Abstract：
:This paper describes a method for building efficient representations of large sets of brain images. Our hypothesis is that the space spanned by a set of brain images can be captured, to a close approximation, by a low-dimensional, nonlinear manifold. This paper presents a method to learn such a low-dimensional manifold from a given data set. The manifold model is generative-brain images can be constructed from a relatively small set of parameters, and new brain images can be projected onto the manifold. This allows to quantify the geometric accuracy of the manifold approximation in terms of projection distance. The manifold coordinates induce a Euclidean coordinate system on the population data that can be used to perform statistical analysis of the population. We evaluate the proposed method on the OASIS and ADNI brain databases of head MR images in two ways. First, the geometric fit of the method is qualitatively and quantitatively evaluated. Second, the ability of the brain manifold model to explain clinical measures is analyzed by linear regression in the manifold coordinate space. The regression models show that the manifold model is a statistically significant descriptor of clinical parameters.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Gerber S,Tasdizen T,Thomas Fletcher P,Joshi S,Whitaker R,Alzheimers Disease Neuroimaging Initiative (ADNI).doi
10.1016/j.media.2010.05.008subject
Has Abstractpub_date
2010-10-01 00:00:00pages
643-53issue
5eissn
1361-8415issn
1361-8423pii
S1361-8415(10)00061-7journal_volume
14pub_type
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