Tensor dissimilarity based adaptive seeding algorithm for DT-MRI visualization with streamtubes

Yonas T. Weldeselassie; Ghassan Hamarneh; Daniel Weiskopf

doi:10.1117/12.710417

22 March 2007 Tensor dissimilarity based adaptive seeding algorithm for DT-MRI visualization with streamtubes

Yonas T. Weldeselassie, Ghassan Hamarneh, Daniel Weiskopf

Proceedings Volume 6509, Medical Imaging 2007: Visualization and Image-Guided Procedures; 65092M (2007) https://doi.org/10.1117/12.710417
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

In this paper, we propose an adaptive seeding strategy for visualization of diffusion tensor magnetic resonance imaging (DT-MRI) data using streamtubes. DT-MRI is a medical imaging modality that captures unique water diffusion properties and fiber orientation information of the imaged tissues. Visualizing DT-MRI data using streamtubes has the advantage that not only the anisotropic nature of the diffusion is visualized but also the underlying anatomy of biological structures is revealed. This makes streamtubes significant for the analysis of fibrous tissues in medical images. In order to avoid rendering multiple similar streamtubes, an adaptive seeding strategy is employed which takes into account similarity of tensors in a given region. The goal is to automate the process of generating seed points such that regions with dissimilar tensors are assigned more seed points compared to regions with similar tensors. The algorithm is based on tensor dissimilarity metrics that take into account both diffusion magnitudes and directions to optimize the seeding positions and density of streamtubes in order to reduce the visual clutter. Two recent advances in tensor calculus and tensor dissimilarity metrics are utilized: the Log-Euclidean and the J-divergence. Results show that adaptive seeding not only helps to cull unnecessary streamtubes that would obscure visualization but also do so without having to compute the culled streamtubes, which makes the visualization process faster.

Citation Download Citation

Yonas T. Weldeselassie, Ghassan Hamarneh, and Daniel Weiskopf "Tensor dissimilarity based adaptive seeding algorithm for DT-MRI visualization with streamtubes", Proc. SPIE 6509, Medical Imaging 2007: Visualization and Image-Guided Procedures, 65092M (22 March 2007); https://doi.org/10.1117/12.710417

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available