[Insight-users] Questions about the fast marching and level set filters

[Luca Antiga]

Dear users,
dealing with sparse field and narrow band level sets. I know that sparse 
field method (at least in the implementation given by Whitaker in his 
library) ensures sub-voxel accuracy even with first order upwinding, by 
estimating level set position according to \phi and \nabla(\phi) and 
evaluating there all the quantities involved with evolution. Is this feature 
included in narrow band version?
Thanks

Luca


>From: Bjorn Hanch Sollie <bhs@pvv.org>
>To: insight-users <insight-users@public.kitware.com>, Joshua Cates 
><cates@sci.utah.edu>
>Subject: Re: [Insight-users] Questions about the fast marching and level 
>set filters
>Date: Thu, 6 Jun 2002 19:29:35 +0200 (CEST)
>
>On Tue, 4 Jun 2002, Joshua Cates wrote:
>
> > You can use the SparseFieldLevelSetImageFilter for applications that 
>only
> > care about a single level set (and you will almost certainly get some
> > speed advantage by doing so).  I
>
>Thanks for answering some questions.  I've been looking at the
>SparseFieldLevelSet filter examples, and as far as I can see, it
>requires the potential map (or target image) to have positive values
>on the inside of the region and negative values on the outside.  This
>is in contrast to the potential map for the ShapeDetectionLevelSet
>filter which wants values between 0 and 1.  My queston then is: What
>is the best way of making a potential map for the SparseFieldLevelSet
>filter from an edge image?  Just do a simple positive/negative
>threshold on some grey value?  Are there any recommended (better?)
>ways?
>
>-Bjorn
>--
>The History of the Universe
>Chapter 1: Bang!  Chapter 2: Sss...  Chapter 3: Crunch!
>The End
>
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