Characterization of biological processes through automated image analysis.
The systems-level analysis of complex biological processes requires methods that enable the quantification of a broad range of phenotypical alterations, the precise localization of signaling events, and the ability to correlate such signaling events in the context of the spatial organization of the biological specimen. The goal of this review is to illustrate that, when combined with modern imaging platforms and labeling techniques, automated image analysis methods can provide such quantitative information. The article attempts to review necessary image analysis techniques as well as applications that utilize these techniques to provide the data that will enable systems-level biology. The text includes a review of image registration and image segmentation methods, as well as algorithms that enable the analysis of cellular architecture, cell morphology, and tissue organization. Various methods that enable the analysis of dynamic events are also presented.
Rittscher
Jens
J
Visualization and Computer Vision Laboratory, GE Global Research, Niskayuna, New York 12309, USA. jens.rittscher@research.ge.com
D000465:Algorithms; D000818:Animals; D001686:Biological Phenomena; D001921:Brain / Q000166:cytology / Q000196:embryology; D002477:Cells / Q000648:ultrastructure*; D004175:Diptera / Q000166:cytology / Q000196:embryology; D006321:Heart / Q000196:embryology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D013569:Synapses / Q000648:ultrastructure; D015027:Zebrafish / Q000196:embryology