Three-dimensional anisotropic noise reduction with automated parameter tuning: Application to electron cryotomography

• Fernández, J.J. ^[1] ; Li, S. ; Lucic, V. ^[2]
  1. [1] Universidad de Almería

     Universidad de Almería

     Almería, España

     
  2. [2] Max Planck Institute of Biochemistry

     Max Planck Institute of Biochemistry

     Kreisfreie Stadt München, Alemania

     
• Localización: XII Conferencia de la Asociación Española para la Inteligencia Artificial: (CAEPIA 2007). Actas / coord. por Daniel Borrajo Millán, Luis Castillo Vidal, Juan Manuel Corchado Rodríguez, Vol. 1, 2007, ISBN 978-84-611-8847-5, págs. 247-256
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.