Segmentation and reconstruction of cultured neuron skeleton

Yu, Donggang, Pham, Tuan D., Yan, Hong, Lai, Wei, and Crane, Denis I. (2007) Segmentation and reconstruction of cultured neuron skeleton. In: Proceedings of the 2007 International Symposium of Computational Models for Life Sciences (952), pp. 21-30. From: Computational Models for Life Sciences - CMLS '07: 2007 International Symposium, 17-19 December 2007, Queensland, Australia.

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Abstract

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.

Item ID: 3029
Item Type: Conference Item (Refereed Research Paper - E1)
Keywords: image analysis; pattern recognition; bioinformatics; image reconstruction; brain models; neural nets
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ISBN: 978-0-7354-0466-3
Date Deposited: 15 Oct 2009 00:28
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 50%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing @ 50%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890205 Information Processing Services (incl. Data Entry and Capture) @ 100%
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