Hindawi Publishing Corporation International Journal of Biomedical Imaging Volume 2011, Article ID 874702, 16 pages doi:10.1155/2011/874702 Research Article Estimating Cell Count and Distribution in Labeled Histological Samples Using Incremental Cell Search Oscar E. Meruvia-Pastor, 1 Jung Soh, 2 Eric J. Schmidt, 3 Julia C. Boughner, 4 Mei Xiao, 2 Heather A. Jamniczky, 3 Benedikt Hallgr´ ımsson, 3 and Christoph W. Sensen 2 1 Department of Computer Science, Faculty of Science, Memorial University of Newfoundland, St John’s, NL, Canada A1B 3X5 2 Department of Biochemistry and Molecular Biology, Visual Genomics Centre, Faculty of Medicine, University of Calgary, Calgary, AB, Canada T2N 4N1 3 Department of Cell Biology and Anatomy, Faculty of Medicine, University of Calgary, Calgary, AB, Canada T2N 4N1 4 Department of Anatomy and Cell Biology, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada S7N 5E5 Correspondence should be addressed to Oscar E. Meruvia-Pastor, oscar@mun.ca Received 30 August 2010; Revised 15 February 2011; Accepted 11 March 2011 Academic Editor: Tiange Zhuang Copyright © 2011 Oscar E. Meruvia-Pastor et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Cell proliferation is critical to the outgrowth of biological structures including the face and limbs. This cellular process has traditionally been studied via sequential histological sampling of these tissues. The length and tedium of traditional sampling is a major impediment to analyzing the large datasets required to accurately model cellular processes. Computerized cell localization and quantification is critical for high-throughput morphometric analysis of developing embryonic tissues. We have developed the Incremental Cell Search (ICS), a novel software tool that expedites the analysis of relationships between morphological outgrowth and cell proliferation in embryonic tissues. Based on an estimated average cell size and stain color, ICS rapidly indicates the approximate location and amount of cells in histological images of labeled embryonic tissue and provides estimates of cell counts in regions with saturated fluorescence and blurred cell boundaries. This capacity opens the door to high-throughput 3D and 4D quantitative analyses of developmental patterns. 1. Introduction In many areas of biomedical research including clinical pathology, cell counts obtained from images are crucial data for diagnosing patients or for addressing hypotheses about developmental or pathological processes. Manual cell counting is challenging in that it typically requires a specialist such as a biologist or a pathologist to identify and characterize different cell types. Even then, manual cell counts are subjective. To date, several stereological tissue analysis methods have been developed with the aim of accurately estimating cell counts in a given tissue [1, 2]. However, traditional stereological techniques require elaborate production and sampling of representative tissue samples, which is both time consuming and labor intensive. Recent technological advances in microscopy now enable high-throughput imaging of thousands of cells in a short time [3–5]. Further, using high-throughput slide scanners allows the rapid collection of high-resolution data for serial sections [6]. These serial sections can then be reconstructed virtually in 3D. The 3D reconstructions can then be statis- tically analyzed using morphometry to quantify variation among samples. Manual cell counting would be a tedious and time-consuming approach for processing such large datasets and may even be subject to inaccuracies due to human error, as noted by several authors [4, 7–10]. In the context of the technological developments previously mentioned, computer-automated cell identification and counting could accelerate data collection. Importantly, this would greatly facilitate high-throughput histomorphometry, permitting large-scale studies of cellular processes that would not be feasible if done only by a manual process. A common challenge for computerized cell counting methods arises from the great variation in the morphology