Image-Driven Population Analysis Through Mixture Modeling
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Sabuncu-2009-Image-Driven Populat.pdf
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Author(s)
Shenton, Martha E.
Balci, Serdar K.
Sabuncu, Mert R.
Golland, Polina
Date Issued
August 2009
Journal
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers
Citation
Sabuncu, M.R. et al. “Image-Driven Population Analysis Through Mixture Modeling.” Medical Imaging, IEEE Transactions on 28.9 (2009): 1473-1487. © Institute of Electrical and Electronics Engineers.
Version
Final published version
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 of the algorithm is a small number of template images that represent different modes in a population. This is in contrast with traditional, hypothesis-driven computational anatomy approaches that assume a single template to construct an atlas. We derive the algorithm based on a generative model of an image population as a mixture of deformable template images. We validate and explore our method in four experiments. In the first experiment, we use synthetic data to explore the behavior of the algorithm and inform a design choice on parameter settings. In the second experiment, we demonstrate the utility of having multiple atlases for the application of localizing temporal lobe brain structures in a pool of subjects that contains healthy controls and schizophrenia patients. Next, we employ iCluster to partition a data set of 415 whole brain MR volumes of subjects aged 18 through 96 years into three anatomical subgroups. Our analysis suggests that these subgroups mainly correspond to age groups. The templates reveal significant structural differences across these age groups that confirm previous findings in aging research. In the final experiment, we run iCluster on a group of 15 patients with dementia and 15 age-matched healthy controls. The algorithm produces two modes, one of which contains dementia patients only. These results suggest that the algorithm can be used to discover subpopulations that correspond to interesting structural or functional “modes.”
Subjects
segmentation
population analysis
image registration
computational anatomy
Clustering
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
http://dx.doi.org/10.1109/tmi.2009.2017942
