NDLI: High-dimensional MRI data analysis using a large-scale manifold learning approach

Content Provider	Springer Nature Link
Author	Tran, Loc Banerjee, Debrup Wang, Jihong Kumar, Ashok J. McKenzie, Frederic Li, Yaohang Li, Jiang
Copyright Year	2013
Abstract	A novel manifold learning approach is presented to efficiently identify low-dimensional structures embedded in high-dimensional MRI data sets. These low-dimensional structures, known as manifolds, are used in this study for predicting brain tumor progression. The data sets consist of a series of high-dimensional MRI scans for four patients with tumor and progressed regions identified. We attempt to classify tumor, progressed and normal tissues in low-dimensional space. We also attempt to verify if a progression manifold exists—the bridge between tumor and normal manifolds. By identifying and mapping the bridge manifold back to MRI image space, this method has the potential to predict tumor progression. This could be greatly beneficial for patient management. Preliminary results have supported our hypothesis: normal and tumor manifolds are well separated in a low-dimensional space. Also, the progressed manifold is found to lie roughly between the normal and tumor manifolds.
Starting Page	995
Ending Page	1014
Page Count	20
File Format	PDF
ISSN	09328092
Journal	Machine Vision and Applications
Volume Number	24
Issue Number	5
e-ISSN	14321769
Language	English
Publisher	Springer-Verlag
Publisher Date	2013-04-19
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Brain tumor Progression Manifold Sampling Pattern Recognition Image Processing and Computer Vision Communications Engineering, Networks
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Computer Science Applications Software Hardware and Architecture

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