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Detection of tubule boundaries based on circular shortest path and polar-transformation of arbitrary shapes
Tianjin University, Peoples R China.
CSIRO Data61, Australia; University of New South Wales, Australia.
Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4255-5130
CSIRO Food and Nutr, Australia.
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2016 (English)In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 264, no 2, 127-142 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
WILEY-BLACKWELL , 2016. Vol. 264, no 2, 127-142 p.
Keyword [en]
Boundary detection; boundary weighting; polar-transform; circular shortest path; tubule boundary; testis images
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-132512DOI: 10.1111/jmi.12421ISI: 000385944300001PubMedID: 27172164OAI: oai:DiVA.org:liu-132512DiVA: diva2:1046497
Available from: 2016-11-14 Created: 2016-11-13 Last updated: 2016-11-14

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Pham, Tuan
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