A Novel Geodesic Distance Based Clustering Approach to Delineating Boundaries of Touching Cells

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.

[1]  Meng Wang,et al.  Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy , 2008, Bioinform..

[2]  Erik H. W. Meijering,et al.  Cell Segmentation: 50 Years Down the Road [Life Sciences] , 2012, IEEE Signal Processing Magazine.

[3]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[4]  S. Coleman,et al.  Near-Circular Corner and Edge Detection Operators , 2007, International Machine Vision and Image Processing Conference (IMVIP 2007).

[5]  Xiaobo Zhou,et al.  Multiple Nuclei Tracking Using Integer Programming for Quantitative Cancer Cell Cycle Analysis , 2010, IEEE Transactions on Medical Imaging.

[6]  Rosalina Abdul Salam,et al.  Blood cell image segmentation using hybrid K-means and median-cut algorithms , 2011, 2011 IEEE International Conference on Control System, Computing and Engineering.

[7]  G. Palacios,et al.  Cell Nuclei Segmentation Combining Multiresolution Analysis, Clustering Methods and Colour Spaces , 2007, International Machine Vision and Image Processing Conference.

[8]  Xiaobo Zhou,et al.  A Novel Cell Segmentation Method and Cell Phase Identification Using Markov Model , 2009, IEEE Transactions on Information Technology in Biomedicine.

[9]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[10]  Stephen T. C. Wong,et al.  An automated feedback system with the hybrid model of scoring and classification for solving over‐segmentation problems in RNAi high content screening , 2007, Journal of microscopy.

[11]  Yanxi Liu,et al.  Computer Vision for Biomedical Image Applications, First International Workshop, CVBIA 2005, Beijing, China, October 21, 2005, Proceedings , 2005, CVBIA.

[12]  Subhash Challa,et al.  Microscopic Cell Segmentation and Dead Cell Detection Based on CFSE and PI Images by Using Distance and Watershed Transforms , 2009, 2009 Digital Image Computing: Techniques and Applications.

[13]  Polina Golland,et al.  Voronoi-Based Segmentation of Cells on Image Manifolds , 2005, CVBIA.

[14]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[15]  Anne E Carpenter,et al.  CellProfiler: free, versatile software for automated biological image analysis. , 2007, BioTechniques.

[16]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[17]  E. Bak,et al.  Efficient segmentation framework of cell images in noise environments , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  Enrico Macii,et al.  Automated Segmentation of Cells With IHC Membrane Staining , 2011, IEEE Transactions on Biomedical Engineering.