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Segmentation of clustered nuclei based on curvature weighting
School of Engineering and Information Technology, The University of New South Wales, Canberra ACT, Australia.
CSIRO Mathematics, Informatics and Statistics, North Ryde, NSW, Australia.
School of Engineering and Information Technology, The University of New South Wales, Canberra ACT, Australia.
Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu Aizu-Wakamatsu, Fukushima, Japan.ORCID iD: 0000-0002-4255-5130
2012 (English)In: IVCNZ '12, Proceedings of the 27th Conference on Image and Vision Computing New Zealand, ACM Digital Library, 2012, 49-54 p.Conference paper (Other academic)Text
Abstract [en]

Cluster of nuclei are frequently observed in thick tissue section images. It is very important to segment overlapping nuclei in many biomedical applications. Many existing methods tend to produce under segmented results when there is a high overlap rate. In this paper, we present a curvature weighting based algorithm which weights each pixel using the curvature information of its nearby boundaries to extract markers, each of which represents an object, from input images. Then we use marker-controlled watershed to obtain the final segmentation. Test results using both synthetic and real cell images are presented in the paper.

Place, publisher, year, edition, pages
ACM Digital Library, 2012. 49-54 p.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-125051DOI: 10.1145/2425836.2425848ISBN: 9781450314732OAI: oai:DiVA.org:liu-125051DiVA: diva2:902713
Conference
27th Image and Vision Computing New Zealand IVCNZ'12, Dunedin, New Zealand, November 26-28, 2012
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2016-02-22Bibliographically approved

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Pham, Tuan D
Computer Vision and Robotics (Autonomous Systems)

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ReferencesLink to record
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