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Konferenzbeitrag

Using Eigenvalue Derivatives for Edge Detection in DT-MRI Data

MPG-Autoren
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Schultz,  Thomas
Computer Graphics, MPI for Informatics, Max Planck Society;

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Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

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Zitation

Schultz, T., & Seidel, H.-P. (2008). Using Eigenvalue Derivatives for Edge Detection in DT-MRI Data. In G. Rigoll (Ed.), Pattern Recognition: 30th DAGM Symposium (pp. 193-202). Berlin: Springer.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-1D51-F
Zusammenfassung
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).