title: Features for cells and nuclei classification.
abstract: The performance of automated analysis of cellular images is heavily influenced by the features that characterize cells or cell nuclei. In this paper, an exhaustive set of features including morphological, topological, and texture features are explored to determine the optimal features for classification of cells and cell nuclei. The optimal subset of features are obtained using popular feature selection methods. The results of feature selection indicate that Zernike moment, Daubechies wavelets, and Gabor wavelets give the most important features for the classification of cells or cell nuclei in fluorescent microscopy images.
authors.affiliation: BioInformatics Research Centre, School of Computer Engineering, Nanyang Technological university, Singapore. y060101@e.ntu.edu.sg
authors.forename: Song
authors.initials: S
authors.lastname: Liu
authors.forename: Piyushkumar A
authors.initials: PA
authors.lastname: Mundra
authors.forename: Jagath C
authors.initials: JC
authors.lastname: Rajapakse
mesh_terms: D000465:Algorithms; D000818:Animals; D003585:Cell Biology; D002453:Cell Cycle; D002467:Cell Nucleus / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods; D003584:Cytological Techniques / Q000379:methods*; D006367:HeLa Cells; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008856:Microscopy, Fluorescence / Q000379:methods; D015233:Models, Statistical; D015203:Reproducibility of Results; D012984:Software