[Insight-users] Registering US -> CT

[N.E. Mackenzie Mackay]

Just as a follow up.

To elaborate on the importance of the registration method being able to 
handle no all the points mapping onto the image.  I will have an 
initial guess on where my US image should be.  I will then extract 
points from my model and then try to use PointSet-> Image registration. 
  The only problem is the region of points that I extract will be bigger 
then the image.  THis is because the initial guess is just that.  A 
guess.  To be fully confidant that the correct points are in the 
registration I would need to select all the points close to the image 
within a certain bounding box.

As for mutual Information I will post some images that I have got 
shortly.

Thank you again for all of your help.
Neilson

On Sep 14, 2004, at 10:33 PM, Luis Ibanez wrote:

>
> Hi Neilson,
>
> MutualInformation is mostly a region-based image metric.
> This means that its value gets better when the overlap
> between matching regions of the image is large. Mutual
> Information is not particularly well suited for matching
> thin structures since thir random sampling is unlikely
> to select many pixels belonging to those structures.
>
> In that sense you probably shouldn't expect much from Mutual
> Information for registering Bone, since bone structures are
> mostly shell-like and they don't fill large regions of space.
> E.g. large bones have their layers of cortical bone with high
> calcifications but their width usually cover just a couple of
> pixels in a CT scan.
>
> Given that you seem to have segmented the bone from the CT scan,
> it is probably worth to try a Model-to-Image registration approach.
> This can be done by taking points on the surface of your bone
> segmentation, and/or from a band around that surface, and using
> them to match the intensities (and structure) of the same bone as
> seen in the UltraSound images.
>
> Could you post a couple of the US images ?
>
> (e.g. you could put them in www.mypacs.net and let us know their
>  image ID).
>
>
> Depending on how the bone structures look like on the US image
> there may be different possible metrics to try in a PointSet to
> Image registration.
>
>
> BTW, when you start working in 3D, don't attempt to use Rigid
> transforms until you have manage to tune all the other parameters
> of the registration to work with simple translation transforms.
> It is more effective to deal with a single issue at a time.
>
>
>
> Regards,
>
>
>     Luis
>
>
>
> -------------------------------
> N.E. Mackenzie Mackay wrote:
>
>> Hi,
>>     I have tried for the last while to get a single ultrasound image 
>> to register to a CT volume.  Specifically try to get the bone of an 
>> ultrasound image and bone of the CT to register together.  Up to now 
>> I am having quite some trouble.
>> I have been able to segment the bone from CT and give an estimate on 
>> where the bone is in the ultrasound.  I am now trying to register 
>> those two images.
>> This is what I am using:
>> MattesMutualInformationImageToImageMetric - decided to use this 
>> because the registration was of two different modalities.  Couldn't 
>> use feature registration becuase to hard to segment ultrasound 
>> correctly
>>     - using 50 bins and %20-%100 of samples still doesn't give 
>> adequate results.
>> linearInterpolateImageFunction
>> RegularSetGradientDecentOptimizer
>> Euler3DTransform
>> Both images ( 3D CT and US ) are normalized before the registration.
>> I have used a maximum step ranging from 0.5-6.  And a min step of 
>> 0.005 - 1.
>> I have a good initial guess ( maximum 2cm away from correct with 
>> about 0- 30degrees of rotation)> I tested out the registration method 
>> in 2D and have had success.  When I use the exact same variables 
>> applied in 3D the registration is poor.
>> Does anyone have any suggestions?  I would be happy to provide a 
>> couple of images or actual code to show you what I am dealing with.
>> Neilson
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>
>
>
>