A New Framework of Multiphase Segmentation and Its Application to Partial Volume Segmentation
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 als...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2011-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2011/786369 |
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| Summary: | 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. |
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| ISSN: | 1687-9724 1687-9732 |