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Unsupervised segmentation of dual-echo MR images by a sequentially learned Gaussian mixture model

Conference Paper


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.

Publication Date


  • 1995

Citation


  • Li, W., Morrison, M., & Attikiouzel, Y. (1995). Unsupervised segmentation of dual-echo MR images by a sequentially learned Gaussian mixture model. In IEEE International Conference on Image Processing Vol. 3 (pp. 576-579).

Scopus Eid


  • 2-s2.0-0029516616

Start Page


  • 576

End Page


  • 579

Volume


  • 3

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.

Publication Date


  • 1995

Citation


  • Li, W., Morrison, M., & Attikiouzel, Y. (1995). Unsupervised segmentation of dual-echo MR images by a sequentially learned Gaussian mixture model. In IEEE International Conference on Image Processing Vol. 3 (pp. 576-579).

Scopus Eid


  • 2-s2.0-0029516616

Start Page


  • 576

End Page


  • 579

Volume


  • 3