[Insight-users] Fast marching and sigmoid coefficients

[Luis Ibanez]

Hi Samuele,


      Was that 2D image extracted from the same
      3D image that you are now processing ?


The heuristic on how to compute the Alpha and Beta coefficients
of the sigmoid is described in the ITK Software Guide:

     http://www.itk.org/ItkSoftwareGuide.pdf

In Section:

          9.3.1 "Fast Marching Segmentation"

In PDF-page: 569:

"The heuristic for finding the values
is the following. From the gradient magnitude image, let's call K1 the
minimum value along
the contour of the anatomical structure to be segmented. Then, let's
call K2 an average value of
the gradient magnitude in the middle of the structure. These two
values indicate the dynamic
range that we want to map to the interval [0 : 1] in the speed image.
We want the sigmoid to map
K1 to 0.0 and K2 to 1.0. Given that K1 is expected to be higher than
K2 and we want to map
those values to 0.0 and 1.0 respectively, we want to select a negative
value for alpha so that the
sigmoid function will also do an inverse intensity mapping. This
mapping will produce a speed
image such that the level set will march rapidly on the homogeneous
region and will definitely
stop on the contour. The suggested value for beta is (K1+K2)/2 while
the suggested value for
alpha is (K2-K1)/6, which must be a negative number. In our simple
example the values are
provided by the user from the command line arguments. The user can
estimate these values by
observing the gradient magnitude image."


-----
In short:

you need to visualize the Gradient Magnitude image with a
program that allows you to measure the intensity values
inside the object, and in the border of the object.



       Regards,


               Luis


-------------------------------------------
On Sat, Oct 17, 2009 at 10:59 AM, Samuele Dietler
<samuele_82 at hotmail.com> wrote:
>
> Hello itk users,
> i'm new user of itk and i have questions concerning the sigmoid filter used
> for computing the speed image for the fast marching filter. I have a
> 512X512X150 DICOM serie of images of a knee. I have found some correct alpha
> and beta for a 2D image(i can segment a bone in a slice) but when i apply
> these parameters to the 3D segmentation seems does not work( i wait for
> really long time without any results). There is a way for correctly
> computing the parameters alpha and beta on the sigmoid filter for a correct
> 3D segmentation of the images???
> Thank you,
> Samuele
>
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