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Segmentation of clu...
Segmentation of clustered nuclei based on concave curve expansion
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- Zhang, C (author)
- North Ryde, New South Wales 2113, Australia,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
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- Sun, C (author)
- The University of New South Wales, Canberra,Australia,CSIRO Mathematics, Informatics and Statistics Division, North Ryde, New South Wales, Australia
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- Pham, Tuan D. (author)
- ‡Aizu Research Cluster for Medical Engineering and Informatics, Research Center for AdvancedInformation Science and Technology, The University of Aizu, Fukushima, Japan,Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Fukushima, Japan
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(creator_code:org_t)
- 2013-05-20
- 2013
- English.
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In: Journal of Microscopy. - : Wiley. - 0022-2720 .- 1365-2818. ; 251:1, s. 57-67
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Keyword
- clustered nuclei
- concave curve expansion
- curvature information
- image segmentation
- watershed segmentation
Publication and Content Type
- ref (subject category)
- art (subject category)
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