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.
Tasdizen
Tolga
T
School of Computing, University of Utah, USA.
Awate
Suyash P
SP
Whitaker
Ross T
RT
Foster
Norman L
NL