[Insight-users] 3D Vessel Extraction and Measurement

[Iván Macía]

Hi Luis,

As you say in the second approach, i was thinking on selecting the vessel
center candidate points by means of an eigenanalysis of the Hessian matrix
and then to perform multi-scale analysis only on those points to estimate
centerline and radius. I want this analysis to be based on an offset
medialness function where the medialness contribution is calculated using
the sum of gradient contributions in a circular neighborhood. For this I was
thinking in a ShapedNeighborhoodIterator, is this a good approach? Then for
every point the scale at which the response is maximum is chosen.

However, it is not very clear to me how to implement the multi-scale
analysis here because I only need to do it on certain points. Is there any
easy way to calculate the gradient gaussian response in the vecinity of one
point? Is there any other better approach? 

Thanks in advance

Ivan


-----Mensaje original-----
De: Luis Ibanez [mailto:luis.ibanez at kitware.com] 
Enviado el: miércoles, 18 de enero de 2006 14:41
Para: Iván Macía
CC: Insight Users
Asunto: Re: [Insight-users] 3D Vessel Extraction and Measurement




Hi Ivan,


You may want to try the application

    http://www.itk.org/HTML/Curve3DExtraction.htm

This application is distributed with the InsightApplications CVS checkout.
(or the .zip and .tgz file in the release).

Note that this application assumes that you are targeting
a particular diameter of vessels.

If what you need is to estimate the diameter of vessels in
the image, then you may want to run the Vesselness filter
for a range of different sigmas (which are associated)
to the vessel diameter) and then choose for each vessel
the sigma where you get the highest response.

Note that this is a typical Multi-Scale analysis.

The drawback is that this may not be a fast processing, since at all stages
you process the entire image.

A more effective approach may involved to just perform the multi-scale
analysis on pixels that you have already spotted as belonging to a vessel.
In that case you will have a two pass processing. First, to identify
prospective pixels belonging to vessels, then Second visit to pixels and
perform multi-scale analysis on them, in order to estimate their diameters.


Another method for segmenting vessels is to use itk SpatialObjects and
register/fit  them to your images. For this purpose you may use the itk Tube
SpatialObjects.


  Please let us know if you have further questions.



      Thanks



        Luis



------------------
Iván Macía wrote:
> Hi,
> 
> I'm trying to perform vessel segmentation on MR liver images using 
> some kind of medialness function to detect vessel centerlines and 
> estimate radius similar to the work of Krissian et al. on "Model Based 
> Detection of Tubular Structures on 3D Images".
> 
> I have found in itk classes for Hessian Matrix computation
> (HessianRecursiveGaussianImageFilter) and measures of vesselness based 
> on the eigenvalues of the Hessian Matrix 
> (Hessian3DToVesselnessMeasureImageFilter). I have also found a 
> VesselTubeSpatialObject suitable to represent the vessel information. 
> But it does not seem to be a class suitable for estimation of vessel 
> medialness, radius and some other properties.
> 
> I would like to know if there is any algorithm in itk that I could 
> use, if someone is working on it or some advice that could be useful 
> to implement one (probably in the form of an ImageToImageFilter).
> 
> Thanks in advance for your help
> 
> Iván Macía
> 


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