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Proceedings Paper

Linear And Nonlinear Feature Extraction Methods Applied To Automatic Classification Of Proliferating Cells
Author(s): W. Hobel; W. Abmayr; S. J. Poppl; W. Giaretti; P. Dormer
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Paper Abstract

Numerical experiments were performed to find optimum feature extraction procedures for the classification of mouse L-fibroblasts into Gl, S and G2 subpopulations. From images of these cells different feature sets such as geometric, densitometric, textural and chromatin features were derived which served as data base for the numerical experiments. Linear and nonlinear supervised stepwise learning techniques for the discrimination of the cells into Gl, S and G2 were performed. The classification error was used as criterion for the evaluation of the different numerical feature selection methods. Optimum results were obtained by combining distance based feature selection methods with nonlinear discriminant analysis. The successive solution of 2-class problems improves the results compared to the solution of the 3-class problem. Linear discriminant analysis then may surpass quadratic discriminant analysis.

Paper Details

Date Published: 1 November 1982
PDF: 6 pages
Proc. SPIE 0375, Medical Imaging and Image Interpretation, (1 November 1982); doi: 10.1117/12.934663
Show Author Affiliations
W. Hobel, Institut fur Medizinische Informatik und Systemforschung (Federal Republic of Germany)
W. Abmayr, Institut fur Strahlenschutz (Federal Republic of Germany)
S. J. Poppl, Institut fur Medizinische Informatik und Systemforschung (Federal Republic of Germany)
W. Giaretti, und Abteilung fur Experimentelle Hamatologie (Federal Republic of Germany)
P. Dormer, und Abteilung fur Experimentelle Hamatologie (Federal Republic of Germany)


Published in SPIE Proceedings Vol. 0375:
Medical Imaging and Image Interpretation
Judith M. S. Prewitt, Editor(s)

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