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

摘要

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.