• About
  • Policies
  • What is openaccess
  • 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.

      Canlı hücre bölütlemesi için gözeticili öğrenme modeli

      Thumbnail
      View / Download
      495.0 Kb
      Author
      Koyuncu, Can Fahrettin
      Durmaz, İrem
      Çetin-Atalay, Rengül
      Gündüz-Demir, Çiğdem
      Date
      2014-04
      Source Title
      22nd Signal Processing and Communications Applications Conference, SIU 2014
      Publisher
      IEEE Computer Society
      Pages
      1971 - 1974
      Language
      Turkish
      Type
      Conference Paper
      Item Usage Stats
      172
      views
      97
      downloads
      Abstract
      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 other system steps. Thus, it is critical to implement robust and efficient segmentation algorithms for the design of successful systems. In the literature, the most commonly used methods for cell segmentation are marker controlled watersheds. These watershed algorithms assume that markers one-to-one correspond to cells and identify their boundaries by growing these markers. Thus, it is very important to correctly define the markers for these algorithms. The markers are usually defined by finding local minima/maxima on intensity or gradient values or by applying morphological operations on the corresponding binary image. In this work, we propose a new marker controlled watershed algorithm for live cell segmentation. The main contributions of this algorithm are twofold. First, different than the approaches in the literature, it implements a new supervised learning model for marker detection. In this model, it has been proposed to extract features for each pixel considering its neighbors' intensities and gradients and to decide whether this pixel is a marker pixel or not by a classifier using these extracted features. Second, it has been proposed to group the neighboring pixels based on the direction information and to extract features according to these groups. The experiments on 1954 cells show that the proposed algorithm leads to higher segmentation results compared to other watersheds. © 2014 IEEE.
      Keywords
      Cell lines
      Cell segmentation
      Marker controlled watershed algorithms
      Support vector machines
      Algorithms
      Cell culture
      Mathematical morphology
      Pixels
      Signal processing
      Supervised learning
      Support vector machines
      Watersheds
      Cell lines
      Cell segmentation
      Marker controlled watershed algorithms
      Marker-controlled watersheds
      Morphological operations
      Segmentation algorithms
      Segmentation results
      Water-shed algorithm
      Image segmentation
      Permalink
      http://hdl.handle.net/11693/27878
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2014.6830643
      Collections
      • Department of Computer Engineering 1410
      • Department of Molecular Biology and Genetics 445
      Show full item record

      Related items

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

      • Thumbnail

        Iterative H-minima-based marker-controlled watershed for cell nucleus segmentation 

        Koyuncu, Can Fahrettin; Akhan, Ece; Ersahin, T.; Cetin Atalay, R.; Gunduz Demir, Çiğdem (John Wiley & Sons, Inc., 2016)
        Automated microscopy imaging systems facilitate high-throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the ...
      • Thumbnail

        A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images 

        Arslan, S.; Ozyurek, E.; Gunduz Demir, C. (John Wiley & Sons, Inc., 2014)
        Computer-based imaging systems are becoming important tools for quantitative assessment of peripheral blood and bone marrow samples to help experts diagnose blood disorders such as acute leukemia. These systems generally ...
      • Thumbnail

        Perceptual watersheds for cell segmentation in fluorescence microscopy images 

        Arslan, Salim (Bilkent University, 2012)
        High content screening aims to analyze complex biological systems and collect quantitative data via automated microscopy imaging to improve the quality of molecular cellular biology research in means of speed and accuracy. ...

      Browse

      All of BUIRCommunities & CollectionsTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartmentsThis CollectionTitlesAuthorsAdvisorsBy Issue DateKeywordsTypeDepartments

      My Account

      Login

      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