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A Novel Cell Segmentation Method and Cell Phase Identification Using Markov Model

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4 Author(s)
Xiaobo Zhou ; Bioinf. Program, Cornell Univ., Houston, TX ; Fuhai Li ; Jun Yan ; Wong, S.T.C.

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.

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Information Technology in Biomedicine, IEEE Transactions on  (Volume:13 ,  Issue: 2 )