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Proceedings Paper

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

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.

Paper Details

Date Published: 16 April 1996
PDF: 11 pages
Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); doi: 10.1117/12.237957
Show Author Affiliations
Benjamin H. Brinkmann, Mayo Clinic and Foundation (United States)
Armando Manduca, Mayo Clinic and Foundation (United States)
Richard A. Robb, Mayo Clinic and Foundation (United States)


Published in SPIE Proceedings Vol. 2710:
Medical Imaging 1996: Image Processing
Murray H. Loew; Kenneth M. Hanson, Editor(s)

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