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Proceedings Paper

Segmentation of multidimensional magnetic resonance (MR) images using a fuzzy neural network
Author(s): Jesse C. Ma; Jeffrey J. Rodriguez
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Paper Abstract

Methods of 3-D visualization of the brain based on fuzzy c-means (FCM) classified magnetic resonance (MR) images and a neural network trained on the FCM data are presented. A 3-D MR scan of a volunteer serves as the basis for the unsupervised classification techniques. The images were first classified into different tissue types by using FCM. The classified images were then reconstructed for 3-D display. Results show that individual tissue types can be discriminated during the 3-D rendering process. A neural network trained on the fuzzy classification data was also implemented. By using the cascade correlation algorithm during the network training, much of the tedious training work was avoided. The preliminary results from the neural network approach are quite encouraging.

Paper Details

Date Published: 21 September 1994
PDF: 8 pages
Proc. SPIE 2298, Applications of Digital Image Processing XVII, (21 September 1994); doi: 10.1117/12.186578
Show Author Affiliations
Jesse C. Ma, Univ. of Arizona (United States)
Jeffrey J. Rodriguez, Univ. of Arizona (United States)

Published in SPIE Proceedings Vol. 2298:
Applications of Digital Image Processing XVII
Andrew G. Tescher, Editor(s)

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