[Insight-users] registration

[imho]

Hi Luis,
thank you for this complete answer

 >Note that the group developing this techniques is
 >actually doing Liver registration for image guided
 >intervention.

in fact that's what we would do too, the final objective is to have a 
precalculated liver 3D model - calculated from segmentation, where the 
surgery can localize tumours- and then  match this to a "live" model 
digitalized with camera per example during the operation, and not to an 
image.

Maybe my explanations are not really clear, my English is poor.

But thanks for yours

Luis Ibanez wrote:
> 
> Hi Imho,
> 
> I'm affraid that what you are looking for, is not
> available in the toolkit at this point.
> 
> The Model to Image registration approach is not
> a Point based registration. It is not associating
> points from two point sets as ICP does.
> 
> Instead you have a geometrical model and you
> define your own metric that will measure how well
> the model match to an image.
> 
> PointSets are one among many other possible
> representations of SpatialObjects.
> 
> You may want to look at the Model Based Registration
> section of the SoftwareGuide
> 
> Section 7.14, pdf-pages 234-244.
> 
> 
> This algorithm is fitting a geometrical model to
> an image.
> 
> 
> The problem with ICP is that there is a lot of
> time spent in finding point correspondances.
> Actually most of the time goes wasted in this
> stage of the algorithm. This time grows to the
> square of the number of points unless you use
> some kind of auxiliary data structure like a
> PointLocator.
> 
> If you imagine to build an image with a distance
> map of one of the point sets, and then registering
> the other point set against this image, you will
> visualize better why Model to Image registration
> may be more efficient than Model To Model registration.
> 
> Note that the group developing this techniques is
> actually doing Liver registration for image guided
> intervention.  In this context, what you want to do
> is to create a geometrical model of the Liver, using
> the SpatialObjects available in:
> 
>             Insight/Code/SpatialObject
> 
> Then register such model against an image.
> 
> Modeling is probably the next step in the evolution
> of medical image algorithms since it allows to
> introduce anatomical meaning to the data representation.
> 
> Note that PointSets and Image are not aware of
> representing a Liver, a Hearth or a Lung. SpatialObjects
> on the other hand can be built with growing complexity
> using a CSG-kind of grouping, making possible to generate
> meaninful shapes.
> 
> 
> 
> Please let us know if you have further questions.
> 
> 
> 
> Thanks
> 
> 
>   Luis
> 
> 
> 
> ----------------------------------------
> 
> imho wrote:
> 
>> Hi Luis,
>>
>> you said that Iterative Closest Point wasn't implemented, so wich 
>> algorithm is it? Iterative Inverse Perspective? Another one?
>>
>> thanks
>> imho
>>
>> Luis Ibanez wrote:
>>
>>>
>>> Hi Imho,
>>>
>>> At this point probably the more interesting
>>> method is Model to Image registration.
>>>
>>> This is done right now in ITK by using the
>>> SpatialObject classes for representing
>>> geometrical models. An example on Model
>>> to Image registration is available in the
>>> SoftwareGuide.
>>>
>>>
>>> Does this help to answer your question ?
>>>
>>>
>>>    Luis
>>>
> 
> 
> 
>