Fusing adaptive atlas and informative features for robust 3D brain image segmentation

Authors:
Cheng-Yi Liu;Juan Eugenio Iglesias;Arthur Toga;Zhuowen Tu
Affiliations:
University of California, Los Angeles, Lab of Neuro Imaging, Los Angeles, CA;University of California, Los Angeles, Medical Imaging Informatics, Los Angeles, CA;University of California, Los Angeles, Lab of Neuro Imaging, Los Angeles, CA;University of California, Los Angeles, Lab of Neuro Imaging, Los Angeles, CA
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

It is an important task to automatically segment brain anatomical structures from 3D MRI images. One major challenge in this problem is to learn/design effective models, for both intensity appearances and shapes, accounting for the large image variation due to the acquisition processes by different machines, at different parameters, and for different subjects. Generative models study the explicit parameters for the generation process, and thus are robust against the global intensity changes; discriminative models are able to combine many of the local statistics, which are insensitive to complex and inhomogeneous texture patterns. In this paper, we propose a robust brain image segmentation algorithm by fusing an adaptive atlas (generative) and informative features (discriminative). We tested our algorithm on several datasets and obtained improved results over state-of-the-art systems.