DocumentCode :
947033
Title :
Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models
Author :
Tu, Zhuowen ; Narr, Katherine L. ; Dollár, Piotr ; Dinov, Ivo ; Thompson, Paul M. ; Toga, Arthur W.
Author_Institution :
UCLA, Los Angeles
Volume :
27
Issue :
4
fYear :
2008
fDate :
4/1/2008 12:00:00 AM
Firstpage :
495
Lastpage :
508
Abstract :
In this paper, a hybrid discriminative/generative model for brain anatomical structure segmentation is proposed. The learning aspect of the approach is emphasized. In the discriminative appearance models, various cues such as intensity and curvatures are combined to locally capture the complex appearances of different anatomical structures. A probabilistic boosting tree (PBT) framework is adopted to learn multiclass discriminative models that combine hundreds of features across different scales. On the generative model side, both global and local shape models are used to capture the shape information about each anatomical structure. The parameters to combine the discriminative appearance and generative shape models are also automatically learned. Thus, low-level and high-level information is learned and integrated in a hybrid model. Segmentations are obtained by minimizing an energy function associated with the proposed hybrid model. Finally, a grid-face structure is designed to explicitly represent the 3-D region topology. This representation handles an arbitrary number of regions and facilitates fast surface evolution. Our system was trained and tested on a set of 3-D magnetic resonance imaging (MRI) volumes and the results obtained are encouraging.
Keywords :
biomedical MRI; brain; image representation; image segmentation; medical image processing; probability; 3-D magnetic resonance imaging; 3-D region topology; MRI volumes; brain anatomical structure segmentation; discriminative appearance models; energy function; grid-face structure; hybrid discriminative-generative models; multiclass discriminative models; probabilistic boosting tree framework; shape information; surface evolution; Brain anatomical structures; discriminative models; generative models; probabilistic boosting tree; probabilistic boosting tree (PBT); segmentation; Algorithms; Artificial Intelligence; Brain; Computer Simulation; Discriminant Analysis; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Models, Neurological; Models, Statistical; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2007.908121
Filename :
4359071
Link To Document :
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