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Sökning: id:"swepub:oai:DiVA.org:liu-132512" > Detection of tubule...

Detection of tubule boundaries based on circular shortest path and polar-transformation of arbitrary shapes

Su, R. (författare)
Tianjin University, Peoples R China
Zhang, C. (författare)
CSIRO Data61, Australia; University of New South Wales, Australia
Pham, Tuan (författare)
Linköpings universitet,Institutionen för medicinsk teknik,Tekniska fakulteten
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Davey, R. (författare)
CSIRO Food and Nutr, Australia
Bischof, L. (författare)
CSIRO Data61, Australia
Vallotton, P. (författare)
CSIRO Data61, Australia; ETH, Switzerland
Lovell, D. (författare)
CSIRO Data61, Australia; Queensland University of Technology, Australia
Hope, S. (författare)
CSIRO Food and Nutr, Australia
Schmoelzl, S. (författare)
CSIRO Food and Nutr, Australia
Sun, C. (författare)
CSIRO Data61, Australia
visa färre...
 (creator_code:org_t)
2016-05-12
2016
Engelska.
Ingår i: Journal of Microscopy. - : Wiley-Blackwell Publishing Inc.. - 0022-2720 .- 1365-2818. ; 264:2, s. 127-142
  • Tidskriftsartikel (refereegranskat)
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

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