CLASSIC: Consistent Longitudinal Alignment and Segmentation for Serial Image Computing

This paper proposes a temporally consistent and spatially adaptive longitudinal MR brain image segmentation algorithm, referred to as CLASSIC, which aims at obtaining accurate measurements of rates of change of regional and global brain volumes from serial MR images. The algorithm incorporates image-adaptive clustering, spatiotemporal smoothness constraints, and image warping to jointly segment a series of 3-D MR brain images of the same subject that might be undergoing changes due to development, aging, or disease. Morphological changes, such as growth or atrophy, are also estimated as part of the algorithm. Experimental results on simulated and real longitudinal MR brain images show both segmentation accuracy and longitudinal consistency.

Authors

• Zhong Xue
• Dinggang Shen
• Christos Davatzikos

Keywords

• Longitudinal brain image analysis
• Image segmentation
• Fuzzy clustering
• Brain atrophy
• Brain growth
• Serial scans
• Volumetry