title: A novel cell segmentation method and cell phase identification using Markov model.
abstract: Optical microscopy is becoming an important technique in drug discovery and life science research. The approaches used to analyze optical microscopy images are generally classified into two categories: automatic and manual approaches. However, the existing automatic systems are rather limited in dealing with large volume of time-lapse microscopy images because of the complexity of cell behaviors and morphological variance. On the other hand, manual approaches are very time-consuming. In this paper, we propose an effective automated, quantitative analysis system that can be used to segment, track, and quantize cell cycle behaviors of a large population of cells nuclei effectively and efficiently. We use adaptive thresholding and watershed algorithm for cell nuclei segmentation followed by a fragment merging method that combines two scoring models based on trend and no trend features. Using the context information of time-lapse data, the phases of cell nuclei are identified accurately via a Markov model. Experimental results show that the proposed system is effective for nuclei segmentation and phase identification.
authors.affiliation: The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Houston, TX 77030, USA. xzhou@tmhs.org
authors.forename: Xiaobo
authors.initials: X
authors.lastname: Zhou
authors.forename: Fuhai
authors.initials: F
authors.lastname: Li
authors.forename: Jun
authors.initials: J
authors.lastname: Yan
authors.forename: Stephen T C
authors.initials: ST
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D002453:Cell Cycle; D002467:Cell Nucleus / Q000502:physiology / Q000648:ultrastructure*; D006367:HeLa Cells; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D008856:Microscopy, Fluorescence; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*