclesse, CAEN cedex 5, France 14076;GRECAN EA 1772, iFR 146 ICORE, Université de Caen, CLCC François Baclesse, CAEN cedex 5, France 14076;GRECAN EA 1772, iFR 146 ICORE, Université de Caen, CLCC François Baclesse, CAEN cedex 5, France 14076;GREYC ENSICAEN UMR 6072, CAEN, France 14050
Venue:
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Year:
2008

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Abstract

A multiscale segmentation strategy using wavelet-domain hidden Markov tree model and pairwise classifiers selection is tested in the present paper for histopathology virtual slide analysis. The classifiers selection is based on a study of the influence of hyper-parameters of the method. Combination of outputs of selected classifiers is then done with majority vote. The results of the segmentation of various types of stroma of ovarian carcinomas are presented and discussed.