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CLASSIC: Consistent Longitudinal Alignment and Segmentation for Serial Image Computing

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3565)

Abstract

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.

Keywords

Fractional Anisotropy Segmentation Result Image Series Neighborhood Size Fuzzy Membership Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.Section of Biomedical Image Analysis, Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA

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