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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 592790, 10 pages
http://dx.doi.org/10.1155/2013/592790
Research Article

An Entropy-Based Automated Cell Nuclei Segmentation and Quantification: Application in Analysis of Wound Healing Process

1Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
2Department of Biochemistry & Molecular Biology, Virginia Commonwealth University, Richmond, VA 23298, USA

Received 23 October 2012; Revised 22 January 2013; Accepted 26 January 2013

Academic Editor: Tianhai Tian

Copyright © 2013 Varun Oswal 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.

Abstract

The segmentation and quantification of cell nuclei are two very significant tasks in the analysis of histological images. Accurate results of cell nuclei segmentation are often adapted to a variety of applications such as the detection of cancerous cell nuclei and the observation of overlapping cellular events occurring during wound healing process in the human body. In this paper, an automated entropy-based thresholding system for segmentation and quantification of cell nuclei from histologically stained images has been presented. The proposed translational computation system aims to integrate clinical insight and computational analysis by identifying and segmenting objects of interest within histological images. Objects of interest and background regions are automatically distinguished by dynamically determining 3 optimal threshold values for the 3 color components of an input image. The threshold values are determined by means of entropy computations that are based on probability distributions of the color intensities of pixels and the spatial similarity of pixel intensities within neighborhoods. The effectiveness of the proposed system was tested over 21 histologically stained images containing approximately 1800 cell nuclei, and the overall performance of the algorithm was found to be promising, with high accuracy and precision values.