[Insight-users] GeodesicActiveContourImageFilter
Karthik Krishnan
karthik.krishnan at kitware.com
Mon Feb 12 17:23:22 EST 2007
On 2/12/07, Pui Yu Lau <pyl111 at rogers.com> wrote:
>
> Hi everyone,
>
> Thanks for the help in regards to the previous email. I sent a
> followup email on a further problem, which may have been deleted from
> the user list because I realized I started talking about vtk functions,
> thinking they were itk. Anyways, I'm hoping someone can help me in my
> problem:
>
> I am attempting to segment certain anatomical structures from 3D images
> that have been sliced. I'm segmenting the structures using
> GeodesicActiveContour. However, at the end of the geodesicactivecontour
> filter, I am left with a black and white image due to the binary
> filter. I was wondering if there was any way I can extract the contour
> of my structure, then use the contour and apply it to my original
> greyscale image (so it won't be black and white) and isolate the
> structure, while keeping the rest of the image in black, for example.
Sounds like a simple mask filter will do.. What exactly do you want here ?
Try reading the software guide to be a bit more familiar with the suite of
image processing algorithms available at your fingre tips
My main focus is on getting the contour extraction, and I have been
> recommended to look for some sort of morphological filter. Through the
> ITK guide, I looked up the binary and greyscale filters from the
> mathematical morphology as well as section 7.6 on extracting regions,
> but I cannot seem to find functions to do what I want. I was wondering
> if anyone has any idea on what I may have overlooked or for suggestions
> on tackling my problem differently.
>
> - PY
>
>
> Luis Ibanez wrote:
>
> >
> > Hi Pui,
> >
> > Please read carefully the description of this filter
> > in the ITK Software Guide.
> >
> > http://www.itk.org/ItkSoftwareGuide.pdf
> >
> > You seem to have missed many of the important feature
> > of the example. It may be convenient for you to read
> > the previous sections on Level Sets. In particular the
> > section on the FastMarching filter and the ShapeDetection
> > filter.
> >
> >
> > The source code of the GeodesicActiveContour example
> > is available at:
> >
> >
> > Insight/Code/Segmentation/
> > GeodesicActiveContourImageFilter.cxx
> >
> >
> > In lines 319-320 you will find:
> >
> > geodesicActiveContour->SetInput( fastMarching->GetOutput() );
> > geodesicActiveContour->SetFeatureImage( sigmoid->GetOutput() );
> >
> >
> > Where the SetInput() method is setting the initial level set
> > to be used by the GeodesicActiveContour filter. This initial
> > level set is computed in this particular case, as the output
> > of a FastMarching image filter that is feed only with seed point.
> > The fast marching filter in this case is simply producing a
> > set of circles (or sphere) around the seed points.
> >
> > The SetFeatureImage() is connecting the image that is going
> > to be used for computing the speed image. This feature image
> > is computed as the sigmoid mapping of the gradient magnitude
> > image. The goal is to have an image that has low (dark) value
> > in the edges where you want your level set to stop, and high
> > (bright) values in the regions where you want the level set to
> > propagate rapidly.
> >
> >
> > The command line parameters passed to the example are described
> > in lines 128-137:
> >
> > if( argc < 10 )
> > {
> > std::cerr << "Missing Parameters " << std::endl;
> > std::cerr << "Usage: " << argv[0];
> > std::cerr << " inputImage outputImage";
> > std::cerr << " seedX seedY InitialDistance";
> > std::cerr << " Sigma SigmoidAlpha SigmoidBeta";
> > std::cerr << " PropagationScaling" << std::endl;
> > return 1;
> > }
> >
> >
> >
> > a) Input image (the image to be segmented
> > b) Ouput image (segmentation resulting from the level set)
> > c) (x,y) index coordinates of the seed point for FastMarching
> > d) initial distance for fast marcing
> > e) Sigma for the smoothing of the gradient magnitude filter
> > f) Alpha and Beta parameters of the Sigmoid
> > g) Propagation scaling parameter for the Geodesic Active contour
> >
> >
> >
> > In general the initial level set image could be *any* rough
> > segmentation of the object. You could produce such initial
> > segmentation by a manual delineation, a quick region growing
> > algorithm or a simple thresholding.
> >
> > It is quite common to implement two-stage segmentation methods
> > where the first stage only produces a quick and dirty segmentation,
> > that is then passed to a second stage where a level set method will
> > fine tune the segmentatino. Note that level sets cannot perform
> > magic on a poor initial segmentation. It is at least desirable for
> > the initial segmentation to be close to the edges of the object
> > to be segmented.
> >
> >
> >
> > Regards,
> >
> >
> > Luis
> >
> >
> > ==================
> > Pui Yu Lau wrote:
> >
> >> Hi,
> >>
> >> I'm a new user to ITK, and I've been trying to learn to use some
> >> of the segmentation filters. I've been looking at the Geodesic
> >> Active Contour method, and I am confused on how it actually works.
> >> Looking through the guide, it seems that it takes two initial image
> >> inputs, where the first one is the initial level set, and the second
> >> is the feature image. However, when running the example of
> >> GeodesicActiveContourImageFilter, the images used were
> >> BrainProtonDensitySlice.png and
> >> GeodesicActiveContourImageFilterOutput.png, where the latter does not
> >> exist. So, my question is, in this case, is the
> >> BrainProtonDensitySlice the feature image (input), while the
> >> GeodesicActiveContourImageFilterOutput is the initial level set
> >> (output)? If not, what exactly is the initial level set image?
> >>
> >> - PY
> >>
> >> _______________________________________________
> >> Insight-users mailing list
> >> Insight-users at itk.org
> >> http://www.itk.org/mailman/listinfo/insight-users
> >>
> >
> >
>
>
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--
Karthik Krishnan
R&D Engineer,
Kitware Inc.
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