TY - GEN
T1 - Efficient statistical/morphological cell texture characterization and classification
AU - Thibault, Guillaume
AU - Angulo, Jesus
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84874574883&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:84874574883
SN - 9784990644109
T3 - Proceedings - International Conference on Pattern Recognition
SP - 2440
EP - 2443
BT - ICPR 2012 - 21st International Conference on Pattern Recognition
T2 - 21st International Conference on Pattern Recognition, ICPR 2012
Y2 - 11 November 2012 through 15 November 2012
ER -