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Proceedings Paper

MRI feature extraction using a linear transformation
Author(s): Hamid Soltanian-Zadeh; Joe P. Windham; Donald J. Peck
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Paper Abstract

We present development and application of a feature extraction method for magnetic resonance imaging (MRI), without explicit calculation of tissue parameters. We generate a three-dimensional (3-D) feature space representation of the data, in which normal tissues are clustered around pre-specified target positions and abnormalities are clustered somewhere else. This is accomplished by a linear minimum mean square error transformation of categorical data to target positions. From the 3-D histogram (cluster plot) of the transformed data, we identify clusters and define regions of interest (ROIs) for normal and abnormal tissues. There ROIs are used to estimate signature (feature) vectors for each tissue type which in turn are used to segment the MRI scene. The proposed feature space is compared to those generated by tissue-parameter-weighted images, principal component images, and angle images, demonstrating its superiority for feature extraction. The method and its performance are illustrated using a computer simulation and MRI images of an egg phantom and a human brain.

Paper Details

Date Published: 14 September 1993
PDF: 14 pages
Proc. SPIE 1898, Medical Imaging 1993: Image Processing, (14 September 1993); doi: 10.1117/12.154536
Show Author Affiliations
Hamid Soltanian-Zadeh, Henry Ford Hospital and Univ. of Michigan (United States)
Joe P. Windham, Henry Ford Hospital (United States)
Donald J. Peck, Henry Ford Hospital (United States)


Published in SPIE Proceedings Vol. 1898:
Medical Imaging 1993: Image Processing
Murray H. Loew, Editor(s)

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