No subject
Tue Jun 23 12:19:35 EDT 2009
if parameters of the optimizer need to be adjusted, or whether you may
have to review other components of your registration structure.
You will find examples on how to use Command Observers in almost
all the files in the directory:
Insight/Examples/Registration
Regards,
Luis
--------------------------------------------------------------------
On Sat, Aug 1, 2009 at 6:49 AM, J.X.J. <wat.a.phony at gmail.com> wrote:
>
> Hi everyone,
>
> I'm current using itk::BSplineDeformableTransform,
> itk::MutualInformationImageToImageMetric and itk::GradientDescentOptimizer
> to register 2D MRI images. My problem is that when running 2 phantom images
> the output image is almost exactly the same as the original moving image
> (nothing like the fixed image).
>
> I have normalized and smoothed the 2 input images using
> itk::NormalizeImageFilter and itk::DiscreteGaussianImageFilter with
> SetVariance(2.0). For mutual information the standard deviation is 0.4.
> Optimizer learning rate is 10.0, 200 iteration and with maximum on. This
> set
> up is kind of a mixture of a number of deformableregistration examples on
> default (as in I copied and pasted all parameter settings etc directly).
>
> Does anyone know what the problem could be? The code runs all 200
> iterations
> is thats an indication, also the metric value of each iteration is around
> 0.2 - 0.4 if that's any help.
>
> J.X.J.
> --
> View this message in context:
> http://www.nabble.com/Helo-with-bspline-mutual-information-gradient-descent-tp24768056p24768056.html
> Sent from the ITK - Users mailing list archive at Nabble.com.
>
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>
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--0015175ce0f0f7061e0470a9739a
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
<br>Hi J.X.J.<br><br>The first thing that you want to do is to instantiate =
a Command/Observer<br>and to print out the values of the Metric at every it=
eration of the optimizer.<br><br>Plot this values and/or send the output to=
the mailing list.=A0 <br>
<br>From the progression of the metric values it will be possible to determ=
ine<br>if parameters of the optimizer need to be adjusted, or whether you m=
ay<br>have to review other components of your registration structure.<br>
<br>You will find examples on how to use Command Observers in almost<br>all=
the files in the directory:<br><br>=A0 =A0=A0 =A0=A0=A0=A0=A0=A0=A0=A0=A0=
=A0=A0=A0=A0=A0 Insight/Examples/Registration<br><br><br>=A0=A0=A0 Regards,=
<br><br><br>=A0=A0=A0=A0=A0=A0=A0=A0=A0 Luis<br><br><br>
--------------------------------------------------------------------<br><di=
v class=3D"gmail_quote">On Sat, Aug 1, 2009 at 6:49 AM, J.X.J. <span dir=3D=
"ltr"><<a href=3D"mailto:wat.a.phony at gmail.com">wat.a.phony at gmail.com</a=
>></span> wrote:<br>
<blockquote class=3D"gmail_quote" style=3D"border-left: 1px solid rgb(204, =
204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;"><br>
Hi everyone,<br>
<br>
I'm current using itk::BSplineDeformableTransform,<br>
itk::MutualInformationImageToImageMetric and itk::GradientDescentOptimizer<=
br>
to register 2D MRI images. My problem is that when running 2 phantom images=
<br>
the output image is almost exactly the same as the original moving image<br=
>
(nothing like the fixed image).<br>
<br>
I have normalized and smoothed the 2 input images using<br>
itk::NormalizeImageFilter and itk::DiscreteGaussianImageFilter with<br>
SetVariance(2.0). For mutual information the standard deviation is 0.4.<br>
Optimizer learning rate is 10.0, 200 iteration and with maximum on. This se=
t<br>
up is kind of a mixture of a number of deformableregistration examples on<b=
r>
default (as in I copied and pasted all parameter settings etc directly).<br=
>
<br>
Does anyone know what the problem could be? The code runs all 200 iteration=
s<br>
is thats an indication, also the metric value of each iteration is around<b=
r>
0.2 - 0.4 if that's any help.<br>
<br>
J.X.J.<br>
--<br>
View this message in context: <a href=3D"http://www.nabble.com/Helo-with-bs=
pline-mutual-information-gradient-descent-tp24768056p24768056.html" target=
=3D"_blank">http://www.nabble.com/Helo-with-bspline-mutual-information-grad=
ient-descent-tp24768056p24768056.html</a><br>
Sent from the ITK - Users mailing list archive at Nabble.com.<br>
<br>
_____________________________________<br>
Powered by <a href=3D"http://www.kitware.com" target=3D"_blank">www.kitware=
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<br>
Visit other Kitware open-source projects at<br>
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<br>
Please keep messages on-topic and check the ITK FAQ at: <a href=3D"http://w=
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Follow this link to subscribe/unsubscribe:<br>
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</blockquote></div><br>
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