Title :
An accurate and efficient Bayesian method for automatic segmentation of brain MRI
Author :
Marroquin, J.L. ; Vemuri, B.C. ; Botello, S. ; Calderon, F. ; Fernandez-Bouzas, A.
Author_Institution :
Centro de Investigaciones en Matematicas, Guanajuato, Mexico
Abstract :
Automatic three-dimensional (3-D) segmentation of the brain from magnetic resonance (MR) scans is a challenging problem that has received an enormous amount of attention lately. Of the techniques reported in the literature, very few are fully automatic. In this paper, we present an efficient and accurate, fully automatic 3-D segmentation procedure for brain MR scans. It has several salient features; namely, the following. 1) Instead of a single multiplicative bias field that affects all tissue intensities, separate parametric smooth models are used for the intensity of each class. 2) A brain atlas is used in conjunction with a robust registration procedure to find a nonrigid transformation that maps the standard brain to the specimen to be segmented. This transformation is then used to: segment the brain from nonbrain tissue; compute prior probabilities for each class at each voxel location and find an appropriate automatic initialization. 3) Finally, a novel algorithm is presented which is a variant of the expectation-maximization procedure, that incorporates a fast and accurate way to find optimal segmentations, given the intensity models along with the spatial coherence assumption. Experimental results with both synthetic and real data are included, as well as comparisons of the performance of our algorithm with that of other published methods.
Keywords :
Bayes methods; biomedical MRI; brain models; hidden Markov models; image registration; image segmentation; medical image processing; probability; accurate efficient Bayesian method; automatic segmentation; brain MRI; brain atlas; expectation-maximization procedure; magnetic resonance imaging; medical diagnostic imaging; multiplicative bias field; nonbrain tissue; nonrigid transformation; parametric smooth models; prior probabilities computation; spatial coherence assumption; voxel location; Bayesian methods; Biomedical imaging; Brain modeling; Hidden Markov models; Image registration; Image segmentation; Magnetic resonance; Magnetic resonance imaging; Robustness; Spatial coherence; Algorithms; Bayes Theorem; Brain; Computer Simulation; Databases, Factual; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Models, Neurological; Models, Statistical; Pattern Recognition, Automated; Quality Control; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Medical Imaging, IEEE Transactions on
DOI :
10.1109/TMI.2002.803119