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Texture-based multiscale segmentation: Application to stromal compartment characterization on ovarian carcinoma virtual slides

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2008) 5099 LNCS 173-182
DOI: 10.1007/978-3-540-69905-7_20
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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. © 2008 Springer-Verlag.

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Signolle, N., Plancoulaine, B., Herlin, P., & Revenu, M. (2008). Texture-based multiscale segmentation: Application to stromal compartment characterization on ovarian carcinoma virtual slides. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5099 LNCS, pp. 173–182). https://doi.org/10.1007/978-3-540-69905-7_20

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