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      Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy images

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      Author
      Arslan, S.
      Ersahin, T.
      Cetin-Atalay, R.
      Gunduz-Demir, C.
      Date
      2013
      Source Title
      IEEE Transactions on Medical Imaging
      Print ISSN
      0278-0062
      Publisher
      IEEE
      Volume
      32
      Issue
      6
      Pages
      1121 - 1131
      Language
      English
      Type
      Article
      Item Usage Stats
      133
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      141
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      Abstract
      More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms. © 2012 IEEE.
      Keywords
      Attributed relational graph
      Graph
      Model-based segmentation
      Nucleus segmentation
      Attributed relational graph
      Fluorescence microscopy imaging
      Cytology
      Monolayers
      Image segmentation
      Accuracy
      Algorithm
      Cell maturation
      Cell nucleus
      Cell nucleus segmentation
      Cellular distribution
      Comparative study
      Fluorescence microscopy
      Human
      Human cell
      Liver cell carcinoma
      Algorithms
      Permalink
      http://hdl.handle.net/11693/20933
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TMI.2013.2255309
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      • Department of Computer Engineering 1368
      • Department of Molecular Biology and Genetics 426
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