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Edge Detection in Diffusion Weighted MRI Using a Tangent Curve Similarity Metric

Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

We present a technique to automatically characterize the geometry of important anatomical structures in diffusion weighted MRI (DWI) data. Our approach is based on the interpretation of diffusion data as a superimposition of multiple line fields that each form a continuum of space filling curves. Using a dense tractography computation, our method quantifies the spatial variations of the geometry of these curves and use the resulting measure to characterize salient structures as edges. Anatomically, these structures have a boundary-like nature and yield a clear picture of major fiber bundles. In particular, the application of our algorithm to high angular resolution imaging (HARDI) data yields a precise geometric description of subtle anatomical configurations associated with the local presence of multiple fiber orientations. We evaluate our technique and study its robustness to noise in the context of a phantom dataset and present results obtained with two diffusion weighted brain images.

Notes

Acknowledgements

This work was made possible in part by a NSF CAREER Program Award No. 1150000: Efficient Structural Analysis of Multivariate Fields for Scalable Visualizations. This support is here gratefully acknowledged.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Purdue UniversityWest LafayetteUSA
  2. 2.IBM ResearchSan JoseUSA

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