[Insight-users] registration

[Luis Ibanez]

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
>>>>
>>
>>
>>
>>
> 
>