Detection of Landmarks in Multidimensional Tensor Data

J. Ruiz-Alzola, R. Kikinis, C.-F. Westin
Signal Processing
Volume 81, Pages 2243-2247
2001

Download full paper

Abstract

This paper describes a uni4ed approach to the detection of point landmarks—whose neighborhoods convey discriminant information—including multidimensional scalar, vector, and higher-order tensor data. The method is based on the interpretation of generalized correlation matrices derived from the gradient of tensor functions, a probabilistic interpretation of point landmarks, and the application of tensor algebra. Results on both synthetic and real tensor data are presented.

Fig. 2a shows part of a sagittal slice obtained with conventional T2-weighted MRI (scalar data showing anatomic information) of the brain with an overlay of all the point landmarks that have been detected from a corresponding DT-MRI slice of the same patient. Figs. 2b-g show the six independent components of a portion of the DT-MRI sagittal slice (top: diagonal components, bottom: oH-diagonal), corresponding to the square highlighted in Fig. 2a, with an overlay of the detected landmarks.

Reference

Ruiz-Alzola J, Kikinis R, Westin CF. Detection of landmarks in multidimensional tensor data. Signal Processing 2001;81:2243-2247.

Bibtex entry

@Article{ruizSIGPROC01,
  author         = {J. Ruiz-Alzola and R. Kikinis and C.-F. Westin},           
  title          = {Detection of Landmarks in Multidimensional Tensor Data},   
  journal        = {Signal Processing},                                        
  year           = {2001},                                                     
  volume         = {81},                                                       
  pages          = {2243--2247}
}

Grants

NIH P41-RR13218 (NAC), CIMIT

Research areas

DTMRI, Tensor,

Copyright Information

© Elsevier. Copyrights to this PDF document are held by Elsevier B.V.. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the Elsevier Publishing.

This material is presented electronically to ensure timely dissemination of scholarly and technical work. Certain rights are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the author and/or copyright holder.