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Multispectral magnetic resonance image analysis.

M W Vannier1, R L Butterfield, D L Rickman

  • 1Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri.

Critical Reviews in Biomedical Engineering
|January 1, 1987
PubMed
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Multispectral image classification, adapted from satellite imaging, aids in magnetic resonance (MR) tissue characterization. This method accurately maps tissue types by analyzing MR image variations, though errors can arise from image inconsistencies.

Area of Science:

  • Medical Imaging
  • Computer Science
  • Biophysics

Background:

  • Multiecho magnetic resonance (MR) scanning provides morphological data with varying gray scales.
  • Multispectral image classification techniques, originally from satellite imaging, are now applied to MR imaging for tissue characterization.

Purpose of the Study:

  • To statistically assess and select the most effective multispectral tissue classification techniques for MR imaging.
  • To analyze normal and pathological tissues using multispectral analysis and image classification on MR data.

Main Methods:

  • MR examinations of the head and body were performed using 0.35, 0.5, or 1.5T imagers with multiple pulse sequences.
  • MR image data underwent radiometric and geometric corrections to minimize errors from acquisition variations, noise, and misregistration.

Related Experiment Videos

  • Supervised and unsupervised classification techniques (table lookup, minimum distance to means, maximum likelihood, cluster analysis) were applied to regions of interest (ROIs) and compared to manually generated maps.

    • Main Results:

    • Both supervised and unsupervised classification techniques generated theme maps (class maps) that effectively demonstrated tissue characteristic signatures.
    • Tissue classification errors in computer-generated maps were attributed to subtle gray scale changes in MR data, stemming from radiometric inhomogeneity and spatial nonuniformity.

    Conclusions:

    • Multispectral image classification is a viable technique for MR tissue characterization.
    • Radiometric inhomogeneity and spatial nonuniformity in MR data are key factors contributing to classification errors.