Efficient statistical/morphological cell texture characterization and classification

Guillaume Thibault, Jesus Angulo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages2440-2443
Number of pages4
StatePublished - Dec 1 2012
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: Nov 11 2012Nov 15 2012

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other21st International Conference on Pattern Recognition, ICPR 2012
CountryJapan
CityTsukuba
Period11/11/1211/15/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Thibault, G., & Angulo, J. (2012). Efficient statistical/morphological cell texture characterization and classification. In ICPR 2012 - 21st International Conference on Pattern Recognition (pp. 2440-2443). [6460660] (Proceedings - International Conference on Pattern Recognition).