Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70171
Title: Large three-dimensional data set segmentation using a graph-theoretic energy-minimization approach
Authors: Parker, B
Feng, D
Issue Date: 2003
Publisher: SPIE-International Society for Optical Engineering
Source: Proceedings of SPIE : the International Society for Optical Engineering, 2003, v. 5032 How to cite?
Journal: Proceedings of SPIE : the International Society for Optical Engineering 
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.
Description: Conference on Medical Imaging 2003: Image Processing, San Diego, California, U.S.A., Feb 2003
URI: http://hdl.handle.net/10397/70171
ISSN: 0277-786X
EISSN: 1996-756X
DOI: 10.1117/12.481414
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