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Proceedings Paper

Automatic brain tumor segmentation
Author(s): Matthew C. Clark; Lawrence O. Hall; Dmitry B. Goldgof; Robert Paul Velthuizen; F. Reed Murtaugh; Martin L. Silbiger
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Paper Abstract

A system that automatically segments and labels complete glioblastoma-multiform tumor volumes in magnetic resonance images of the human brain is presented. The magnetic resonance images consist of three feature images (T1- weighted, proton density, T2-weighted) and are processed by a system which integrates knowledge-based techniques with multispectral analysis and is independent of a particular magnetic resonance scanning protocol. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intra-cranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intra-cranial region, with region analysis used in performing the final tumor labeling. This system has been trained on eleven volume data sets and tested on twenty-two unseen volume data sets acquired from a single magnetic resonance imaging system. The knowledge-based tumor segmentation was compared with radiologist-verified `ground truth' tumor volumes and results generated by a supervised fuzzy clustering algorithm. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.

Paper Details

Date Published: 24 June 1998
PDF: 12 pages
Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310932
Show Author Affiliations
Matthew C. Clark, Univ. of South Florida (United States)
Lawrence O. Hall, Univ. of South Florida (United States)
Dmitry B. Goldgof, Univ. of South Florida (United States)
Robert Paul Velthuizen, Univ. of South Florida (United States)
F. Reed Murtaugh, Univ. of South Florida (United States)
Martin L. Silbiger, Univ. of South Florida (United States)

Published in SPIE Proceedings Vol. 3338:
Medical Imaging 1998: Image Processing
Kenneth M. Hanson, Editor(s)

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