Journal of Digital Imaging

, Volume 24, Issue 3, pp 464–469 | Cite as

Image Denoising Methods for Tumor Discrimination in High-Resolution Computed Tomography

  • José Silvestre Silva
  • Augusto Silva
  • Beatriz Sousa Santos


Pixel accuracy in images from high-resolution computed tomography (HRCT) is ultimately limited by reconstruction error and noise. While for visual analysis this may not be relevant, for computer-aided quantitative analysis in either densitometric, or shape studies aiming at accurate results, the impact of pixel uncertainty must be taken into consideration. In this work, we study several denoising methods: geometric mean filter, Wiener filtering, and wavelet denoising. The performance of each method was assessed through visual inspection, profile region intensity analysis, and global figures of merit, using images from brain and thoracic phantoms, as well as several real thoracic HRCT images.

Key words

Denoising methods tomographic reconstruction Poisson noise computed tomography 


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Copyright information

© Society for Imaging Informatics in Medicine 2010

Authors and Affiliations

  • José Silvestre Silva
    • 1
    • 2
  • Augusto Silva
    • 3
    • 4
  • Beatriz Sousa Santos
    • 3
    • 4
  1. 1.Department of Physics, Faculty of Sciences and TechnologyUniversity of CoimbraCoimbraPortugal
  2. 2.Instrumentation Center, Faculty of Sciences and TechnologyUniversity of CoimbraCoimbraPortugal
  3. 3.Department of Electronics, Telecommunications and InformaticsUniversity of AveiroAveiroPortugal
  4. 4.Institute of Electronics and Telematics Engineering of AveiroAveiroPortugal

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