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. [I think that "natural" is not descriptive enough. I would try to refine that. Is the orientation a tangency constraint? There's something about it that seems important, but "orientation aware" doesn't quite seem like what it is... . I would give an example or two of the kind of higher-level operations you mean.
  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. [dhl mentions Ken Joy's work...] [define "superior". Faster? more accurate? If you mean more "natural" then this may be the same thing as above]).
• 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. [nice!]
  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. [I suspect that there are some other shrink-wrap approaches that might be competitors, unless alpha-shapes are always superior]

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 / Neuroimage paper / TVCG paper? Demonstrate the utility of the cluster boundary surface algorithm for repairing broken curves (§4.1.5).

• Title, Authors: "Healing a Broken DTI Tractogram with Curve Cluster Boundary Surfaces". Jadrian Miles and David H. Laidlaw.
• Contributions
  1. A slicing-based (or alpha-shape contraction--based) algorithm for generating a cluster boundary surface with orientation, spreading, and medial axis metadata.
  2. An algorithm for "sampling" novel un-broken curves from the cluster boundary surface model ("sparsifying")
  3. An algorithm for extrapolating the curve cluster along its axis ("lengthening")
  4. An algorithm for joining axially-aligned curve clusters based on extrapolation and refinement against underlying DWIs ("bridging")
  5. Maybe others: "fattening", "smoothing"
• Proofs of Contributions
  1. Demonstration (with figures) of the boundary surface algorithm on synthetic and real-world tractography data.
  2. Compare the proposed algorithm (which uses only the boundary surface, not tractography curves) against one that uses barycentric coordinates of the tractography curves that form a triangle about the seed point to propagate an interpolated curve. This comparison should result in an error measure.
  3. Demonstration (with figures) of the boundary extrapolation algorithm. I'm currently unaware of any competitors for this.
  4. Compare against local extrapolation models: tensor deflection, linear extrapolation, cubic spline extrapolation, smoothed Bezier extrapolation. Also compare QBI tractography on HARDI data against our algorithm on angular-subsampled DTI data.
• Figures
  1. Fig
  2. Fig
  3. Fig
• Related work
• Abstract
• Conclusions
• Methods

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]