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Design-based (unbiased) stereology provides an accurate, precise, and efficient method to quantify morphological parameters of biological microstructures, such as the total number of three-dimensional (3D) objects (cells) in stained tissue sections. The current requirement for extensive user interaction with commercially available computerized stereology systems limits the throughput of data collection. To increase the efficiency of this process, an algorithm was developed to automate data collection from stained objects in thick, transparent tissue sections. We present a novel approach to extract, count and classify stained objects of interest in 3D by linking them through a z-stack of images. Skeletonization and erosion are used to further segment the under segmented (overlapping) cells resulting from the extraction of out of focus cells in conjunction with in focus cells. Finally, 3D shape features, computed from the re-linked cells, are used for final classification of counted objects into “cells” and “not-cells”. We achieve a classification accuracy of 85% using SVM in a leave one-out experiment. The results demonstrate the effectiveness of our algorithm to count cells in 3D from thick, transparent tissue sections.