[Insight-users] registration

[imho]

Hi Luis,

in fact like I've first said the final objective is "How can I register 
two liver datasets " but I asked for other types of registrations first 
to practise a little with it.

Thanks for yours answers

Luis Ibanez wrote:
> Hi Imho,
> 
>  > Won't this do what I want?
> 
> Well,
> You were asking for ICP and PointSet registration...
> Deformable registration will not do that...
> 
> 
> However, if you rephrase your question as:
> 
>     "How can I register two liver datasets ?"
> 
> Then, Deformable registration may be an option.
> 
> If you want to pursue this possibility, you may
> want to look at the following methods
> 
> 1) Demon's deformable registration
> 2) FEM based, deformable registration
> 
> Note that these methods are computationaly intensive.
> In the case of FEM you may want to take advantage
> of their support for Multi-Resolution.
> 
> Both methods are described in the SoftwareGuide.
> 
> 
> Please let us know if you have further questions.
> 
> 
>    Thanks
> 
> 
>       Luis
> 
> 
> -------------------------------------
> 
> imho wrote:
> 
>> Hi Luis,
>>
>> in the Software Guide there is a chapter about deformable registration 
>> page 229.
>> Won't this do what I want?
>>
>> Thanks
>>
>>
>>
>> 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
>>>>>
>>>
>>>
>>>
>>>
>>
>>
> 
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