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 setsthrough the application of image processing and analysis techniques from computer vision and patternrecognition. An automated, high-speed method for analysis of cellular systems in 2D includes boundaryanalysis of the cells and may be extended to texture (content) analysis or further. The overall goal of suchanalysis is to reach conclusions as to the physiological state and behavior of the cells. In this paper, we focuson shape analysis of cells, as shape is an effective factor for quantification of the many apparentphysiological changes. We explore shape analysis techniques, including geometric (regular), Zernike, andKrawtchouk moment invariants. We also report on our investigation of the effects of resolution changes (inimaging systems) on the descriptors of cell shape in terms of stability and consistence of these momentinvariants. Our results show that Krawtchouk moment invariants are better cell shape descriptors comparedto geometric moment invariants in low resolution images.