Evaluation of denoising algorithms for biological electron tomography

Rajesh Narasimha, Iman Aganj, Adam E. Bennett, Mario J. Borgnia, Daniel Zabransky, Guillermo Sapiro, Steven W. McLaughlin, Jacqueline L S Milne, Sriram Subramaniam

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Tomograms of biological specimens derived using transmission electron microscopy can be intrinsically noisy due to the use of low electron doses, the presence of a "missing wedge" in most data collection schemes, and inaccuracies arising during 3D volume reconstruction. Before tomograms can be interpreted reliably, for example, by 3D segmentation, it is essential that the data be suitably denoised using procedures that can be individually optimized for specific data sets. Here, we implement a systematic procedure to compare various nonlinear denoising techniques on tomograms recorded at room temperature and at cryogenic temperatures, and establish quantitative criteria to select a denoising approach that is most relevant for a given tomogram. We demonstrate that using an appropriate denoising algorithm facilitates robust segmentation of tomograms of HIV-infected macrophages and Bdellovibrio bacteria obtained from specimens at room and cryogenic temperatures, respectively. We validate this strategy of automated segmentation of optimally denoised tomograms by comparing its performance with manual extraction of key features from the same tomograms.

Original languageEnglish (US)
Pages (from-to)7-17
Number of pages11
JournalJournal of Structural Biology
Issue number1
StatePublished - Oct 2008
Externally publishedYes


  • Automated techniques
  • Denoising
  • Diffusion
  • Electron tomography
  • Feature extraction
  • Template matching

ASJC Scopus subject areas

  • Structural Biology


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