[Insight-users] vessel segmentation

[Karthik Krishnan]

We've had to deal with anisotropic CT data as well (2.5mm) etc and have run
into similar issues.

You could try out using tracking based methods. An example is that by
Stephen Aylward which follows the centerline (on the intensity ridges),
starting from a seed point.

Colliding fronts on your vesselness measure (Frangi/Sato etc) might be
suitable if you have the start and end points of your vessel.

----
Alternatively, you can resample your data to isotropic spacing before going
about extracting vessels.


On Fri, Feb 13, 2009 at 3:04 AM, Bryn Lloyd <blloyd at vision.ee.ethz.ch>wrote:

> Hi,
>
>
> I am trying to segment small vessels from MRA images (in mice).
>
> The images typically have a spacing of [0.1, 0.1, 0.5] mm.
>
> I find I can detect the vessels which are oriented more or less parallel to
> the z-direction quite well. The in-slice (x-y) vessels are much more
> difficult.
>
> One of the problems is that a vessel might e.g. be at position [10,10] in
> one slice and at [15,15] in the next. This large "jump" is due to the large
> spacing in z-direction.
>
>
> Can anybody suggest an approach which can handle such situations?
>
> I have tried region growing on "vesselness" feature images, and thought
> about using the CURVES algorithm. The problem with CURVES (I expect) is that
> there will be holes where the level set can leak out.
>
> In addition, the size of the vessels can be very small (approximately 2-5
> pixels diameter). According to a mail from Luis, CURVES needs at least 5
> pixels to work well. Can one trick CURVES to work also for smaller vessels,
> e.g. through (in-slice) re-sampling using nearest neighbor interpolation?
>
>
> Thanks
> Bryn
>
>
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-- 
Karthik Krishnan
R&D Engineer,
Kitware Inc.
Ph: 518 371 3971 x119
Fax: 518 371 3971
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