Linear And Nonlinear Feature Extraction Methods Applied To Automatic Classification Of Proliferating Cells

W. Hobel; W. Abmayr; S. J. Poppl; W. Giaretti; P. Dormer

doi:10.1117/12.934663

1 November 1982 Linear And Nonlinear Feature Extraction Methods Applied To Automatic Classification Of Proliferating Cells

W. Hobel, W. Abmayr, S. J. Poppl, W. Giaretti, P. Dormer

Author Affiliations +

Proceedings Volume 0375, Medical Imaging and Image Interpretation; (1982) https://doi.org/10.1117/12.934663
Event: 1st International Symposium on Medical Imaging and Image Interpretation, 1982, Berlin, Germany

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.

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W. Hobel, W. Abmayr, S. J. Poppl, W. Giaretti, and P. Dormer "Linear And Nonlinear Feature Extraction Methods Applied To Automatic Classification Of Proliferating Cells", Proc. SPIE 0375, Medical Imaging and Image Interpretation, (1 November 1982); https://doi.org/10.1117/12.934663

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