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Segmentation and reconstruction of cultured neuron skeleton
James Cook University, Townsville, QLD 4811, Australia .
James Cook University, Townsville, QLD 4811, Australia .ORCID iD: 0000-0002-4255-5130
City University of Hong Kong, Kowloon, Hong Kong .
Swinburne University of Technology, Melborne, VIC 3122, Australia .
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2007 (English)In: COMPUTATIONAL MODELS FOR LIFE SCIENCES—CMLS’07, 2007, Vol. 952, 21-30 p.Conference paper (Refereed)Text
Abstract [en]

One approach to investigating neural death is through systematic studies of the changing morphology of cultured brain neurons in response to cellular challenges. Image segmentation and reconstruction methods developed to date to analyze such changes have been limited by the low contrast of cells. In this paper we present new algorithms that successfully circumvent these problems. The binary method is based on logical analysis of grey and distance difference of images. The spurious regions are detected and removed through use of a hierarchical window filter. The skeletons of binary cell images are extracted. The extension direction and connection points of broken cell skeletons are automatically determined, and broke neural skeletons are reconstructed. The spurious strokes are deleted based on cell prior knowledge. The efficacy of the developed algorithms is demonstrated here through a test of cultured brain neurons from newborn mice.

Place, publisher, year, edition, pages
2007. Vol. 952, 21-30 p.
Keyword [en]
Neuron cell image, image segmentation, grey difference, distance difference, filtering window, neuron skeleton, skeleton reconstruction.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-125042DOI: 10.1063/1.2816626 ISBN: 978-0-7354-0466-3OAI: oai:DiVA.org:liu-125042DiVA: diva2:902746
Conference
2007 International Symposium on Computational Models of Life Sciences. 17–19 December 2007Gold Coast, Queensland (Australia)
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2016-02-24

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Pham, Tuan D
Computer Vision and Robotics (Autonomous Systems)

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