Deformable templates guided discriminative models for robust 3D brain MRI segmentation.

ABSTRACT: Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discrimi

SUBMITTER: Liu CY 

PROVIDER: S-EPMC5966025 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature