Image-driven population analysis through mixture modeling

Author

Sabuncu, Mert R. ; Golland, Polina

Author_Institution

Comput. Sci. & Artificial Intell. Lab., MIT, Cambridge, MA, USA

fYear

2009

fDate

June 28 2009-July 1 2009

Firstpage

825

Lastpage

825

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.

Keywords

biomedical MRI; brain; neurophysiology; pattern clustering; OASIS dataset; anatomically homogeneous subpopulation identification; brain MRI scans; dense spatial correspondence; iCluster process; image clustering; image-driven partitioning; image-driven population analysis; mental health; mixture modeling approach; neuroanatomy; template computing; Artificial intelligence; Availability; Clustering algorithms; Computer science; Dementia; Demography; Image analysis; Image segmentation; Neuroimaging; Spatial resolution;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on

Conference_Location

Boston, MA

ISSN

1945-7928

Print_ISBN

978-1-4244-3931-7

Electronic_ISBN

1945-7928

Type

conf

DOI

10.1109/ISBI.2009.5193178

Filename

5193178