期刊名称：Applied Computational Intelligence and Soft Computing
印刷版ISSN：1687-9724
电子版ISSN：1687-9732
出版年度：2011
卷号：2011
DOI：10.1155/2011/786369
出版社：Hindawi Publishing Corporation
摘要：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.
