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MICCAI
2005
Springer

MRI Tissue Classification with Neighborhood Statistics: A Nonparametric, Entropy-Minimizing Approach

10 years 11 months ago
MRI Tissue Classification with Neighborhood Statistics: A Nonparametric, Entropy-Minimizing Approach
We introduce a novel approach for magnetic resonance image (MRI) brain tissue classification by learning image neighborhood statistics from noisy input data using nonparametric density estimation. The method models images as random fields and relies on minimizing an entropy-based metric defined on high dimensional probability density functions. Combined with an atlas-based initialization, it is completely automatic. Experiments on real and simulated data demonstrate the advantages of the method in comparison to other approaches.
Tolga Tasdizen, Suyash P. Awate, Ross T. Whitaker,
Added 15 Nov 2009
Updated 15 Nov 2009
Type Conference
Year 2005
Where MICCAI
Authors Tolga Tasdizen, Suyash P. Awate, Ross T. Whitaker, Norman L. Foster
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