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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Published in | 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro p. 825 |
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Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.06.2009
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Subjects | |
Online Access | Get full text |
ISBN | 1424439310 9781424439317 |
ISSN | 1945-7928 |
DOI | 10.1109/ISBI.2009.5193178 |
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Abstract | 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. |
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AbstractList | 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. |
Author | Sabuncu, M.R. Golland, P. |
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Snippet | We present a novel, image-driven population analysis framework, called iCluster. iCluster processes a large set of images to determine a partitioning of the... |
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SubjectTerms | Artificial intelligence Availability Clustering algorithms Computer science Dementia Demography Image analysis Image segmentation Neuroimaging Spatial resolution |
Title | Image-driven population analysis through mixture modeling |
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