Progress Report

December 12^th, 2005

Rough Overview: Progress in a Nutshell

• Journal paper draft:
  • Progress in terms of text, embellishments, clarifications
  • Need more material on model building
  • Communication with a guest editor
• Reading on clouds overlap in hyperspace
• Experiments - a complete shift to MATLAB was successful
• Initial and planned experiments
• Student talks abstract re-worked, re-submitted

TMI Special Issue on Validation in Medical Image Processing

• Needed to enquire with regards to suitability of our work
• Using our recently-submitted work (ISBI 2006)
• The work is yet unpublished on its own right
• The approach itself has been part of the work presented in 2 conferences (IPMI, BMVC), but never yet in a journal

TMI Special Issue - Ctd.

• Our work contains many elements which have been listed as those that define the special issue's scope, namely:
  • Validation of image processing methods: registration
  • Performance comparisons: different approaches for non-rigid registration
  • Design of ground truth: expert-based
  • New methods for validation without ground truth
  • New validation criteria for estimating performances: 2 of them
• Guest editor described the work as "very relevant to the topics of the special issue"

TMI Special Issue - Ctd.

Full quote: "I think an expanded and updated version of the work you describe below (ISBI 2006) is very relevant to the topics of the special issue. I encourage you to submit it for review for consideration in the special issue. In order to do that, follow the regular IEEE TMI submission procedure, but also make a note for the editor in chief that it is to be considered for the special issue."

Overlap of Clouds in Hyperscape

• Reading and questioning involved
• Identified something valuable: HTML abstract:

"...techniques are broadly based on: (i) overlap based pattern synthesis which can generate a larger number of artificial patterns than the number of input patterns and thus can reduce the curse of dimensionality effect, (ii) a compact representation of the given set of training patterns called over lappattern graph (OLP-graph) which can be incrementally built by scanning the training set only once and (iii) an efficient NN classifier called OLP-NNC which directly works with OLP-graph and does implicit overlap based pattern synthesis..."

Incompatibilities with the Approach of Viswanath

• Suspicion that we can use none of the methods proposed in the paper from Viswanath
• We rarely need to increase the number of points that lie in space because we can generate as many synthetic example as we wish
• Building a model for overlap estimation is not applicable as we have one already
• The Next few slides show examples of the data in question, model synthesis in particular

Entropy and its Relationship to Graphs

• In attempting to estimate overlap, we are essentially dealing with point distances (i.e. graphs)
• Can gain insight from work on entropic graphs by Hufeza Neemuchwala and Alfred Hero
• What we are doing is related to the nearest neighbour graph approach to estimating entropy
• What is of interest is the calculation of entropy, not just in the context of one particular application as in the work above

C++/MATLAB Transition

• Wishing to become independent from VXL for more rapid implementation
• Code interpretation rather than compilation
• Experiments are slower to run, but can expolit multiple workstations
• The transition was a success, so there is now a full framework which is C-independent.
• Concerned with re-use of previous code, going back to the beginning of the year

Early Experiments

• Got the AAM viewer working again, so can produce larger (as large as desired) AAM models, unlike the small one in the presenatation. The issue was a bug in the code, which could not cope with long file paths.
• Finished the first large batch of experiments (about a dozen computers overnight)
  • These demonstrate that we can reproduce the results we had in the ISBI paper, but do so in MATLAB
  • Code includes the derivation of error bars and sensitivity, if needed
  • It will be easier (thus quicker) to implement other distance metrics
  • I am still getting the 'dip' at the start where the sample points on the graph are denser

Planned Nightly Experiments

• Our aim is to investigate a variety of methods for computing the overlap between data clouds
• Investigate/implement various distance metrics:
  • Shuffle
  • Euclidean
  • Fast Euclidean
• Also run a set of experiments which compare the performance of modified definitions of Generalisation and Specificity
• This no longer makes them Generalisation and Specificity, but useful for the sake of the argument

Planned Nightly Experiments - Ctd.

• Other methods/approaches to aggregating/interpreting image distances:
  • Case 1: Mean of all distances (accumulate distances between all points in the clouds) instead of minimum of distances (both Euclidean and shuffle)
  • Case 2: kNN - varying values of k (both Euclidean and shuffle)
  • Case 3: Entropy-based
  • Case 4: Shuffle and Euclidean for the definitions we have been using thus far (minimum of distances)
• The Next few slides show examples of the data in question, model synthesis in particular

Warp Examples - #1

Warp Examples - #2

Warp Examples - #3

Warp Examples - #4

Warp Examples - #5

Warp Examples - #6

Warp Examples - #7

Student Talks Abstract

• Shelagh received a different abstract
• Previous abstract made poorer re-use of an existing paper (ISBI 2006 submission)
• View abstract as plain-text

Next Steps

• Journal paper draft
  • Writing up
  • Expansion of experiments
  • Assemblage of text covering model-building aspects
• Further experiments with cloud overlap and distance metrics
• Absence January 20^th-29^th; no absence during Christmas