[Insight-users] Laplacian level set filter

[Joshua Cates]

Hello Radhika,

I would suggest that you look at the SpeedImage from the level-set filter
as a way to guide your parameter tuning.  The SpeedImage is precomputed
and sampled by the level-set solver as the force term guiding deformation
of the level-set surface.  Where the speed term is positive, the
level-sets will grow.  Where the speed term is negative the level sets
will shrink.  Try to adjust your parameters so that you see zero crossings
at the boundaries of the structures you are trying to segment.

You can access the speed term through a GetSpeedImage method on the
level-set filter _after_ the filter has been updated once (even with 0
iterations) and then write to a file or otherwise view it.  See the
documentation for more details.

Your conductance term for the anisotropic diffusion looks rather high but 
will vary with the amount of noise and content of your data.  Typically i 
start setting conductance at around 0.5.

Josh.

______________________________
 Josh Cates			
 Scientific Computing and Imaging Institute
 University of Utah
 Email: cates at sci.utah.edu
 Phone: (801) 587-7697
 URL:   http://www.sci.utah.edu/~cates


On Thu, 29 Jan 2004, Radhika Sivaramakrishna wrote:

> Hi Luis,
> I was trying to use the Laplacian level set (with anisotropic diffiusion as
> a preprocessing step) to improve an already available initial mask. These
> are 3D brain T2-weighted images.
>  
> I set the # of iterations for diffusion as 10
> Time step for diffusion = 0.125
> Conductance parameter for diffusion = 2.0
>  
> Curvature scaling = 1.0
> Propagation Scaling for Laplacian =  1.0
> # iterations for Laplacian = 10
> Isosurface value = 127.5
> Maximum rms error for laplacian = 0.002
>  
> The initial mask was quite approximate but the solution was a good starting
> point. However, after running with above settings, I found very marginal
> improvement.
> I was playing with some of the parameters but did not see significant
> improvement. 
>  
> So my question was, for a 3D MRI T2-weighted image,
>  
> 1)       what are the recommended values for the above parameters
> 2)       What is the range of reasonable values to "play with" for each of
> these parameters?
> 3)       What are the parameters for the Laplacian scaling that make the
> growing surface move faster? It seems to me that the advancement at each
> iteration is very less.
>  
> Thanks
> Radhika
>  
>  
>  
>  
>  
>                                                      
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