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Proceedings Paper

Large three-dimensional data-set segmentation using a graph-theoretic energy-minimization approach
Author(s): Brian Parker; Dagan David Feng
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Paper Abstract

A new graph algorithm for the multiscale segmentation of large three-dimensional medical data sets is presented. It is a region-merging segmentation algorithm based on minimizing the Mumford-Shah energy. The Mumford-Shah functional formulation leads to improved segmentation results compared with alternative approaches; and the graph theoretic approach yields improved performance and simplified data structures. Also, the graph algorithm acts on only a subset of the full data set at a given time, allowing its application to large data sets such as whole-body scans. Results on a head MRI data set are presented and compared with a manual segmentation of this data set.

Paper Details

Date Published: 15 May 2003
PDF: 9 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481414
Show Author Affiliations
Brian Parker, Univ. of Sydney (Australia)
Dagan David Feng, Univ. of Sydney (Australia)
Hong Kong Polytechnic Univ. (China)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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