Filtering of Small Components for Isosurface Generation
This addresses visualization clutter for users in medical imaging or scientific visualization, but it is incremental as it applies existing filtering techniques.
The paper tackles the problem of small distracting components in isosurfaces from scanned data like CT or MRI scans, and shows that simple prefiltering can remove these components without affecting the main visualization.
Let $f: \mathbb{R}^3 \rightarrow \mathbb{R}$ be a scalar field. An isosurface is a piecewise linear approximation of a level set $f^{-1}(σ)$ for some $σ\in \mathbb{R}$ built from some regular grid sampling of $f$. Isosurfaces constructed from scanned data such as CT scans or MRIs often contain extremely small components that distract from the visualization and do not form part of any geometric model produced from the data. Simple prefiltering of the data can remove such small components while having no effect on the large components that form the body of the visualization. We present experimental results on such filtering.