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Proceedings Paper

Computerized Analysis And Information Extraction' Of Medical Magnetic Resonance Images (Mri)
Author(s): Robert L DeLaPaz; Ralph Bernstein
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Paper Abstract

Magnetic Resonance Imaging (MRI) is a relatively new diagnostic imaging modality that is rapidly finding broad clinical application. MRI differs from other diagnostic techniques in its capacity to obtain multiple qualitatively different images of the same anatomic region each emphasizing a different fundamental parameter of the tissue. This multiparametric nature of MRI provides the potential for greatly improved sensitivity and specificity in the detection of pathological conditions. However, the complexity of MRI can produce a potentially overwhelming volume of image data for the physician to analyze visually utilizing the traditional grey-scale. Additionally, "visual synthesis" of images from multiple data sets is only semi-quantitative at best and subject to errors introduced by observer bias. Data dimension reduction techniques are needed for analysis of these image sets of multi-parametric MRI data. It is hoped that improved diagnostic specificity of MRI will come from such a quantitative analysis of multiple MR images. Our initial experience with application of fuzzy clustering analysis to these MR images as a method of data dimension reduction suggests that such an approach can improve tissue specificity.

Paper Details

Date Published: 8 June 1988
PDF: 4 pages
Proc. SPIE 0902, Three-Dimensional Imaging and Remote Sensing Imaging, (8 June 1988); doi: 10.1117/12.944777
Show Author Affiliations
Robert L DeLaPaz, Stanford University (United States)
Ralph Bernstein, Stanford University (United States)


Published in SPIE Proceedings Vol. 0902:
Three-Dimensional Imaging and Remote Sensing Imaging
Woodrow E. Robbins, Editor(s)

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