title: Extraction of color features in the spectral domain to recognize centroblasts in histopathology.
abstract: In this paper, we are proposing a novel automated method to recognize centroblast (CB) cells from non-centroblast (non-CB) cells for computer-assisted evaluation of follicular lymphoma tissue samples. The method is based on training and testing of a quadratic discriminant analysis (QDA) classifier. The novel aspects of this method are the identification of the CB object with prior information, and the introduction of the principal component analysis (PCA) in the spectral domain to extract color texture features. Both geometric and texture features are used to achieve the classification. Experimental results on real follicular lymphoma images demonstrate that the combined feature space improved the performance of the system significantly. The implemented method can identify centroblast cells (CB) from non-centroblast cells (non-CB) with a classification accuracy of 82.56%.
authors: Department of Biomedical Informatics, The Ohio State University, Columbus, 43210, USA. Kamel.Boussaid@osumc.edu
authors: Kamel
authors: K
authors: Belkacem-Boussaid
authors: Olcay
authors: O
authors: Sertel
authors: Gerard
authors: G
authors: Lozanski
authors: Arwa
authors: A
authors: Shana'aah
authors: Metin
authors: M
authors: Gurcan
mesh_terms: D000465:Algorithms; D003116:Color; D003584:Cytological Techniques; D003593:Cytoplasm / Q000378:metabolism; D016002:Discriminant Analysis; D006652:Histological Techniques; D006653:Histology / Q000295:instrumentation*; D006801:Humans; D008223:Lymphoma / Q000378:metabolism / Q000473:pathology*; D008224:Lymphoma, Follicular / Q000175:diagnosis* / Q000473:pathology*; D015233:Models, Statistical; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D015203:Reproducibility of Results