Automated microscopy for lymph node cancer diagnosis

Lawrence M. Firestone

doi:10.1117/12.154951

17 September 1993 Automated microscopy for lymph node cancer diagnosis

Lawrence M. Firestone

Proceedings Volume 1894, Clinical Applications of Modern Imaging Technology; (1993) https://doi.org/10.1117/12.154951
Event: OE/LASE'93: Optics, Electro-Optics, and Laser Applications in Scienceand Engineering, 1993, Los Angeles, CA, United States

Abstract

A Coulter diff3/50 research microscope under computer control was used to digitize biopsy slides at differing resolutions and in full color. Then, a broad set of candidate features were extracted using color analysis, template matching, statistical texture analysis, frequency domain techniques, and surface modeling by both cellular logic filters and relative extrema analysis. In all, over 600 candidate features were measured for selection and classifier design. Standard pattern recognition techniques for classifier design assume that objects cluster into distinct classes. For the lymphoma problem, where the classes (subtypes) form a continuum based on the percentage of large cells, this discrete class assumption does not apply. Estimation theoretic techniques were combined with pattern recognition for this project to design classifiers that exploit the continuous nature of lymphoma subtyping.

Citation Download Citation

Lawrence M. Firestone "Automated microscopy for lymph node cancer diagnosis", Proc. SPIE 1894, Clinical Applications of Modern Imaging Technology, (17 September 1993); https://doi.org/10.1117/12.154951

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