Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorith...

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Bibliographic Details
Published in:PloS one 2015-06, Vol.10 (6), p.e0130178-e0130178
Main Authors: Wang, Yuliang, Zhang, Zaicheng, Wang, Huimin, Bi, Shusheng
Format: Article
Language:English
Subjects:
Algorithms
Biological effects
Boundaries
Cell Count
Cell Line
Cell Shape - physiology
Cell Size
Field of view
Humans
Image contrast
Image detection
Image processing
Image segmentation
International conferences
Light
Light intensity
Luminous intensity
Mechanical engineering
Medical imaging
Methods
Microscopy
Microscopy, Phase-Contrast - methods
Morphology
Phase contrast
Reproducibility of Results
Robotics
Segmentation
Therapeutic applications
Time-Lapse Imaging - methods
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Summary:Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0130178