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Detection of tubule...
Detection of tubule boundaries based on circular shortest path and polar-transformation of arbitrary shapes
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- Su, R. (författare)
- Tianjin University, Peoples R China
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- Zhang, C. (författare)
- CSIRO Data61, Australia; University of New South Wales, Australia
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- Pham, Tuan (författare)
- Linköpings universitet,Institutionen för medicinsk teknik,Tekniska fakulteten
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- Davey, R. (författare)
- CSIRO Food and Nutr, Australia
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- Bischof, L. (författare)
- CSIRO Data61, Australia
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- Vallotton, P. (författare)
- CSIRO Data61, Australia; ETH, Switzerland
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- Lovell, D. (författare)
- CSIRO Data61, Australia; Queensland University of Technology, Australia
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- Hope, S. (författare)
- CSIRO Food and Nutr, Australia
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- Schmoelzl, S. (författare)
- CSIRO Food and Nutr, Australia
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- Sun, C. (författare)
- CSIRO Data61, Australia
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(creator_code:org_t)
- 2016-05-12
- 2016
- Engelska.
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Ingår i: Journal of Microscopy. - : Wiley-Blackwell Publishing Inc.. - 0022-2720 .- 1365-2818. ; 264:2, s. 127-142
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- In studies of germ cell transplantation, counting cells and measuring tubule diameters from different populations using labelled antibodies are important measurement processes. However, it is slow and sanity grinding to do these tasks manually. This paper proposes a way to accelerate these processes using a new image analysis framework based on several novel algorithms: centre points detection of tubules, tubule shape classification, skeleton-based polar-transformation, boundary weighting of polar-transformed image, and circular shortest path smoothing. The framework has been tested on a dataset consisting of 27 images which contain a total of 989 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually and the novel approach can achieve a better performance than two existing methods. Lay description In studies of germ cell transplantation, counting cells and measuring tubule diameters from different populations using labelled antibodies are important measurement processes. However, it is slow and sanity grinding to do these tasks manually. This paper proposes a way to accelerate these processes using a new image analysis framework based on several novel algorithms: center points detection of tubules, tubule shape classification, skeleton based polar-transformation, boundary weighting of polar-transformed image, and circular shortest path smoothing. The framework has been tested on a dataset consisting of 27 images which contain a total of 989 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually and the novel approach can achieve a better performance than two existing methods.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Boundary detection; boundary weighting; polar-transform; circular shortest path; tubule boundary; testis images
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Su, R.
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Zhang, C.
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Pham, Tuan
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Davey, R.
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Bischof, L.
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Vallotton, P.
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visa fler...
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Lovell, D.
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Hope, S.
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Schmoelzl, S.
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Sun, C.
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visa färre...
- Om ämnet
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- NATURVETENSKAP
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NATURVETENSKAP
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och Data och informa ...
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och Datorseende och ...
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Journal of Micro ...
- Av lärosätet
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Linköpings universitet