[Insight-developers] The Insight Journal-New Submission

[Stephen R. Aylward]

Hi Marius and Stefan,

Thanks for the feedback!

The new image-to-image-metric class (in addition to optionally providing 
a uniformly distributed, potentially masked, subsampling of the fixed 
image for metric computation) also allows a user to pass-in a list of 
fixed-image points to be used in computing the metric.  If we use the 
same data structure for the point list as you use in your subsamplers - 
  our classes should be partially compatible with what you've done.  The 
question is, should we also allow your sampler class to be plugged-in. 
  I think so - the user can then have the freedom to specify that 
re-sampling should occur every N iterations, etc.

We (particularly Luis) are still working on the optimizations at a 
furious pace.   Once the code settles a bit, we can give you cvs write 
access to integrate your code.   I think your modification deserve to be 
at the top level instead of in a derived class - it is a good idea!

The top-level metric code does check for when it is being used with 
bsplines transforms and bspline interpolators and provides appropriate 
general optimizations.  Also, the class has member variables that are 
bools that specify if the transform or interpolator are the bsplines 
versions - so derived classes can also perform related optimizations. 
Great minds think alike :)

Sorry for not completing the paper yet - been traveling for three 
weeks...family staying with me for the summer...my dog ate my laptop... 
I should have the final paper uploaded today/tomorrow.

Stephen

Marius Staring wrote:
> Hi all,
> 
> The refactoring of the registration framework is a very nice initiative.
> 
> We have a few remarks/suggestions:
> 
> 1. From the powerpoint presentation in the Insight journal we noticed 
> that the new OptImageToImageMetric now includes the functionality to 
> sample the fixed image, like the MattesMutualInformation used to do 
> already. We have implemented something similar. You may be interested to 
> inspect our implementation. We have tried to seperate the sampling 
> functionality from the ImageToImageMetric, by designing a set of filters 
> that accept an image as input and return a vector containing the samples 
> as output (in a so-called itk::VectorDataContainer). For this purpose we 
> wrote a base class, the itk::ImageSamplerBase, from which different 
> image samplers can inherit. We have implemented several of them: the 
> itk::ImageFullSampler (takes all voxels within mask), the 
> itk::ImageRandomSampler (which does the same as the 
> MattesMutualInformation class does), and some others. The ImageSampler 
> is just another component to be set in the metric, just like the 
> Interpolator, Transform, etc.
> 
> The code (metricwithsampler.zip) can be found at 
> www.isi.uu.nl/People/Stefan. The AdvancedImageToImageMetric inherits 
> from the ImageToImageMetric and supports setting an ImageSampler. Don't 
> mind the somewhat ambitious name, by the way :)
> The relevant methods are:
> - SetImageSampler() and GetImageSampler()
> - SetUseImageSampler() and GetUseImageSampler()
> - InitializeImageSampler()
> The provided code won't compile as is, since it contains some references 
> to other code that we use, see www.isi.uu.nl/Elastix.
> 
> 2. The same class includes also some functions that benefit from 
> transforms that have sparse Jacobians (compact suppport), such as the 
> B-spline transform. The code does essentially the same as the code in 
> the MattesMutualInformation, but can be re-used by all inheriting 
> metrics. The relevant functions for this are:
> - CheckForBSplineTransform()
> - TransformPoint()
> - EvaluateTransformJacobian()
> 
> If you are interested, feel free to adopt (parts of) the code. If you 
> have any questions, don't hesitate to contact us.
> 
> Stefan and Marius
> 
> 
> Insight Journal wrote:
>> Hello,
>> A new submission has been added to the Insight Journal.
>>
>> Title: Optimizing ITK’s Registration Methods for Multi-processor, 
>> Shared-Memory Systems
>> Author(s): Aylward, Stephen; Jomier, Julien; Barre, Sebastien; Davis, 
>> Brad; Ibanez, Luis
>> Abstract: This document describes work-in-progress for refactoring 
>> ITK’s registration methods to exploit the parallel, computation 
>> power of multi-processor, shared-memory systems.  Refactoring includes 
>> making the methods multi-threaded as well as optimizing the 
>> algorithms.  API backward compatibility is being maintained.  Helper 
>> classes that solve common registration tasks are also being developed.
>>
>>
>> The refactoring has reduced computation times by factors of 2 to 200 
>> for metrics, interpolators, and transforms computed on multi-processor 
>> systems.  Extensive sets of tests are being developed to further test 
>> operation and backward compatibility.
>>
>> Download the paper at: http://hdl.handle.net/1926/566
>> Review the paper at: http://insight-journal.org
>>
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> 

-- 
=============================================================
Stephen R. Aylward, Ph.D.
Chief Medical Scientist
Kitware, Inc. - Chapel Hill Office
http://www.kitware.com
Phone: (518)371-3971 x300