Conference paper
Unsupervised segmentation of dual-echo MR images by a sequentially learned Gaussian mixture model
Proceedings., International Conference on Image Processing, Vol.3, pp.576-579
IEEE
Proceedings of the International Conference on Image Processing (Washington, DC, USA, 26/10/1995–23/10/1995)
1995
Abstract
This paper proposes a method for unsupervised segmentation of brain tissues from dual-echo MR images without any prior knowledge about the number of tissues and their density distributions on each MRI echo. The brain tissues are described by a finite Gaussian mixture model (FGMM). The FGMM parameters are learned by sequentially applying the expectation maximization (EM) algorithm to a stream of data sets which are specifically organized according to the global spatial relationship of the brain tissues. Preliminary results on actual MRI slices have shown the method to be promising.
Details
- Title
- Unsupervised segmentation of dual-echo MR images by a sequentially learned Gaussian mixture model
- Authors/Creators
- W. Li (Author/Creator) - The University of Western AustraliaM. Morrison (Author/Creator) - The University of Western AustraliaY. Attikiouzel (Author/Creator) - The University of Western Australia
- Publication Details
- Proceedings., International Conference on Image Processing, Vol.3, pp.576-579
- Conference
- Proceedings of the International Conference on Image Processing (Washington, DC, USA, 26/10/1995–23/10/1995)
- Publisher
- IEEE
- Identifiers
- 991005543788607891
- Copyright
- © 1995 IEEE
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Conference paper
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