Using Eigenvalue Derivatives for Edge Detection in DT-MRI Data

Schultz, Thomas; Seidel, Hans-Peter

doi:10.1007/978-3-540-69321-5_20

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Using Eigenvalue Derivatives for Edge Detection in DT-MRI Data

MPS-Authors

/persons/resource/persons45428

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

/persons/resource/persons45449

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

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-1D51-F

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).