Zittersteyn, D (2013) Abstraction of Dense Line Data Using Shaped Integration and Field Compensation. Master's Thesis / Essay, Computing Science.
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Abstract
Recent advances in medical imaging have given us unprecedented access to information about the structure of the human brain. Using the DTI technique we can measure water diffusion in the brain at a microscopic scale, which is used to detect the orientation of white matter bundles. This data is then used to generate a large number of lines, representing these bundles in the brain. The resulting dense line data is however hard to visualize, due to occlusion and visual complexity. Many methods have been developed to alleviate these issues. They however mostly focus on more advanced visualization methods. The method we propose takes a different approach. We reduce the complexity of the dataset by abstracting certain features. An abstracted dataset shows the overall structure of the dense line dataset with as little as 1% of the number of lines. This abstracted dataset is then used to provide 3D context for a small number of selected lines from the original dataset.
| Item Type: | Thesis (Master's Thesis / Essay) |
|---|---|
| Degree programme: | Computing Science |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:52 |
| Last Modified: | 15 Feb 2018 07:52 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/10885 |
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