Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction

Research output: Contribution to journalArticle

182 Scopus citations

Abstract

Grayscale inhomogeneities in magnetic resonance (MR) images confound quantitative analysis of these images. Homomorphic unsharp masking and its variations have been commonly used as a post-processing method to remove inhomogeneities in MR images. However, little data is available in the literature assessing the relative effectiveness of these algorithms to remove inhomogeneities, or describing how these algorithms can affect image data. In this study, we address these questions quantitatively using simulated images with artificially constructed and empirically measured bias fields. Our results show that mean-based filtering is consistently more effective than medianbased algorithms for removing inhomogeneities in MR images, and that artifacts are frequently introduced into images at the most commonly used window sizes. Our results demonstrate dramatic improvement in the effectiveness of the algorithms with significantly larger windows than are commonly used.

Original languageEnglish (US)
Pages (from-to)161-171
Number of pages11
JournalIEEE transactions on medical imaging
Volume17
Issue number2
DOIs
StatePublished - Jan 1 1998

Keywords

  • Grayscale inhomogeneity
  • Image restoration
  • Magnetic resonance imaging (MRI)
  • Nonlinear filtering

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction'. Together they form a unique fingerprint.

  • Cite this