Adaptive feature selection for classification of microscope images

Authors:
Ralf Tautenhahn;Alexander Ihlow;Udo Seiffert
Affiliations:
Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany;Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Gatersleben, Germany;Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Gatersleben, Germany
Venue:
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Year:
2005

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Abstract

For high-throughput screening of genetically modified plant cells, a system for the automatic analysis of huge collections of microscope images is needed to decide whether the cells are infected with fungi or not. To study the potential of feature based classification for this application, we compare different classifiers (kNN, SVM, MLP, LVQ) combined with several feature reduction techniques (PCA, LDA, Mutual Information, Fisher Discriminant Ratio, Recursive Feature Elimination). We achieve a significantly higher classification accuracy using a reduced feature vector instead of the full length feature vector.