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.

[1]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[2]  Koenraad Van Leemput,et al.  Automated model-based bias field correction of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[3]  Ron Kikinis,et al.  Markov random field segmentation of brain MR images , 1997, IEEE Transactions on Medical Imaging.

[4]  Alan C. Evans,et al.  BrainWeb: Online Interface to a 3D MRI Simulated Brain Database , 1997 .

[5]  Jean-Claude Froment,et al.  Unsupervised and Adaptive Segmentation of Multispectral 3D Magnetic Resonance Images of Human Brain: A Generic Approach , 2001, MICCAI.

[6]  Wiro J. Niessen,et al.  Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001 , 2001, Lecture Notes in Computer Science.

[7]  Kim Steenstrup Pedersen,et al.  The Nonlinear Statistics of High-Contrast Patches in Natural Images , 2003, International Journal of Computer Vision.

[8]  Arthur W Toga,et al.  The LONI Pipeline Processing Environment , 2003, NeuroImage.

[9]  Edward R. Dougherty,et al.  Random processes for image and signal processing , 1998, SPIE / IEEE series on imaging science and engineering.

[10]  D. Louis Collins,et al.  ANIMAL+INSECT: Improved Cortical Structure Segmentation , 1999, IPMI.

[11]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[12]  W. Eric L. Grimson,et al.  Adaptive Segmentation of MRI Data , 1995, CVRMed.

[13]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[14]  Ron Kikinis,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002 , 2002, Lecture Notes in Computer Science.

[15]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[16]  M. Stella Atkins,et al.  Fully automatic segmentation of the brain in MRI , 1998, IEEE Transactions on Medical Imaging.

[17]  Larry S. Davis,et al.  Improved fast gauss transform and efficient kernel density estimation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[18]  Suyash P. Awate,et al.  Higher-order image statistics for unsupervised, information-theoretic, adaptive, image filtering , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[19]  Kris Popat,et al.  Cluster-based probability model and its application to image and texture processing , 1997, IEEE Trans. Image Process..

[20]  W. Eric L. Grimson,et al.  Segmentation of brain tissue from magnetic resonance images , 1995, Medical Image Anal..

[21]  W. Eric L. Grimson,et al.  Incorporating Non-rigid Registration into Expectation Maximization Algorithm to Segment MR Images , 2002, MICCAI.

[22]  David G. Stork,et al.  Pattern Classification , 1973 .

[23]  Koenraad Van Leemput,et al.  Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[24]  A. Dale,et al.  Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain , 2002, Neuron.

[25]  Michael Brady,et al.  Estimating the bias field of MR images , 1997, IEEE Transactions on Medical Imaging.

[26]  Guido Gerig,et al.  Nonlinear anisotropic filtering of MRI data , 1992, IEEE Trans. Medical Imaging.

[27]  A. Kelemen,et al.  Three-dimensional model-based segmentation of brain MRI , 1998, Proceedings. Workshop on Biomedical Image Analysis (Cat. No.98EX162).