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Segmentation of clustered nuclei based on concave curve expansion
CSIRO Mathematics, Informatics and Statistics Division, North Ryde, New South Wales, Australia; School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT, Australia.
CSIRO Mathematics, Informatics and Statistics Division, North Ryde, New South Wales, Australia.
Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Fukushima, Japan.ORCID iD: 0000-0002-4255-5130
2013 (English)In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 251, no 1, 57-67 p.Article in journal (Refereed) PublishedText
Abstract [en]

Segmentation of nuclei from images of tissue sections is important for many biological and biomedical studies. Many existing image segmentation algorithms may lead to oversegmentation or undersegmentation for clustered nuclei images. In this paper, we proposed a new image segmentation algorithm based on concave curve expansion to correctly and accurately extract markers from the original images. Marker-controlled watershed is then used to segment the clustered nuclei. The algorithm was tested on both synthetic and real images and better results are achieved compared with some other state-of-the-art methods.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2013. Vol. 251, no 1, 57-67 p.
Keyword [en]
Clustered nuclei, concave curve expansion, curvature information, image segmentation, watershed segmentation
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-125011DOI: 10.1111/jmi.12043ISI: 000320170900008PubMedID: 23692597OAI: oai:DiVA.org:liu-125011DiVA: diva2:902791
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2016-02-22Bibliographically approved

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Pham, Tuan D
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