CLASSIC: Consistent longitudinal alignment and segmentation for serial image computing

Zhong Xue, Dinggang Shen, Christos Davatzikos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science
EditorsG.E. Christensen, M. Sonka
Pages101-113
Number of pages13
Volume3565
Publication statusPublished - 2005
Externally publishedYes
Event19th International Conference on Information Processing in Medical Imaging, IPMI 2005 - Glenwood Springs, CO, United States
Duration: 2005 Jul 102005 Jul 15

Other

Other19th International Conference on Information Processing in Medical Imaging, IPMI 2005
CountryUnited States
CityGlenwood Springs, CO
Period05/7/1005/7/15

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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  • Cite this

    Xue, Z., Shen, D., & Davatzikos, C. (2005). CLASSIC: Consistent longitudinal alignment and segmentation for serial image computing. In G. E. Christensen, & M. Sonka (Eds.), Lecture Notes in Computer Science (Vol. 3565, pp. 101-113)