Large three-dimensional data-set segmentation using a graph-theoretic energy-minimization approach

Brian Parker; Dagan David Feng

doi:10.1117/12.481414

15 May 2003 Large three-dimensional dataset segmentation using a graph-theoretic energy-minimization approach

Brian Parker, Dagan David Feng

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.481414
Event: Medical Imaging 2003, 2003, San Diego, California, United States

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.

Citation Download Citation

Brian Parker and Dagan David Feng "Large three-dimensional dataset segmentation using a graph-theoretic energy-minimization approach", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.481414

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