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ISBI
2009
IEEE

Image-Driven Population Analysis Through Mixture Modeling

10 years 6 months ago
Image-Driven Population Analysis Through Mixture Modeling
—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 ...
Mert R. Sabuncu
Added 19 May 2010
Updated 19 May 2010
Type Conference
Year 2009
Where ISBI
Authors Mert R. Sabuncu
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