Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set

Xin Qi, Fuyong Xing, David J. Foran, Lin Yang

Research output: Contribution to journalArticle

159 Scopus citations


Automated image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of breast cancer. Automated segmentation of the cells comprising imaged tissue microarrays (TMAs) is a prerequisite for any subsequent quantitative analysis. Unfortunately, crowding and overlapping of cells present significant challenges for most traditional segmentation algorithms. In this paper, we propose a novel algorithm that can reliably separate touching cells in hematoxylin-stained breast TMA specimens that have been acquired using a standard RGB camera. The algorithm is composed of two steps. It begins with a fast, reliable object center localization approach that utilizes single-path voting followed by mean-shift clustering. Next, the contour of each cell is obtained using a level set algorithm based on an interactive model. We compared the experimental results with those reported in the most current literature. Finally, performance was evaluated by comparing the pixel-wise accuracy provided by human experts with that produced by the new automated segmentation algorithm. The method was systematically tested on $234$ image patches exhibiting dense overlap and containing more than $2200$ cells. It was also tested on whole slide images including blood smears and TMAs containing thousands of cells. Since the voting step of the seed detection algorithm is well suited for parallelization, a parallel version of the algorithm was implemented using graphic processing units (GPU) that resulted in significant speedup over the C/C implementation.

Original languageEnglish (US)
Article number6099601
Pages (from-to)754-765
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Issue number3
StatePublished - Mar 2012

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering


  • Level set
  • mean shift
  • parallel computing
  • seed detection
  • segmentation

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