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Proceedings Paper

Toward automated quantification of biological microstructures using unbiased stereology
Author(s): Om Pavithra Bonam; Daniel Elozory; Kurt Kramer; Dmitry Goldgof; Lawrence O. Hall; Osvaldo Mangual; Peter R. Mouton
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Paper Abstract

Quantitative analysis of biological microstructures using unbiased stereology plays a large and growing role in bioscience research. Our aim is to add a fully automatic, high-throughput mode to a commercially available, computerized stereology device (Stereologer). The current method for estimation of first- and second order parameters of biological microstructures, requires a trained user to manually select objects of interest (cells, fibers etc.,) while stepping through the depth of a stained tissue section in fixed intervals. The proposed approach uses a combination of color and gray-level processing. Color processing is used to identify the objects of interest, by training on the images to obtain the threshold range for objects of interest. In gray-level processing, a region-growing approach was used to assign a unique identity to the objects of interest and enumerate them. This automatic approach achieved an overall object detection rate of 93.27%. Thus, these results support the view that automatic color and gray-level processing combined with unbiased sampling and assumption and model-free geometric probes can provide accurate and efficient quantification of biological objects.

Paper Details

Date Published: 9 March 2011
PDF: 8 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79633G (9 March 2011); doi: 10.1117/12.878710
Show Author Affiliations
Om Pavithra Bonam, Univ. of South Florida (United States)
Daniel Elozory, Univ. of South Florida (United States)
Kurt Kramer, Univ. of South Florida (United States)
Dmitry Goldgof, Univ. of South Florida (United States)
Lawrence O. Hall, Univ. of South Florida (United States)
Osvaldo Mangual, Univ. of South Florida (United States)
Peter R. Mouton, Univ. of South Florida School of Medicine (United States)

Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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