Wavelet-based multiscale texture segmentation: Application to stromal compartment characterization on virtual slides

Authors:
Nicolas Signolle;Marinette Revenu;Benoít Plancoulaine;Paulette Herlin
Affiliations:
GREYC-ENSICAEN UMR CNRS 6072, 6 Boulevard Maréchal Juin, 14050 CAEN cedex, France and GREYC-ENSICAEN UMR CNRS 6072, 6 Boulevard Maréchal Juin, 14050 CAEN cedex, France;GREYC-ENSICAEN UMR CNRS 6072, 6 Boulevard Maréchal Juin, 14050 CAEN cedex, France;GREYC-ENSICAEN UMR CNRS 6072, 6 Boulevard Maréchal Juin, 14050 CAEN cedex, France;GREYC-ENSICAEN UMR CNRS 6072, 6 Boulevard Maréchal Juin, 14050 CAEN cedex, France
Venue:
Signal Processing
Year:
2010

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Abstract

We aim at segmenting very large images of histopathology virtual slides with an heterogeneous and complex content. To this end, we propose a multiscale framework for texture-based color image segmentation. The core of the method is based on a wavelet-domain hidden Markov tree model and a pairwise classifiers design and selection. The classifier selection is founded on a study of the influence of the hyperparameters of the method used. Over the testing set, majority vote was found to be the best way of combining outputs of the selected classifiers. The method is applied to the segmentation of various types of ovarian carcinoma stroma, on very large virtual slides. This is the first time such a segmentation is tested. The segmentation results are presented and discussed.