Using Eigenvalue Derivatives for Edge Detection in DT-MRI Data

Authors:
Thomas Schultz;Hans-Peter Seidel
Affiliations:
MPI Informatik, Saarbrücken, Germany 66123;MPI Informatik, Saarbrücken, Germany 66123
Venue:
Proceedings of the 30th DAGM symposium on Pattern Recognition
Year:
2008

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Visualization and analysis of diffusion tensor fields

Visualization and analysis of diffusion tensor fields
Flexible segmentation and smoothing of DT-MRI fields through a customizable structure tensor

ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I

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Abstract

This paper introduces eigenvalue derivatives as a fundamental tool to discern the different types of edges present in matrix-valued images. It reviews basic results from perturbation theory, which allow one to compute such derivatives, and shows how they can be used to obtain novel edge detectors for matrix-valued images. It is demonstrated that previous methods for edge detection in matrix-valued images are simplified by considering them in terms of eigenvalue derivatives. Moreover, eigenvalue derivatives are used to analyze and refine the recently proposed Log-Euclidean edge detector. Application examples focus on data from diffusion tensor magnetic resonance imaging (DT-MRI).