Progress Report
July 19th, 2005
Overview
- Bending energy
- Number of modes and syntheses in evaluation
- Euclidean distance variant
- Segmentation propagation and joint registration
- Set of experiments for a future ISBI 2006 submission
Bending Energy and Knot-points
- We select a knot-points set whenever applying CPS warps to an image
- We also consider a different number of knot-points when perturbing images
- Plotted is the mean bending energy, calculated over 10 random instantiations for a different number of knot-points (indicated by the numbers in the legend)
- Warp magnitude is overly high, hence not easily comparable across different numbers of knot-points
- Note: Y scale is logarithmic
Bending Energy and Knot-points Visually
![](bending_energy_and_knot_points.jpg)
The curves show a 'mis-behaved' increase in values since scale of deformations is ill-chosen
Synthetic Images in Evaluation
- Investigate the number of synthetic images which are necessary for evaluation
- Number of modes and number of instantiations not fully understood
- Shown in the next slide is an exploration of the model evaluation method
- The model being evaluated was constructed from a set of 38 non-rigidly registered images
Synthetic Images in Evaluation - Ctd.
- Figures show Specificity and Generalisation as a function of the number of model syntheses being generated for evaluation
- 3 curves are plotted to account for yet another parameter, namely the number of modes
- Important: This set is properly (though not perfectly) registered
- Yet to be compared against similar plots generated for a different model
- Later, a model will be constructed from images which are badly registered (otherwise said to be perturbed)
Synthesis and Number of Modes
![](gen_syntheses_modes_registered.jpg)
Generalisability for the different number of modes and an increasing synthetic set size. The model was built from a registered set
Synthesis and Number of Modes
![](spec_syntheses_modes_registered.jpg)
Specificity for the different number of modes and an increasing synthetic set size. The model was built from a registered set
Synthetic Images and a Perturbed Set
- Perturbed image set can be investigated to see if similar results are obtained regardless of the quality of registration
- Figures are drawn in the same way as previously done
- They also appear to behave similarly
- Further experiments can validate this test using different data
Synthesis and Number of Modes - Perturbed Set
![](gen_syntheses_modes_perturbed.jpg)
Generalisability for the different number of modes and an increasing synthetic set size. The model was built from a perturbed set
Synthesis and Number of Modes - Perturbed Set
![](spec_syntheses_modes_perturbed.jpg)
Specificity for the different number of modes and an increasing synthetic set size. The model was built from a perturbed set
IMage Euclidean Distance (IMED)
- Towards implementation if necessary and suitable
- Needs to be implemented in
C++
(VXL)
- This consistent implementation is necessary for comparability
- Explanation of the principles
- a great deal of basic geometry is involved
- use of a spatial coefficient to be robust to deformation
IMage Euclidean Distance (IMED) - Ctd.
- Computing the distance and angle between pixels (or angles between voxels)
- Nearby pixels will be treated as related (distance dependence)
- Similar to the shuffle distance in practical terms
- Related closely to image smoothing
- Implies that smoothing noiseless image assists computation of image distance
- Offered as an improvement over simple Euclidean distance
IMage Euclidean Distance (IMED) - Ctd.
- The method sounds plausible, but not convincing as work which is said to offer improvements
- Performance probably does not equate to that of the Hausdorff distance
- A pro is said to be the easy embedment in algorithms
- A limited number of examples of embedment are demonstrated with quantitative results
- Future work will make use of tengential distance as well
IMage Euclidean Distance (IMED) - Ctd.
- The group has not considered the shuffle distance yet
- Shuffle distance is expected to outperform this method
- Overall, not entirely convincing
Model Evaluation/Registration Assessment
- VXL code for 2-D registration appears to be unready
- Document on milestones - ISBI 2006 submission plan (PDF)
- Needs to be discussed or at least confirmed to be a reasonable plan
- Primary issue is feasibility because scale is currently a misfit
Segmentation, Registartion and Models
- Experiments which demonstrate the application of our methods to segmentation
- Approach involves the following stages:
- take a group of images
- build a model from this group of images
- warp (register) the images to improve their model
- segment one image in the image set
- use the model to propagate this segmentation to all other images in the set
Segmentation, Registartion and Models - Results
- It is possible to convince ourselves that this approach works
- Based on several results that have been accumulated (c/f experiments archive)
- There are several pitfalls (yet to be listed)
- The next slide contains an example experiment where propagation of small labels is estimated
Label Propagation Example
![](labels_after_10_iterations.jpg)
The automatic propagation of labels after only 10 iterations of registration (10-20 seconds in duration)
Weaknesses of the Approach
- MSD-based objective function works brutally on the data
- Yet, MSD-based OF does not identify the true correspondences
- Better performance achieved when a model-base objective function gets used
- If the experiments are run for too long, flat regions (background) get deformed
- Fitting to noise in data rather than improving the structures within
Weaknesses of the Approach - Ctd.
- Results are often far from satisfactory
- Their success if based on that of model-based registration (determinant minimisation)
- Registration is slow and unsuccessful in the MATLAB implementation
- It does roughly the right thing
- Capable of producing moderate appearance models
Summary/Conclusions
- Bending energy
- appears to increase quadratically as a function of warp magnitude
- warps must be made small for comparable experiments
- In model/registration evaluation:
- the number of modes accounted for does not matter much
- this may vary depending on the data and the size of the dataset
Summary/Conclusions - Ctd.
- Euclidean distance
- needs extra work on VXL code
- will not lead to much-desired results
- Segmentation propagation:
- feasibility "proof of concept"
- lacks accuracy and suffers from lack of constraints
- Agreement on scale of experiments towards a ISBI 2006 submission
Next Stages
- Awaiting results from registration in VXL, which is needed for extensive evaluation experiments
- Continued experiments to improve the ability to propagate segments based on registration/model-building
- Possibly test segmentation propagation in the context of 2-D registration
- Consider using segmentation to place control points more cunningly
- Add Liwei's Euclidean image distance to VXL if time allows