title: Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images.
abstract: 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.
authors.affiliation: Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. China.
authors.forename: Yuliang
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. China.
authors.forename: Zaicheng
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Department of Materials Science and Engineering, The Ohio State University, 2041 College Rd., Columbus, Ohio 43210, United States of America.
authors.forename: Huimin
authors.initials: H
authors.lastname: Wang
authors.affiliation: Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, P.R. China.
authors.forename: Shusheng
authors.initials: S
authors.lastname: Bi
mesh_terms: D000465:Algorithms; D002452:Cell Count; D002460:Cell Line; D048430:Cell Shape / Q000502:physiology*; D048429:Cell Size; D006801:Humans; D008858:Microscopy, Phase-Contrast / Q000379:methods*; D015203:Reproducibility of Results; D059008:Time-Lapse Imaging / Q000379:methods*