Image-driven population analysis through mixture modeling

We present a novel, image-driven population analysis framework, called iCluster. iCluster processes a large set of images to determine a partitioning of the population based on image similarities, while establishing a dense spatial correspondence across individuals and computing the templates that r...

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Bibliographic Details
Published in2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro p. 825
Main Authors Sabuncu, M.R., Golland, P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2009
Subjects
Artificial intelligence
Availability
Clustering algorithms
Computer science
Dementia
Demography
Image analysis
Image segmentation
Neuroimaging
Spatial resolution
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ISBN1424439310
9781424439317
ISSN1945-7928
DOI10.1109/ISBI.2009.5193178

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Summary:We present a novel, image-driven population analysis framework, called iCluster. iCluster processes a large set of images to determine a partitioning of the population based on image similarities, while establishing a dense spatial correspondence across individuals and computing the templates that represent each subpopulation. In experiments, we show that an image-driven partitioning of a large population reveals age effects on neuroanatomy and correlates with mental health.
ISBN:1424439310
9781424439317
ISSN:1945-7928
DOI:10.1109/ISBI.2009.5193178