Efficient statistical/morphological cell texture characterization and classification

Abstract : This paper presents the different steps for an automatic fluorescence-labelled cell classification method. First a data features study is discussed in order to describe cell texture by means of morphological and statistical texture descriptors. Then, results on supervised classification using logistic regression, random forest and neural networks, for both morphological and statistical descriptors, is presented. We propose a final consolidated classifier based on a weighted probability for each class, where the weights are given by the empirical classification performances. The method is evaluated on ICPR'12 HEp-2 dataset contest.
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Communication dans un congrès
21th International Conference on Pattern Recognition (ICPR), Nov 2012, Tsukuba, Japan. IEEE, pp.2440-2443, 2012
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Guillaume Thibault, Jesus Angulo. Efficient statistical/morphological cell texture characterization and classification. 21th International Conference on Pattern Recognition (ICPR), Nov 2012, Tsukuba, Japan. IEEE, pp.2440-2443, 2012. 〈hal-00834446〉

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