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Proceedings Paper

Development of a stained cell nuclei counting system
Author(s): Niranjan Timilsina; Christopher Moffatt; Kazunori Okada
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Paper Abstract

This paper presents a novel cell counting system which exploits the Fast Radial Symmetry Transformation (FRST) algorithm [1]. The driving force behind our system is a research on neurogenesis in the intact nervous system of Manduca Sexta or the Tobacco Hornworm, which was being studied to assess the impact of age, food and environment on neurogenesis. The varying thickness of the intact nervous system in this species often yields images with inhomogeneous background and inconsistencies such as varying illumination, variable contrast, and irregular cell size. For automated counting, such inhomogeneity and inconsistencies must be addressed, which no existing work has done successfully. Thus, our goal is to devise a new cell counting algorithm for the images with non-uniform background. Our solution adapts FRST: a computer vision algorithm which is designed to detect points of interest on circular regions such as human eyes. This algorithm enhances the occurrences of the stained-cell nuclei in 2D digital images and negates the problems caused by their inhomogeneity. Besides FRST, our algorithm employs standard image processing methods, such as mathematical morphology and connected component analysis. We have evaluated the developed cell counting system with fourteen digital images of Tobacco Hornworm's nervous system collected for this study with ground-truth cell counts by biology experts. Experimental results show that our system has a minimum error of 1.41% and mean error of 16.68% which is at least forty-four percent better than the algorithm without FRST.

Paper Details

Date Published: 11 March 2011
PDF: 12 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79620K (11 March 2011); doi: 10.1117/12.878437
Show Author Affiliations
Niranjan Timilsina, San Francisco State Univ. (United States)
Christopher Moffatt, San Francisco State Univ. (United States)
Kazunori Okada, San Francisco State Univ. (United States)


Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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