Pattern based morphometry

Authors:
Bilwaj Gaonkar;Kilian Pohl;Christos Davatzikos
Affiliations:
Section for Biomedical Image Analysis, University of Pennsylvania;Section for Biomedical Image Analysis, University of Pennsylvania;Section for Biomedical Image Analysis, University of Pennsylvania
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Year:
2011

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Abstract

Voxel based morphometry (VBM) is widely used in the neuroimaging community to infer group differences in brain morphology. VBM is effective in quantifying group differences highly localized in space. However it is not equally effective when group differences might be based on interactions between multiple brain networks. We address this by proposing a new framework called pattern based morphometry (PBM). PBM is a data driven technique. It uses a dictionary learning algorithm to extract global patterns that characterize group differences. We test this approach on simulated and real data obtained from ADNI . In both cases PBM is able to uncover complex global patterns effectively.