Toward automated quantification of biological microstructures using unbiased stereology

Om Pavithra Bonam; Daniel Elozory; Kurt Kramer; Dmitry Goldgof; Lawrence O. Hall; Osvaldo Mangual; Peter R. Mouton

doi:10.1117/12.878710

9 March 2011 Toward automated quantification of biological microstructures using unbiased stereology

Om Pavithra Bonam, Daniel Elozory, Kurt Kramer, Dmitry Goldgof, Lawrence O. Hall, Osvaldo Mangual, Peter R. Mouton

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 79633G (2011) https://doi.org/10.1117/12.878710
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

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.

Citation Download Citation

Om Pavithra Bonam, Daniel Elozory, Kurt Kramer, Dmitry Goldgof, Lawrence O. Hall, Osvaldo Mangual, and Peter R. Mouton "Toward automated quantification of biological microstructures using unbiased stereology", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79633G (9 March 2011); https://doi.org/10.1117/12.878710

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