User:Jadrian Miles/Streamline clustering: Difference between revisions

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#* A curve's distance to a cluster is the minimum distance to any curve in that cluster.
#* A curve's distance to a cluster is the minimum distance to any curve in that cluster.
#* In each iteration,  with lowest minimum curve-to-curve distance to its closest cluster.
#* In each iteration,  with lowest minimum curve-to-curve distance to its closest cluster.
== Code ==
=== Distance Measurements ===
* Core functions
** <tt>dcc.m</tt> computes a single, symmetric distance between two curves, using <tt>pdcc.m</tt>.
** <tt>pdcc.m</tt> computes a set of point-to-point distances between two curves, with a few customizable options.
** <tt>dpc.m</tt> computes the distance from a single point to a curve.
* Helpers
** <tt>followCurve.m</tt> gives the point at a specified fractional index along a curve.
** <tt>distOnCurve.m</tt> gives the distance along a curve between two fractional indices.
* Graphics
** <tt>drawpdcc.m</tt> plots two curves and the point-to-point matches found on them by <tt>pdcc.m</tt>.
** <tt>drawpdccset.m</tt> plots all four variations of the asymmetric distance for two curves.

Revision as of 23:05, 26 March 2009

Tubegen generates an easy-to-parse .nocr file specifying points on streamlines.

  1. Pick a good dataset (Diffusion_MRI#Collaboration_Table) -- $G/data/diffusion/brown3t/cohen_hiv_study_registered.2007.02.07/patient120
  2. Run tubegen on it with modified parameters so it doesn't cull anything---this will result in ~100k curves, with an average of ~70 points per curve.
  3. Write a python script to divide the computation of the curve-to-curve distance matrix among many computers.
    • Try max and mean minimum point-to-curve distance in overlapping region as inter-curve distance measure. Or exponentially weighted mean a la cad.
      • See also cad's /map/gfx0/tools/linux/src/embed/utils/fast_distance_computing/src/ICurveDist/test
    • The per-curve script should return the assigned matrix line as well as a list of curves sorted by distance and annotated with the distance, for fast clustering.
    • After computing the upper half of the matrix, create an ordered list of curve-to-curve distances annotated with the curve pairs. Distributed w:quicksort? [1]
  4. Build up clusters until some termination condition: satisfactory number of non-singleton clusters, satisfactory median size of non-singleton clusters, etc. Or just run until you get one huge cluster, but store the binary cluster tree. It may be really skewed but maybe a tree rebalancing algorithm could help in post-processing.
    • Initialization: each curve is a singleton cluster.
    • A curve's distance to a cluster is the minimum distance to any curve in that cluster.
    • In each iteration, with lowest minimum curve-to-curve distance to its closest cluster.

Code

Distance Measurements

  • Core functions
    • dcc.m computes a single, symmetric distance between two curves, using pdcc.m.
    • pdcc.m computes a set of point-to-point distances between two curves, with a few customizable options.
    • dpc.m computes the distance from a single point to a curve.
  • Helpers
    • followCurve.m gives the point at a specified fractional index along a curve.
    • distOnCurve.m gives the distance along a curve between two fractional indices.
  • Graphics
    • drawpdcc.m plots two curves and the point-to-point matches found on them by pdcc.m.
    • drawpdccset.m plots all four variations of the asymmetric distance for two curves.