[Insight-users] Levelsets-v4 curvature term drives circles to squares (2-D) (+other issues)

Bill Lorensen bill.lorensen at gmail.com
Mon Dec 17 10:45:45 EST 2012


Make sure you build your program Release and not Debug.

On Mon, Dec 17, 2012 at 10:02 AM, Vaaksiainen <vaaksiainen at gmail.com> wrote:
> Update to this question and some credit to authors :) now that I've
> tried it, Chan-Vese levelset works nicely, and although a single
> iteration is slowish, the convergence is very fast from the initial
> circle initialization (~comparable with v3 snake in total time). I
> remark, that default value of Heaviside epsilon is kinda high, so
> changing that was my first solution. The original question with plain
> curvature or geodesic active contour remains open still.
>
> I try not to profile spammer, so I also attach here my thanks to
> Arnaud regarding my previous question
> (LevelSetEquationTermContainer->SetInput() ? ).
>
> Best,
>
> -Vaaksiainen
>
> 2012/12/17 Vaaksiainen <vaaksiainen at gmail.com>:
>> Hi,
>>
>> I'm learning Levelsets-v4 hard. Currently, after not getting the
>> convergence appreciated, I tried the following:
>>
>> I set up LevelSetEvolution following the
>> itkSingleLevelSetDenseImage2DTest -example, but I use only propagation
>> or curvature terms one at a time. I set the feature images unitary,
>> and initialize the levelset as small circles (equi-spaced).
>>
>> Problem: curvature term drives circles towards squares (really
>> sharp-edged NxN pixel squares eventually), while obviously should not.
>> Somewhat similar happens with the propagation term alone, where I find
>> speed along coordinate axes to be dominative.
>>
>> What I might be doing wrong here?
>>
>> Another question, is it possible to obtain (approximately) similar
>> behaviour using GeodesicActiveContourLevelSetImageFilter and
>> Levelsets-v4 framework with propagation, curvature and advection
>> terms. I tried, but obvious reasons (mentioned above) already
>> cancelled this study. Also: number of iterations per time unit seems
>> to be drastically lower (hundred folds) with the new framework.
>>
>> -V
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