Shuffle Distance and Symmetry
R. S. Schestowitz1
April 28th, 2005
Imaging Science and Biomedical Engineering
- We currently evaluate shuffle distance from shuffle distance images
that work in a single direction.
- The direction is dependent on the implementation and is arbitrary.
- We might wish to use information from a shuffle distance image that
goes in both direction.
- Averaging of the resulting two images seems like the most basic idea.
- The aggregated shuffle distance image seems to be affected slightly.
- In the case of the brain:
- Skull misalignment is highlighted equally well for both original images.
- More brighter shades appear, but their intensity is lower because
of the averaging.
- Evaluation and comparison between symmetrical and asymmetrical measures
- One can evaluate the performance from the distance (average pixel
intensity) or from the raw images, which are too large to handle en
- By taking an image set and slicing it into two groups, a large number
of pairings becomes available.
- Evaluation can also be made by using the existing task of model evaluation.
However, correct solutions are scarcely known.
- If ground truth was available, then performance of a symmetric measure
and an asymmetric one would be comparable.
- Can an artificial and quick test be conducted? Perhaps with synthetic
- One problem with measuring performance is that the nature of shuffle
is quite 'organic'. It does not compute anything that is an inherent
characteristic the data.
- One possibility is to plot results of some kind for shuffle in either
direction and in both. By looking at the 3 curves, conclusions can
be drawn. But the question then becomes: ``What results should
- Mean intensity of the images is expected to have the following relationship:
- Since the above holds, the question then becomes: ``How do the
different intensity values spread within the shuffle distance images?''.
Also, it would interesting to decide on where high intensities are
most helpful. For example, does one want high intensities around the
skull or in the centre of the brain? Many parameters control the behaviour
for a given set of data and, quite clearly, there is an element of
art in parameter selection.
- A more correct way of handling distances and using the shuffle transform
is by computing it in both direction. It is potentially twice as expensive
in terms of resources, but in practice, possible speed-ups exist.
- Averaging of the two shuffle distance images is weak. A more
cunning approach will use the correlation between the two images to
produce meaningful and valuable information.
- For instance, the inability to 'fit' pixels (a discrepancy) in both
directions, implies that the penalty should perhaps be raised.
- Salt-and-pepper noise might in some cases work in favour of one side,
but not in favour of the other. In a sense, shuffle distance can detect
inconsistency in the data as it detects unexpected pixels without
a local region and assumes a pair of images should be similar.
When results that are somehow bound to ground-truth become available,
different shuffle distance approaches can be compared. The aim is
to fit a shuffle-based method to a somewhat 'correct' solution so
that it emulates more reliable methods.
Shuffle Distance and Symmetry
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