A novel geodesic distance based clustering approach to delineating boundaries of touching cells

Authors:
Xu Chen;Yanqiao Zhu;Fuhai Li;Zeyi Zheng;Eric Chang;Jinwen Ma;Stephen T. C. Wong
Affiliations:
Department of Information Science, School of Mathematical Sciences, Peking University, Beijing, China;Department of Information Science, School of Mathematical Sciences, Peking University, Beijing, China;NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, The Methodist Hospital Research Institute, Weill Cornell Medical College of Cornell University, Houst ...;Department of Molecular and Cellular Biology,The Breast Center, Baylor, College of Medicine, Houston, TX;Department of Molecular and Cellular Biology,The Breast Center, Baylor, College of Medicine, Houston, TX;Department of Information Science, School of Mathematical Sciences, Peking University, Beijing, China;NCI Center for Modeling Cancer Development, Department of Systems Medicine and Bioengineering, The Methodist Hospital Research Institute, Weill Cornell Medical College of Cornell University, Houst ...
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Year:
2013

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Abstract

In this paper, we propose a novel geodesic distance based clustering approach for delineating boundaries of touching cells. In specific, the Riemannian metric is firstly adopted to integrate the spatial distance and intensity variation. Then the distance between any two given pixels under this metric is computed as the geodesic distance in a propagational way, and the K-means-like algorithm is deployed in clustering based on the propagational distance. The proposed method was validated to segment the touching Madin-Darby Canine Kidney (MDCK) epithelial cell images for measuring their N-Ras protein expression patterns inside individual cells. The experimental results and comparisons demonstrate the advantages of the proposed method in massive cell segmentation and robustness to the initial seeds selection, varying intensity contrasts and high cell densities in microscopy images.