[Insight-users] non-gradient base optimizer for 3D registration with versors

[Luis Ibanez]

Hi Moti,


I missed your last question:

Yes,
you will find an example of the use of the Powell optimizer for
3D image registration in the directory:

    InsightApplications/
     LandmarkInitializedMutualInformationRegistration


   Regards,


      Luis


-------------------
Moti Freiman wrote:
> Hi Luis,
> Thanks for your quick response.
> 
> 1. I assumed that i have a bad setting of the parameters, but I've no 
> idea from where to start.
> My images are T2 mri images which were taken in two times. Firstly i 
> used the mutual information multi resolution example provided with the 
> ITK to register them. I initialized the transform with the identity 
> transform. and the images registered well.
> Then I started to modify the multi resolution example to working with my 
> similarity measure.
> The first step was to use the Powell optimizer instead of the versor  
> optimizer , but staying with the 3d rigid versor transform, and 
> initialization to the identity transform. In the first evaluation of the 
> similarity measure i got the exception. The scaling parameters and 
> others were the same as appear in the multi resolution MI registration 
> example.
> I also tried to change the similarity measure from MI to my similarity 
> measure, but after 4 evaluations, i got  the exception when it starts to 
> apply the rotation about the Z axiz.
> 
> 2. Is the powell optimizer can work with the versor transform, or just 
> with euler3D transform ?
> 3. Is there anywhere  example of 3D registration with the powell optimizer?
> 
> Many thanks,
> Moti 
> 
> On 4/15/07, *Luis Ibanez* < luis.ibanez at kitware.com 
> <mailto:luis.ibanez at kitware.com>> wrote:
> 
> 
>     Hi Moti,
> 
> 
>     Here are a couple of suggestions:
> 
> 
>     1) You can use the OnePlusOne evolutionary optimizer.
>         This optimizer does not require gradients in the
>         cost function. Note that the versor rigid 3D transform
>         will map its parameters to the Versor space, so that
>         you stay in the unit sphere.
> 
> 
> 
> 
>         but.. before you get rid of the Powell optimizer,
>         note that you may be simply using a bad set of
>         parameters for it:
> 
> 
> 
>     2) The message that you are getting from the Powell optimizer
>         can be related to:
> 
>           A) Poor initialization of the transform.
> 
>              How are you initializing your transform ?
>              Have you verified that the initial transform
>              give you a large overlap of the fixed and moving images ?
> 
> 
>           B) Using steps that are too large
> 
>              How many iterations does the optimizer performs before
>              you get the exception ?
>              What parameters are you passing to the optimizer
> 
> 
> 
>           C) Lack of using a right combination of parameter scaling.
> 
>              What values are you setting for the parameter scaling?
>              This is very important, since it compensates for the
>              difference in the dynamic range of the versor components
>              versus the translation components.
>              You may find useful to read the description of this
>              issue in the ITK Software Guide:
> 
>                  http://www.itk.org/ItkSoftwareGuide.pdf
> 
> 
> 
> 
>         Regards,
> 
> 
>             Luis
> 
> 
>     ---------------------
>     Moti Freiman wrote:
>      > Hello,
>      > I'm trying register two 3D volumes within the ITK framework using
>     a new
>      > similarity measure which has no analytical gradients.
>      > Since the 3D versor optimizer in itk is based on a gradient decent
>      > optimizer. i cannot use it with my similarity measure.
>      > I tried to use the Powell optimizer with 3DRigidVersor transform
>     but I
>      > got the following message:
>      > "All the points mapped to outside of the moving image"
>      > It seems that powell is not optimized for the versor space.
>      > 1. Is there any option to build a non-gradient based optimizer
>     for the
>      > versor space as it done for gradient decent optimizer?
>      > 2. Any other suggestions to which elements i should use in my
>      > registration process given that my similarity measure has no
>     gradients?
>      > Thanks,
>      > Moti
>      > --
>      > __
>      > Moti Freiman, Ph.D Student.
>      > Medical Image Processing and Computer-Assisted Surgery Laboratory.
>      > School of Computer Science and Engineering.
>      > The Hebrew University of Jerusalem Givat Ram, Jerusalem 91904,
>     Israel
>      > Phone: +(972)-2-658-5371 (laboratory)
>      > WWW site: http://www.cs.huji.ac.il/~freiman
>      > <http://www.cs.huji.ac.il/%7Efreiman
>     <http://www.cs.huji.ac.il/%7Efreiman>>
>      >
>      > --
>      > __
>      > Moti Freiman, Ph.D Student.
>      > Medical Image Processing and Computer-Assisted Surgery Laboratory.
>      > School of Computer Science and Engineering.
>      > The Hebrew University of Jerusalem Givat Ram, Jerusalem 91904,
>     Israel
>      > Phone: +(972)-2-658-5371 (laboratory)
>      > WWW site: http://www.cs.huji.ac.il/~freiman
>      >
>      >
>      >
>     ------------------------------------------------------------------------
> 
>      >
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> 
> 
> 
> 
> -- 
> __
> Moti Freiman, Ph.D Student.
> Medical Image Processing and Computer-Assisted Surgery Laboratory.
> School of Computer Science and Engineering.
> The Hebrew University of Jerusalem Givat Ram, Jerusalem 91904, Israel
> Phone: +(972)-2-658-5371 (laboratory)
> WWW site: http://www.cs.huji.ac.il/~freiman