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

Li, W., Morrison, M. and Attikiouzel, Y. (1995) Unsupervised segmentation of dual-echo MR images by a sequentially learned Gaussian mixture model. In: Proceedings of the International Conference on Image Processing, 26 - 23 October, Washington, DC, USA pp. 576-579.

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Link to Published Version: http://dx.doi.org/10.1109/ICIP.1995.537700
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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 Type: Conference Paper
Publisher: IEEE
Copyright: © 1995 IEEE
URI: http://researchrepository.murdoch.edu.au/id/eprint/19281
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