User:Jadrian Miles/Paper list

Automatic Shape-Sensitive Curve Clustering (§4.1.2) --- distance measure definition, distributed clustering algorithm, spectral clustering refinement, comparison to other techniques. Base on q-ball and DTI tractography from DTK; use Mori's atlas for ground truth? Moberts et al.'s ground truth clusterings? [moberts/van_wijk/vilanova might have a ground truth. Song Zhang had a clustering ground truth paper. Cagatay played with this at some point. bang not huge, how big is the buck? but I could be convinced -- not sure this is a vis paper? but it could be. Application of curve similarity to other areas (bat flight trajectories) would be convincing at Vis too.]

An Orientation-Aware Boundary Surface Representation of Space Curve Clusters

• 'Contributions:
  1. "Natural" representation of clusters of curves, useful for higher-level operations.
  2. Superior results versus naive algorithms (and previously published work to solve the same problem? "Competitors" to consider include Gordon's crease surfaces, flow critical surfaces, etc.).
• Proofs:
  1. Prose argument that contrasts cross-section-based boundary surfaces to other representations of curve clusters. A bunch of curves is a mess and does not lend itself to operations on the cluster volume (smoothing, joining, etc.). Median curves have no width. Rasterization creates surface artifacts and loses orientation information. Alpha shapes lose orientation information.
  2. Alpha shapes is the main "competitor". Expected results show topological defects resulting from a global choice of alpha. Run both algorithms on phantom and real data and discuss features.

A Sparse, Volumetric Representation of Space Curve Clusters (§4.1.4) --- the benefit of the initial form of the macrostructure model is its ability to reconstruct its input curves. The evaluation on this should be relatively easy, as there is no comparison to other techniques. A manual clustering is acceptable but automatic clustering that implies some bound on reconstruction error would probably be better. Good for Vis, SIGGRAPH, EG, EV, I3D, ISMRM. [but why does anyone care? This could be a way of establishing the minimal information required to represent brain datasets... but again, does anyone care about that? Must find related work. dhl suggests that this may be "importance filtering" for curves, but what's the benefit?]

Automatic Tractography Repair / "Healing a Broken Tractogram" --- ISMRM poster: demonstrate repairing broken curves by clustering, macrostructure generation, and then bridging gaps (§4.1.5).

Automatic Tractography-Based DW-MRI Segmentation --- using DTI/QBI, automatic clustering, and simple macrostructure adjustment (dilation, splitting, merging, bridging gaps, §4.1.5), segment the WM and compare to some ground truth. Mori's atlas?

[The above two could each be ISMRM posters or talks, and should be quickly followed up by an MRM paper.]

A ??? (§4.2.1) --- generating synthetic images from the macrostructure model. Who would care about this? Possible improvement over Leemans, et al. due to gap-filling? [use data matching to support that the model is good, and wave hands about the usefulness of the higher-level model -- has a bigger bang feel than the clustering one]

[check out Ken Joy's multi-material volume representation stuff (last author) -- tvcg, I believe or maybe TOG]