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A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brainMagnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms and a supervised computational neural network, a dynamic multilayered perception trained with the cascade correlation learning algorithm. Initial clinical results are presented on both normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. However, for a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed.
Document ID
19930047270
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Hall, Lawrence O.
(NASA John F. Kennedy Space Center Cocoa Beach, FL, United States)
Bensaid, Amine M.
(NASA John F. Kennedy Space Center Cocoa Beach, FL, United States)
Clarke, Laurence P.
(NASA John F. Kennedy Space Center Cocoa Beach, FL, United States)
Velthuizen, Robert P.
(NASA John F. Kennedy Space Center Cocoa Beach, FL, United States)
Silbiger, Martin S.
(South Florida Univ. Tampa, FL, United States)
Bezdek, James C.
(West Florida Univ. Pensacola, FL, United States)
Date Acquired
August 16, 2013
Publication Date
September 1, 1992
Publication Information
Publication: IEEE Transactions on Neural Networks
Volume: 3
Issue: 5
ISSN: 1045-9227
Subject Category
Aerospace Medicine
Accession Number
93A31267
Funding Number(s)
CONTRACT_GRANT: NSF IRI-90-03252
Distribution Limits
Public
Copyright
Other

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