Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models

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 explic...

• Type: Journal
• Year: 2008
• Where: TMI
• Authors: Zhuowen Tu, Katherine Narr, Piotr Dollár, Ivo D. Dinov, Paul M. Thompson, Arthur W. Toga
• Keywords: Anatomical Structure, Discriminative Appearance, Discriminative Appearance Models, TMI 2008