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

Author Affiliations +

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE