Segmentation of cultured neurons using logical analysis of grey and distance difference

Yu, Donggang, Pham, Tuan D., Yan, Hong, Zhang, Bailing, and Crane, Denis I. (2007) Segmentation of cultured neurons using logical analysis of grey and distance difference. Journal of Neuroscience Methods, 166 (1). pp. 125-137.

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Abstract

The molecular and cellular bases of neuronal cell death that underpin a wide range of neurodegenerative disorders are still not well understood. One approach to investigating neuronal death is through systematic studies of the changing morphology of cultured brain neurons in response to cellular challenges. Image segmentation methods developed to date to analyze such changes have been limited by the low contrast of cells in unstained neuronal cultures and the unimodal histograms generated by these analyses. In this paper we present new algorithms based on logical analysis of grey and distance difference of images that successfully circumvent these problems. Two key parameters of this analysis, window width and logical threshold, are automatically extracted for use in logical level technique, and spurious regions are detected and removed through use of a hierarchical window filter. The efficacy of the developed algorithms is demonstrated here through an analysis of cultured brain neurons from newborn mice.

Item ID: 2505
Item Type: Article (Research - C1)
ISSN: 0165-0270
Keywords: pattern classification; pattern recognition; image analysis; neuronal cell image; image segmentation; grey difference; distance difference; filtering window
Date Deposited: 24 Jul 2009 02:00
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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