Discovering Modes of an Image Population through Mixture Modeling

M. Sabunchu, S. Balci, M.E. Shenton, P. Golland
miccai
Volume 5242, Pages 381-389
October, 2008

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Abstract

We present iCluster, a fast and efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. The output is a small number of template images that represent different modes in a population. This is in contrast with traditional approaches that assume a single template to construct atlases. We validate and explore the algorithm in two experiments. First, we employ iCluster to partition a data set of 416 whole brain MR volumes of subjects aged 18-96 years into three sub-groups, which mainly correspond to age groups. The templates reveal significant structural differences across these age groups that confirm previous findings in aging research. In the second experiment, we run iCluster on a group of 30 patients with dementia and 30 age-matched healthy controls. The algorithm produced three modes that mainly corresponded to a sub-population of healthy controls, a sub-population of patients with dementia and a mixture group that contained both types. These results suggest that the algorithm can be used to discover sub-populations that correspond to interesting structural or functional ’¡Èmodes.’¡É

Reference

Sabunchu M, Balci S, Shenton M, Golland P. Discovering modes of an image population through mixture modeling. In miccai, volume 5242. 2008;381-389.

Grants

NIH NIBIB NAMIC U54-EB005149, NIN NCRR NAC P41-RR13218, NIH NCRR mBIRN U24-RR021382, NIH NINDS grant R01-NS051826, NSF CAREER 0642971

Research area

brainatlas
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