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Proceedings Paper

Feature extraction for cellular shape analysis in high-content screening (HCS) applications
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Paper Abstract

Detailed information on cellular and sub-cellular interactions can be extracted from large-scale data sets through the application of image processing and analysis techniques from computer vision and pattern recognition. An automated, high-speed method for analysis of cellular systems in 2D includes boundary analysis of the cells and may be extended to texture (content) analysis or further. The overall goal of such analysis is to reach conclusions as to the physiological state and behavior of the cells. In this paper, we focus on shape analysis of cells, as shape is an effective factor for quantification of the many apparent physiological changes. We explore shape analysis techniques, including geometric (regular), Zernike, and Krawtchouk moment invariants. We also report on our investigation of the effects of resolution changes (in imaging systems) on the descriptors of cell shape in terms of stability and consistence of these moment invariants. Our results show that Krawtchouk moment invariants are better cell shape descriptors compared to geometric moment invariants in low resolution images.

Paper Details

Date Published: 29 March 2005
PDF: 12 pages
Proc. SPIE 5699, Imaging, Manipulation, and Analysis of Biomolecules and Cells: Fundamentals and Applications III, (29 March 2005); doi: 10.1117/12.597440
Show Author Affiliations
Bulent Bayraktar, Purdue Univ. (United States)
Bartek Rajwa, Purdue Univ. (United States)
J. Paul Robinson, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 5699:
Imaging, Manipulation, and Analysis of Biomolecules and Cells: Fundamentals and Applications III
Dan V. Nicolau; Dan V. Nicolau; Jörg Enderlein; Ramesh Raghavachari; Robert C. Leif; Daniel L. Farkas, Editor(s)

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