A novel cell segmentation method and cell phase identification using Markov model

Authors:
Xiaobo Zhou;Fuhai Li;Jun Yan;Stephen T. C. Wong
Affiliations:
The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Houston TX and Department of Radiology, The Methodist Hospital, Weill Medical College, Cornell University, Hou ...;The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Houston TX and Department of Radiology, The Methodist Hospital, Weill Medical College, Cornell University, Hou ...;Harvard Center for Neurodegeneration and Repair, Center for Bioinformatics, Harvard Medical School, Boston, MA and Department of Radiology, Brigham and Women’s Hospital, Boston, MA;The Methodist Hospital Research Institute, Weill Medical College, Cornell University, Houston TX and Department of Radiology, The Methodist Hospital, Weill Medical College, Cornell University, Hou ...
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
2009

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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.