Clustering of high dimensional longitudinal imaging data

Seonjoo Lee, Vadim Zipunnikov, Navid Shiee, Ciprian Crainiceanu, Brian S. Caffo, Dzung L. Pham

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

Abstract

In the study of brain disease processes and aging, longitudinal imaging studies are becoming increasingly commonplace. Indeed, there are hundreds of studies collecting multi-sequence multi-modality brain images at multiple time points on hundreds of subjects over many years. A fundamental problem in this context is how to classify subjects according to their baseline and longitudinal changes in the presence of strong spatio-temporal biological and technological measurement error. We propose a fast and scalable clustering approach by defining a metric between latent trajectories of brain images. Methods were motivated by and applied to a longitudinal voxel-based morphometry study of multiple sclerosis. Results indicate that there are two distinct patterns of ventricular change that are associated with clinical outcomes.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013
Pages33-36
Number of pages4
DOIs
StatePublished - Oct 15 2013
Event2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013 - Philadelphia, PA, United States
Duration: Jun 22 2013Jun 24 2013

Publication series

NameProceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013

Other

Other2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013
CountryUnited States
CityPhiladelphia, PA
Period6/22/136/24/13

Keywords

  • cluster analysis
  • longitudinal functional principal component analysis (LFPCA)
  • regional analysis of volumes examined in normalized space (RAVENS)
  • ultra high dimensional longitudinal data

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

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