Toward automated quantification of biological microstructures using unbiased stereology

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.