A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Image-Driven Population Analysis Through Mixture Modeling
2009
IEEE Transactions on Medical Imaging
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
doi:10.1109/tmi.2009.2017942
pmid:19336293
pmcid:PMC2832589
fatcat:m42yn2u24jezrjzr46ejgqjfl4