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Segmentation of clustered nuclei based on curvature-weighting
The University of New South Wales, Canberra, ACT, Australia .
CSIRO Mathematics, Informatics and Statistics, NSW, Australia .
The University of New South Wales, Canberra, ACT, Australia .
The University of Aizu, Fukushima, Japan .ORCID iD: 0000-0002-4255-5130
2012 (English)In: Proceedings of the 27th Conference on Image and Vision Computing New Zealand, 2012, 49-54 p.Conference paper (Refereed)Text
Abstract [en]

Clusters 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
2012. 49-54 p.
Keyword [en]
Image segmentation, Clump splitting, Cell nuclei, Curvature weighting, Curvature information based weighting
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-127961DOI: 10.1145/2425836.2425848ISBN: 978-1-4503-1473-2OAI: oai:DiVA.org:liu-127961DiVA: diva2:928494
Conference
IVCNZ '12 Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Available from: 2016-05-16 Created: 2016-05-13 Last updated: 2016-05-23

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

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ReferencesLink to record
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