• About
  • Policies
  • What is open access
  • Library
  • Contact
Advanced search
      View Item 
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Computer Engineering
      • View Item
      •   BUIR Home
      • Scholarly Publications
      • Faculty of Engineering
      • Department of Computer Engineering
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy images

      Thumbnail
      View / Download
      1.4 Mb
      Author(s)
      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
      151
      views
      148
      downloads
      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
      Collections
      • Department of Computer Engineering 1435
      • Department of Molecular Biology and Genetics 468
      Show full item record

      Related items

      Showing items related by title, author, creator and subject.

      • Thumbnail

        Unsupervised tissue image segmentation through object-oriented texture 

        Tosun, Akif Burak; Sokmensuer, C.; Gündüz-Demir, Çiğdem (IEEE, 2010)
        This paper presents a new algorithm for the unsupervised segmentation of tissue images. It relies on using the spatial information of cytological tissue components. As opposed to the previous study, it does not only use ...
      • Thumbnail

        Canlı hücre bölütlemesi için gözeticili öğrenme modeli 

        Koyuncu, Can Fahrettin; Durmaz, İrem; Çetin-Atalay, Rengül; Gündüz-Demir, Çiğdem (IEEE Computer Society, 2014-04)
        Automated cell imaging systems have been proposed for faster and more reliable analysis of biological events at the cellular level. The first step of these systems is usually cell segmentation whose success affects the ...
      • Thumbnail

        Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection 

        Tosun, A. B.; Kandemir, M.; Sokmensuer, C.; Gunduz Demir, C. (Elsevier BV, 2009-06)
        Staining methods routinely used in pathology lead to similar color distributions in the biologically different regions of histopathological images. This causes problems in image segmentation for the quantitative analysis ...

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      LoginRegister

      Statistics

      View Usage StatisticsView Google Analytics Statistics

      Bilkent University

      If you have trouble accessing this page and need to request an alternate format, contact the site administrator. Phone: (312) 290 1771
      © Bilkent University - Library IT

      Contact Us | Send Feedback | Off-Campus Access | Admin | Privacy