[Insight-users] divergence when setting a moving mask with mates MI

Tue Jul 12 13:10:01 EDT 2011

Hi Yann,

Since you are using a mask for metric calculation I don't think the

cause of your problem is the optimizer, I assume you are using the

ImageMaskSpatialObject after segmenting part of your image (?!!).

How do you initiate your mask? This can be tricky sometimes!

I had a similar problem in the past, the origin of the image mask

had a 256 pixles shift towards the center of the image, but this was

only detectable when used in a Free Form Deformation due to obvious

deformations induced only  in the lower right corner of the image!

When I used the SpatialObjectToImageFilter to see whether if my

mask was valid everything seemed fine but the results still were wrong.

If this is your problem it can be solved by shifting the mask towards

the origin of the image, a sample code for this would look like the
following,

  SOTransformType::OffsetType originOffset;

  originOffset[0] = -255;

  originOffset[1] = -255;

  maskSO->GetIndexToObjectTransform()->SetOffset(originOffset);

As for the LBFGSB optimizer you can read the following papers,

[1] R. H. Byrd, P. Lu and J. Nocedal.
A<http://www.itk.org/Doxygen314/html/classA.html> Limited
Memory Algorithm

for Bound Constrained Optimization, (1995), SIAM Journal on Scientific

and Statistical Computing , 16, 5, pp. 1190-1208.

[2] C. Zhu, R. H. Byrd and J. Nocedal. L-BFGS-B: Algorithm 778:

L-BFGS-B, FORTRAN routines for large scale bound constrained

optimization (1997), ACM Transactions on Mathematical Software,

Vol 23, Num. 4, pp. 550 - 560.

HTH,

Dawood

>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<<

>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<<

On Mon, Jul 11, 2011 at 9:51 PM, asertyuio <asertyuio at yahoo.fr> wrote:

>  Hy dawood, and thanks for your interest !
>
> Because I did not found any solution to my divergence problem,  I did
> abandon the mutual information metric. I try again today, and found what
> 'cause' the divergence.
> It comes only when I set a moving mask on the metric. No problem with the
> fixed mask. Although, I'm sure of my mask validity, because I'm doing an
> initialization with them, and I've no problem with others metrics.
>
> I join a animated gif showing it. First step is mask registration, second,
> image registration, with only a mask on moving image. The metric value
> increase, despite the optimizer is set to do the opposite.
>
> Setting both masks is important for me because I don't want to take account
> of the background outlines on the registration process.
>
> As I have mostly problems with the regular step gradient optimizer, I think
> the problem must be related to derivative computation in the metric, but I
> don't know.
> Could you confirm this ?
>
> I'm going to try the LBFGSB optimizer, but there is not much information on
> it on itk user guide.
>
> Best regards,
>
> Yann
>
> Le 03/07/2011 19:59, Dawood Al Masslawi a écrit :
>
> Yann,
>
>  Apparently I misread your outputs, sorry :)
>
>  Have you tried other optimizers? LBFGSB optimizer have proved to work
> well
>
>  with the Mattes MI.
>
>  Best regards,
>
>  Dawood
>
>
>  >>>>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<
>
>  >>>>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<
>
>  Hi Yann,
>
> If your initialized transform is precise (near perfect?!!) it's only natural
>
> that the optimizer would change less. Have you tried to set the minimum
>
> step length to a higher value?
>
> In the example you provided the metric value is decreasing, isn't that
>
> what you want?
>
> Your fixed and moving images and their intensity distribution also can
>
> effect the quality of the registration, it might be helpful to give us more
>
> information about your images.
>
> HTH,
>
> Dawood
>
>
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>*<<<<<<<<<<<<<<<<<<<<<<<<<<<<<*
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>*<<<<<<<<<<<<<<<<<<<<<<<<<<<<<*
>
> Hi all,
>
> I'm exploring the mattes mutual information metric with the similarity
> transform. I have big problems with the regular step gradient optimizer.
>
> First one, I initialize the transform quite precisely, so only small
> change are required, especially for scale and rotation. But I must put
> the scaling factor to 100 000, for a maximal step length of 2 to obtain
> change around 0.01 for each step. Why is there a x100 between 2/100 000
> (what I expect to be the scaled step length) and the one which really
> append ?
>
> Second one, the optimizer fails to minimize the metrics (the value seems
> to increase), despite the fact that minimizeOn is set, and that I see a
> clear behavior difference when maximizeOn is set.
> I've played a lot with parameters without significant improvement.
> Here is an example :
>
> 0   -0.0794786   [1.08213, 0.215013, 142.753, 93.1165, 19.484, -5.38814]
> 1   -0.0837397   [1.09388, 0.224094, 142.734, 93.1303, 21.0015, -4.08564]
> 2   -0.0781118   [1.10518, 0.231022, 142.713, 93.1446, 22.5308, -2.79707]
> 3   -0.07013   [1.11679, 0.230021, 142.688, 93.1515, 23.5889, -1.10016]
> 4   -0.060149   [1.13054, 0.220847, 142.661, 93.1574, 24.6365, 0.603225]
> 5   -0.0533438   [1.15086, 0.197135, 142.637, 93.1682, 26.0843, 1.98246]
> 6   -0.0552559   [1.16682, 0.181277, 142.611, 93.1705, 27.2256, 3.62452]
> 7   -0.0499306   [1.17806, 0.163796, 142.586, 93.1626, 27.8154, 5.53527]
> 8   -0.0436633   [1.18673, 0.144479, 142.567, 93.1464, 27.8202, 7.53499]
> 9   -0.0373628   [1.19785, 0.129059, 142.55, 93.1295, 27.8735, 9.53404]
> 10   -0.0307262   [1.21034, 0.0999255, 142.53, 93.1149, 28.3516, 11.4756]
> 11   -0.0280043   [1.21907, 0.0717341, 142.518, 93.0946, 28.3674, 13.4752]
> 12   -0.0261761   [1.22857, 0.0535265, 142.507, 93.0742, 28.5985, 15.4616]
> 13   -0.0226728   [1.23642, 0.0331298, 142.5, 93.0514, 28.5777, 17.4612]
> 14   -0.022181   [1.24189, 0.0185733, 142.495, 93.0282, 28.7283, 19.4553]
> 15   -0.0190938   [1.25188, -0.00835667, 142.492, 93.0041, 28.7346, 21.455]
> 16   -0.0196048   [1.25686, -0.0400155, 142.497, 92.9792, 28.4755, 23.4377]
> 17   -0.0188159   [1.2638, -0.0595771, 142.508, 92.9557, 27.975, 25.3738]
> 18   -0.0174128   [1.27541, -0.0936444, 142.496, 92.9314, 29.3307, 26.8434]
>
>   I've done a deep analysis of the metrics value on my images, and it
> is very regular, so the gradient approach seems to be good. The global
> minimum with exhaustive search is good also.
> I try the 1+1 evo optimizer, he is doing much better, but do not reach
> the desired minimum.
>
> I really don't understand where does this behaviour comes from !
>
> Does anyone have any idea of the step I could have missed up ?
>
> Thanks in advance,
>
> Regards,
>
> Yann
>
>
>
>
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