[Insight-users] Metric, optimizer choice

[Dawood Al Masslawi]

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


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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


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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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