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Detecting Structure in Diffusion Tensor MR Images K. Krishna Nand1
 

Summary: Detecting Structure in Diffusion Tensor MR Images
K. Krishna Nand1
, Rafeef Abugharbieh1
, Brian G. Booth2
, and Ghassan Hamarneh2
1
Biomedical Signal and Image Computing Lab, University of British Columbia
2
Medical Image Analysis Lab, School of Computing Science, Simon Fraser University
{kkrishna,rafeef}@ece.ubc.ca, {bgb2,hamarneh}@sfu.ca
Abstract. We derive herein first and second-order differential operators for de-
tecting structure in diffusion tensor MRI (DTI). Unlike existing methods, we are
able to generate full first and second-order differentials without dimensionality
reduction and while respecting the underlying manifold of the data. Further, we
extend corner and curvature feature detectors to DTI using our differential opera-
tors. Results using the feature detectors on diffusion tensor MR images show the
ability to highlight structure within the image that existing methods cannot.
1 Introduction
Feature detection is a core component in most image processing and analysis tasks with
ubiquitous applications ranging from data registration to object segmentation, classi-

  

Source: Abugharbieh, Rafeef - Department of Electrical and Computer Engineering, University of British Columbia

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences