[Insight-users] ConfidenceImageFilter

[Samuel Rodríguez Bescos]

Thanks for the information.

Sam
----- Original Message -----
From: "Luis Ibanez" <luis.ibanez@kitware.com>
To: "Samuel Rodríguez Bescos" <srodrigu@gbt.tfo.upm.es>
Cc: <insight-users@public.kitware.com>; "Miller, James V (Research)"
<millerjv@crd.ge.com>
Sent: Thursday, November 14, 2002 2:57 PM
Subject: Re: [Insight-users] ConfidenceImageFilter


> Hi Samuel,
>
> The ConfidenceImageFilter is surprisingly efficient
> for segmentation despite its apparent simplicity.
>
> It has been quite succesful in MRI and CT images
> both for segmenting normal anatomy and for segmenting
> globular tumors.
>
> You may get better results by first smoothing the
> images with any of the anisotropic diffusion filters
> that provide edge-preserving smoothing.
>
> for example:
>
> - CurvatureFlow
> - CurvatureAnisotropicDiffusion
> - GradientAnisotropicDiffusion
>
>
http://www.itk.org/Insight/Doxygen/html/classitk_1_1CurvatureFlowImageFilter
.html
>
http://www.itk.org/Insight/Doxygen/html/classitk_1_1CurvatureAnisotropicDiff
usionImageFilter.html
>
http://www.itk.org/Insight/Doxygen/html/classitk_1_1GradientAnisotropicDiffu
sionImageFilter.html
>
> pre-smoothed images simplify the task of the
> ConfidenceConnectedImage filter since they provide
> regions with more specific statistical distributions.
>
> A demo application using these filters is availabe
> under the Insight/Applications directory:
>
>        RegionGrowingSegmentation
>
> ----
>
> The Mean and Variance around the seed are computed in the
> first iteration of the filter by using the following helper
> classes:
>
> itk::MeanImageFunction
> itk::VarianceImageFunction
>
> http://www.itk.org/Insight/Doxygen/html/classitk_1_1MeanImageFunction.html
>
http://www.itk.org/Insight/Doxygen/html/classitk_1_1VarianceImageFunction.ht
ml
>
> These two functions use the SmartNeighborhood iterator and
> define the size of the neigborhood to a radius of 1. That
> is a 3x3 classical neighborhood in 2D and a 3x3x3 in 3D.
>
> So basically, the data over which the initial mean and variance
> are computed in 3D are the 26 closest pixels around the seed and
> the seed itself.
>
> Now that you raise this question, it may be interesting to increase
> the size of the initial neigborhood in order to improve the chances
> of getting a more representative estimation of the region statistics.
>
> This will reduce the sensitivity of the algorithm to the selection
> of the seed point.
>
> Probably we may want to add a method:
>
>           void SetInitialNeigborhoodRadius(int)
>
> to the itk::ConfidenceConnectedImageFilter.
>
>
> ----
>
>
> Please let us know if you have further questions.
>
> Thanks
>
>     Luis
>
>
> =======================================
> Samuel Rodríguez Bescos wrote:
> > Hello everyBody,
> >
> >
> >
> > Could annybody tell me where I can find information about the algorithms
> > using in the implementation of the filter ConfidenceImageFilter and for
> > wich type of images have best results?.
> >
> >
> >
> > I read the information at the website but I have a question. How are the
> > pixels at the neighborhood seed evualuated in the first iteration?. What
> > are the mean an variance values?.
> >
> >
> >
> > Thanks in advance,
> >
> >
> >
> > Sam
> >
>
>
>
>