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Proceedings Paper

Segmentation of MRI brain scans into gray matter, white matter, and CSF
Author(s): Tamas Sandor; Hoo-Tee Ong; Vladimir I. Valtchinov; Marilyn Albert; Ferenc A. Jolesz
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Paper Abstract

An algorithm is described that can separate gray matter, white matter and CSF in brain scans taken with 3DFFT T1- weighted gradient echo magnetic resonance imaging. Although the algorithm is fully automated, it requires brain contours as input that utilize user-defined features. The inter- and intra-operator errors stemming from the variability of the contour definition and affecting the segmentation were assessed by using coronal brain scans of 19 subjects. The inter-operator errors were (1.61 plus or minus 2.38)% (P equals 0.01) for gray matter, (0.31 plus or minus 2.06)% (P equals 0.53) for white matter and (0.28 plus or minus 3.84)% (P equals 0.76) for cerebrospinal fluid (CSF). the intra- operator error was (0.28 plus or minus 0.55)% (P greater than 0.04) for gray matter, (0.40 plus or minus 0.37)% (P equals 0.0002) for white matter and (0.26 plus or minus 1.31)% (P equals 0.39) for CSF.

Paper Details

Date Published: 25 April 1997
PDF: 9 pages
Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); doi: 10.1117/12.274106
Show Author Affiliations
Tamas Sandor, Harvard Medical School (United States)
Hoo-Tee Ong, Harvard Medical School (United States)
Vladimir I. Valtchinov, Harvard Medical School (United States)
Marilyn Albert, Harvard Medical School (United States)
Ferenc A. Jolesz, Harvard Medical School (United States)

Published in SPIE Proceedings Vol. 3034:
Medical Imaging 1997: Image Processing
Kenneth M. Hanson, Editor(s)

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