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Applied Computational Intelligence and Soft Computing
Volume 2011, Article ID 786369, 11 pages
Research Article

A New Framework of Multiphase Segmentation and Its Application to Partial Volume Segmentation

1Department of Mathematics, University of Florida, Gainesville, FL 32611-8105, USA
2Department of Diagnostic Radiology, Yale University, New Haven, CT 06520-8042, USA

Received 14 October 2010; Revised 24 January 2011; Accepted 16 February 2011

Academic Editor: Antonio Di Nola

Copyright © 2011 Fuhua Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We proposed a novel framework of multiphase segmentation based on stochastic theory and phase transition theory. Our main contribution lies in the introduction of a constructed function so that its composition with phase function forms membership functions. In this way, it saves memory space and also avoids the general simplex constraint problem for soft segmentations. The framework is then applied to partial volume segmentation. Although the partial volume segmentation in this paper is focused on brain MR image, the proposed framework can be applied to any segmentation containing partial volume caused by limited resolution and overlapping.