Three-Dimensional Anisotropic Noise Reduction with Automated Parameter Tuning: Application to Electron Cryotomography

  • Authors:

  • J. J. Fernández;S. Li;V. Lucic

  • Affiliations:

  • MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, UK and Dept. Computer Architecture, University of Almería, Almería 04120, Spain;MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, UK;Dept. Structural Biology, Max Planck Institute of Biochemistry, Martinsried, Germany

  • Venue:
  • Current Topics in Artificial Intelligence
  • Year:
  • 2007

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Abstract

This article presents an approach for noise filtering that is based on anisotropic nonlinear diffusion. The method combines edge-preserving noise reduction with a strategy to enhance local structures and a mechanism to further smooth the background. We have provided the method with an automatic mechanism for parameter self-tuning and for stopping the iterative filtering process. The performance of the approach is illustrated with its application to electron cryotomography (cryoET). CryoET has emerged as a leading imaging technique for visualizing the molecular architecture of complex biological specimens. A challenging computational task in this discipline is to increase the extremely low signal-to-noise ratio (SNR) to allow visualization and interpretation of the three-dimensional structures. The filtering method here proposed succeeds in substantially reducing the noise with excellent preservation of the structures.