Image and Informatics Group, LBNL : Home
Image and Informatics Group, LBNL : Home

A Delunay Triangulation Approach for Segmenting Clumps of Nuclei

IEEE Int. Symp. on Biomedical Imaging-from nano to macro , 2009

    Q. Wen
    H. Chang
    B. Parvin


    Cell-based fluorescence imaging assays have the potential to generate massive amount of data, which requires detailed quantitative analysis. Often, as a result of fixation, labeled nuclei overlap and create a clump of cells. However, it is important to quantify phenotypic read out on a cell-by-cell basis. In this paper, we propose a novel method for decomposing clumps of nuclei using high-level geometric constraints that are derived from low-level features of maximum curvature computed along the contour of each clump. Points of maximum curvature are used as vertices for Delaunay triangulation (DT), which provides a set of edge hypotheses for decomposing a clump of nuclei. Each hypothesis is subsequently tested against a constraint satisfaction network for a near optimum decomposition. The proposed method is compared with other traditional techniques such as the watershed method with/without markers. The experimental results show that our approach can overcome the deficiencies of the traditional methods and is very effective in separating severely touching nuclei.

    click here to see the full version of the paper in Acrobat format