Quantitative analysis of statistical methods of grayscale inhomogeneity correction in magnetic resonance images

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations


Grayscale inhomogeneities in magnetic resonance (MR) images cause significant problems in automated quantitative image analysis. Removal of such inhomogeneities is a difficult task, but it has been investigated by a number of different authors recently. The most common methods used involve some type of homomorphic filtering to create a smoothed version of the original image, which is then used as an estimate of the bias field to be removed from the image. Many investigators have implemented variations of this technique and have demonstrated their usefulness for a wide range of applications, but no investigator has yet attempted a systematic, quantitative study to describe the effects these algorithms have on images. This study introduces a quantitative paradigm for evaluating inhomogeneity correction algorithms by their performance on a constructed simulation image with different bias fields applied. We find that mean filter algorithms are more successful than median filter algorithms, and that larger kernel sizes than what are currently reported in the literature offer significant improvements in post-correction image quality.

Original languageEnglish (US)
Pages (from-to)542-552
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 1996
EventMedical Imaging 1996 Image Processing - Newport Beach, CA, United States
Duration: Feb 12 1996Feb 15 1996


  • Brain
  • Classification
  • Correction
  • Grayscale
  • Inhomogeneity
  • MRI
  • Radiofrequency
  • Restoration
  • Stat istical

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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