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.
Highlights
Cell image segmentation is a process which differentiates cell regions from the background in images containing one or more cells
Our goal is to provide a systematic approach for cell image segmentation in terms of cell imaging, cell boundary detection, and separation of the clustered cells
To get the optimized cell boundary detection, a new approach which is referred to as contour expansion method is introduced in this paper
Summary
Cell image segmentation is a process which differentiates cell regions from the background in images containing one or more cells. It plays an important role in both fundamental biology research [1,2,3] and clinical applications [4] regarding cell morphology analysis and cell behavior characterization. Cell image segmentation is a crucial step for cell tracking, which is widely applied in characterizations of cell behaviors, including directed cell migration [8,9,10], wound healing [11], and tumor cell metastasis and invasion [12, 13].
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