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Image-driven population analysis through mixture modeling
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Institution: |
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. msabuncu@csail.mit.edu |
Publisher: |
IEEE Trans Med Imaging |
Publication Date: |
Sep-2009 |
Citation: |
IEEE Trans Med Imaging. 2009 Sep;28(9):1473-87. |
PubMed ID: |
19336293 |
Keywords: |
Image Registration, Clustering, Population Analysis, Computational Anatomy, Segmentation, Projects:MultimodalAtlas |
Appears in Collections: |
NAC, NA-MIC, PNL |
Sponsors: |
5R01-MH050740-13 (MH) funded by NIMH NIH HHS P41 RR13218 (RR) funded by NCRR NIH HHS R01 NS051826 (NS) funded by NINDS NIH HHS U24 RR021382 (RR) funded by NCRR NIH HHS U54 EB005149 (EB) funded by NIBIB NIH HHS |
Generated Citation: |
Sabuncu M, Balci S, Shenton M, Golland P. Image-driven population analysis through mixture modeling. IEEE Trans Med Imaging. 2009 Sep;28(9):1473-87. PMID: 19336293. |
| Downloaded: | 175 times. [view map] |
| Paper: | Download, View online |
| Export citation: |
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".
Additional Material
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