[Insight-users] Mattes Mutual Information

[Tim Allman]

Thanks Matt and Brian for the information.

I have been working with ITK for about 6 months now and I am truly 
impressed with it from the perspectives of content and design. The 
templating and pipeline architecture are especially good. It's a great 
piece of work.

However, I have wasted at least a couple of these months because it has 
been so difficult to learn what's in ITK and how to use it. Essentially, 
the documentation is incomplete and scattered. The book is badly out of 
date having been written for ITK-2.4. The doxygen documents are not well 
maintained with many classes and functions uncommented. The information 
below appears nowhere else as far as I know. Certainly I never found a 
link to it on the ITK site. As for the changes between ITK-4.2 and 4.3, 
how can I find them without looking at the checkin logs? On top of this, 
the examples on the wiki and in the code could be updated and expanded.

I'm certainly grateful for the quick turnaround by the KitWare staff on 
questions on this list but it can be hard even to know what to ask. I am 
a seasoned programmer with experience in C++ going back 25 years and a 
lot of experience relevant to image registration but using ITK is a 
struggle. Perhaps it would be a good use of time to better organise the 
information, update the book - even if just in pdf form - and make the 
doxygen docs more meaningful.

Thanks again for all the help you have given.

Tim

On 13-01-09 11:19 AM, brian avants wrote:
> there is a brief summary of the v4 framework here:
>
> https://github.com/stnava/ITKv4Documentation/blob/wbir2012/ITKv4RefactoringNotes.pdf
>
> the most salient points:
>
> * all metrics are multi-threaded
>
> * all metrics may be computed from sparse or dense samples
>
> * all metrics may be used with low or high-dimensional (e.g.
> diffeomorphic, syn) transformations + generic pixel types
>
> * optimization / transformation framework reworked to be memory
> efficient i.e. avoid copying (potentially large) parameter vectors
>
> * new metrics for vector pixels (e.g. RGB) use same base classes
>
> * optimizers employ gradient step sizes with physical units, line
> search and have the ability to automate parameter scaling
>
> * composite transforms are now trivial.
>
> * "fixed" images can have transforms too (useful in unbiased
> registration strategies)
>
> * point set metrics can be combined with image metrics via
> itkObjectToObjectMultiMetricv4
>
> * some global optimization tricks are available in the
> MultiStartOptimizer ... very useful.
>
> there is also a GPU registration framework though that is a separate beast.
>
>
> brian
>
>
>
>
> On Wed, Jan 9, 2013 at 7:49 AM, Matt McCormick
> <matt.mccormick at kitware.com> wrote:
>> Hi Tim,
>>
>> The v4 registration framework slightly different, and the
>> MattesMutualInformationImageToImageMetric can only be used with the v3
>> framework and the MattesMutualInformationImageToImageMetricv4 can only
>> be used with the v4 framework (the v4 has many additional features).
>> Additionally, I believe the implementations may be slightly different.
>>
>> Thanks,
>> Matt
>>
>> On Wed, Jan 9, 2013 at 7:35 AM, Tim Allman <dr.tim.allman at gmail.com> wrote:
>>> There are two classes which implement the Mattes mutual information metric,
>>> MattesMutualInformationImageToImageMetric and
>>> MattesMutualInformationImageToImageMetricv4. There is little documentation
>>> or indication why I would want to use one or the other or why the v4 version
>>> was created. Could someone give me a little guidance?
>>>
>>> Thanks,
>>> Tim
>>>
>>> --
>>> Tim Allman, Ph.D.
>>> 35 Margaret Street,
>>> Guelph Ontario N1E 5R6
>>> Canada
>>>
>>>
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>> Visit other Kitware open-source projects at
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>>
>> Kitware offers ITK Training Courses, for more information visit:
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>>
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-- 
Tim Allman, Ph.D.
35 Margaret Street,
Guelph Ontario N1E 5R6
Canada

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