FW: [Insight-users] GeodesicActiveContourImageFilter...

Luis Ibanez luis.ibanez@kitware.com
Fri, 22 Nov 2002 10:14:36 -0500


This is a multi-part message in MIME format.
--------------020104080605090300060102
Content-Type: text/plain; charset=us-ascii; format=flowed
Content-Transfer-Encoding: 7bit


Hi Jean-Phillipe,


The edge potential map can be built as exp(-x)
of the magnitude of the image gradient. In this
way, places with high gradients will result in
values close to zero in the edge potential map.

You may want to use the filter EdgePotentialImageFilter:
http://www.itk.org/Insight/Doxygen/html/classitk_1_1EdgePotentialImageFilter.html

for generating the edge potetial from the image gradient.

You can achieve the same effect by connecting together
the two following filters:

1) GradientMagnitudeRecursiveGaussianImageFilter
2) ExpNegativeImageFilter

The first will compute the magnitude of the gradient
from the input image, while the second will apply
pixel-wise the functor  exp( -x ).

---

The fornt propagation is quite sensitive to the
distribution of values in the edge potential. You may
want to play a bit with the example application:

       FastMarchingLevelSet

and get a felling of how the edge potential map
should look like.

For example if you load in this application the
BrainWeb dataset :   brainweb165a10f17.mha

The output of the NegativeExponential filter
will show you the edge potential map.

For this particular data set, for example,
if you compute derivatives using a sigma of 1.2
(the default), the resulting edge map will be
quite noisy.  Increasing sigma to about 2.0
improves the continuity of the derivative
and hence make a more consistent edge map.
(slice 97 is shown in the attached jpg file)


Hope this helps,


Luis


========================================
Jean-Philippe Guyon wrote:
> Hello Thomas,
> 
> I have not been able yet to get a useful segmentation out of the
> GeodesicActiveContourImageFilter. It seems that the front that propagates
> always goes beyond the boundaries of the object, even though the edge
> potential image has quite noticeable edges with values close to zero, and
> the surrounding environment has values close to one ( area where the
> gradient value is low, refer to the documentation
> http://www.itk.org/Doxygen/html/classitk_1_1GeodesicActiveContourImageFilter
> .html ). There are still a few discontinuities in the edges through which
> the front could eventually propagate, but I was expecting that setting the
> smoothness constraint to a pretty high value should prevent the propagation
> through these gaps. Since the edge potential image that I feed into the
> filter seems to be valid, I assumed that something must be wrong with the
> derivative images that I feed into the filter.
> 
> Please let me know if you have any idea.
> 
> Thank you,
> 
> ps:
> the parameters I use are the following:
> Number of iterations: 50
> TimeStepSize: 0.5
> LengthPenaltyStrength: 0.5
> InflationStrength: 0.1
> NarrowBanding: true
> 
> 
> -----------------------
> Jean-Philippe Guyon
> home: (919) 929-4132
> work: (919) 843-4921           Department of Radiology, CB# 7515
> piloo@unc.edu                  UNC-Chapel Hill
> http://www.pilooarena.fr.st    Chapel Hill, NC 27599-7515
> 
> -----------------------
> 
> -----Original Message-----
> From: Th. Boettger [mailto:t.boettger@dkfz-heidelberg.de]
> Sent: Friday, November 22, 2002 4:21 AM
> To: Jean-Philippe Guyon
> Subject: Re: [Insight-users] GeodesicActiveContourImageFilter...
> 
> 
> Hallo,
> 
> If you are already using the GeodesicActiveContour, do you use it with
> 3D data and did you have any success? I tried the filter, but could not
> get any useful results out of it.
> 
> Thanks
> Thomas Boettger
> 
> 
> 
> 
> Jean-Philippe Guyon wrote:
> 
>>Hello,
>>
>>I am using the GeodesicActiveContourImageFilter to segment uterus fibroids
>>in 3D. I was wondering if the derivative images that have to be set as
>>
> input
> 
>>into the filter need to be normalized. It would be helpful if it was
>>mentioned in the documentation.
>>
>>Thank you in advance for your help.
>>
>>Cheers,
>>
>>-----------------------
>>Jean-Philippe Guyon
>>home: (919) 929-4132
>>work: (919) 843-4921           Department of Radiology, CB# 7515
>>piloo@unc.edu                  UNC-Chapel Hill
>>http://www.pilooarena.fr.st    Chapel Hill, NC 27599-7515
>>
>>-----------------------
>>
>>_______________________________________________
>>Insight-users mailing list
>>Insight-users@public.kitware.com
>>http://public.kitware.com/mailman/listinfo/insight-users
>>
> 
> 
> 
> --
> Dipl.-Inform. Thomas Boettger
> Deutsches Krebsforschungszentrum         (German Cancer Research Center)
> Div. Medical and Biological Informatics H0100    Tel: (+49) 6221-42 2328
> Im Neuenheimer Feld 280                          Fax: (+49) 6221-42 2345
> D-69120 Heidelberg                            e-mail: t.boettger@dkfz.de
> Germany                      http://www.dkfz.de/mbi/people/thomasb.shtml
> 
> 
> _______________________________________________
> Insight-users mailing list
> Insight-users@public.kitware.com
> http://public.kitware.com/mailman/listinfo/insight-users
> 
> 



--------------020104080605090300060102
Content-Type: image/jpeg;
 name="EdgePotentialMap.jpg"
Content-Transfer-Encoding: base64
Content-Disposition: inline;
 filename="EdgePotentialMap.jpg"
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--------------020104080605090300060102--