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

  • Authors:

  • Loc Tran;Debrup Banerjee;Jihong Wang;Ashok J. Kumar;Frederic Mckenzie;Yaohang Li;Jiang Li

  • Affiliations:

  • Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, USA;Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, USA;Department of Imaging Physics, Division of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, USA;Department of Diagnostic Radiology, Division of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, USA;Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, USA;Department of Computer Science, Old Dominion University, Norfolk, USA;Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, USA

  • Venue:
  • Machine Vision and Applications
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

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.