CLASSIC: consistent longitudinal alignment and segmentation for serial image computing.

Zhong Xue, Dinggang Shen, Christos Davatzikos

Research output: Contribution to journalArticle

5 Citations (Scopus)

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
Pages (from-to)101-113
Number of pages13
JournalInformation processing in medical imaging : proceedings of the ... conference
Volume19
Publication statusPublished - 2005 Dec 1
Externally publishedYes

ASJC Scopus subject areas

  • Medicine(all)

Fingerprint Dive into the research topics of 'CLASSIC: consistent longitudinal alignment and segmentation for serial image computing.'. Together they form a unique fingerprint.

  • Cite this