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Clustered nuclei splitting using curvature information
The University of New South Wales at the Australian Defence Force Academy, Canberra, Australia; CSIRO Mathematics, Informatics and Statistics, North Ryde, Australia.
CSIRO Mathematics, Informatics and Statistics, North Ryde, Australia.
The University of New South Wales at the Australian Defence Force Academy, Canberra,, Australia.ORCID iD: 0000-0002-4255-5130
2011 (English)Conference paper, Published paper (Refereed)
Resource type
Text
Abstract [en]

Automated splitting of clustered nuclei from images of tissue sections is essential to many biomedical studies. Many existing image segmentation methods tend to produce over-segmented or under-segmented results for clustered nuclei images. In this paper, a new curvature information based image segmentation algorithm is proposed. Through combining curvature information with a distance map, our algorithm can extract correct markers corresponding to each nucleus. Afterwards, marker based watershed segmentation is used to segment the clustered nuclei. The algorithm is tested on both synthetic and real images. Experimental results show that our algorithm is accurate and robust to noise in segmentation of clustered nuclei.

Place, publisher, year, edition, pages
2011. 352-357 p.
Keyword [en]
Image segmentation; Curvature information; Clustered cell nuclei; Watershed segmentation
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-127906DOI: 10.1109/DICTA.2011.66Scopus ID: 2-s2.0-84863052510ISBN: 978-1-4577-2006-2 (print)OAI: oai:DiVA.org:liu-127906DiVA: diva2:928793
Conference
2011 International Conference on Digital Image Computing: Techniques and Applications. 6-8 December 2011, Noosa, QLD
Available from: 2016-05-16 Created: 2016-05-13 Last updated: 2017-08-08Bibliographically approved

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Pham, Tuan D
Medical Image Processing

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CiteExportLink to record
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Citation style
  • apa
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Output format
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