Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images.
Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images.
复制标题
在负相差图像中通过优化边界检测对簇状细胞进行分割
DOI：
10.1371/journal.pone.0130178
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Bi S
中科院分区：
文献类型：
--
作者：
Wang Y;Zhang Z;Wang H;Bi S
细胞图像分割在许多生物学研究和临床应用中起着核心作用。因此，开发具有高鲁棒性和准确性的细胞图像分割算法越来越受到人们的关注。本研究开发了一种自动细胞图像分割算法，从细胞边界检测和对负相差图像视场内所有细胞的聚类细胞分割两方面改进了细胞图像分割。提出了一种结合阈值法和基于边缘的活动轮廓法的细胞边界检测优化方法。为了分割聚类细胞，利用细胞光强的地理峰来检测聚类细胞的数量和位置。本文介绍了算法的工作原理。研究了细胞边界检测参数和阈值的选取对最终分割结果的影响。最后，将该算法应用于不同实验的负相对比图像。最后对该方法的性能进行了评价。结果表明，该方法可以实现优化的细胞边界检测和对聚类细胞的高精度分割。
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.
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影响因子：
3.7
作者：
Bhardwaj A;Rosen D;Liu M;Liu Y;Hao Q;Ganesan N;Etzel CJ;Gullett A;Albarracin CT;Bedrosian I
通讯作者：
Bedrosian I
影响因子：
3
作者：
Neff KL;Argue DP;Ma AC;Lee HB;Clark KJ;Ekker SC
通讯作者：
Ekker SC
影响因子：
3.4
作者：
Lo, CM;Wang, HB;Wang, YL
通讯作者：
Wang, YL
影响因子：
19.5
作者：
KASS, M;WITKIN, A;TERZOPOULOS, D
通讯作者：
TERZOPOULOS, D
影响因子：
4.6
作者：
Cheng, Jierong;Rajapakse, Jagath C.
通讯作者：
Rajapakse, Jagath C.