[Insight-users] Question about HybridSegmentationFuzzyVoronoi

[Frederic Perez]

Hello Jane,

if I'm not wrong, you can find an HTML version of the paper here:
http://www.nlm.nih.gov/research/visible/vhp_conf/imiels/nlmseg.htm

Frederic Perez

 --- Jane Meinel <myitk at yahoo.com> escribió: 
> Hi Celina,
> I'm appreciated for your detailed answer. In order to understand this
> method better, I should read your old paper:
> Imielinska, C.; Downes, M; and Yuan, W., "Semi-Automated Color
> Segmentation of Anatomical Tissue", Journal of Computerized Medical
> Imaging and Graphics, 24(2000), 173-180, April, 2000
> However, I can not get this paper. Could you please do me a favor and
> send a copy of this paper to me?
> Is it possible to draw the triangle mesh of the middle result of the
> iteration of Voronoid diagram like the figures in your paper? Which
> class of ITK should I use?
>  
>  
> Thank you very much!
>  
>  
> Best regards,
>  
> Jane
> 
> Celina Imielinska <ci42 at columbia.edu> wrote:
> 
> Jane,
> 
> the most detailed description of the Voronoi diagram classifier
> (without the fuzzy connectedness part) you can find in my old paper:
> 
> Imielinska, C.; Downes, M; and Yuan, W., "Semi-Automated Color
> Segmentation of Anatomical Tissue", Journal of Computerized Medical
> Imaging and Graphics, 24(2000), 173-180, April, 2000.
> 
> in the hybrid method that is a combination of (simple) fuzzy 
> connectedness and voronoi classification, we use the simplest version
> of 
> otherwise "stand-alone" fuzzy connectedness segmentation (look at
> other 
> fuzzy connectedness filters provided by the itk), to derive
> statistics for 
> a homogeneity operator for the tissue that we are segmenting. We do
> need 
> a well defined homogeneity operator (in theory, it can be provided by
> "any" method that can do it "well") to "drive" the subdivisions in
> the
> iterative voronoi classification part of the hybrid method. In the
> voronoi 
> classification, random points are thrown at the image, and each
> voronoi 
> region, in the voronoi diagram, is classified as 
> interior/exterior/boundary depending how "close" it is to the 
> characteristics of the homogeneity operator. We iterate the method
> and 
> keep subdividing the boundary voronoi regions only, until the method 
> converges to the boundary of the object/organ (in the process, we
> keep 
> "pushing" the interior inside-out, and the exterior outside-in, and 
> squizze the boundary in-between, until stopping ctriteria "kick-in).
> 
> The estimated mean and standard deviation and other parameters that
> are 
> automatically computed from a sample 3D region segmented by the 
> (simple) fuzzy, can be stored and applied to a new image (same
> tissue, 
> same image modality etc.). This method hinges on the "quality" of the
> 
> homegeneity operator. We can store the homogeneity operators as a
> database 
> for same tissue/organ, same image modality, etc.
> 
> if you need more details, please let us know (Yinpeng Jin
> yj76 at columbia 
> can answer all questions, too),
> 
> -Celina
> 
> 
> 
> On Thu, 26 Aug 2004, Jane Meinel wrote:
> 
> > Dear itk-users,
> > I tried the example of HybridSegmentationFuzzyVoronoi. It is quite
> good
> > image segmentation frame.
> > Now I have some questions about this example:
> >
> > *1. In the example image case BrainT1Slice.png, the parameters are:
> 140 125
> > 140 25 0.2 2.0. Among them, (140, 125) is the seed position. It is 
> > obviously. However, "140 and 25 are the estimated mean and standard
> 
> > deviation, respectively, of the object to be segmented. Finally,
> 0.2 
> > and 2.0 are the tolerance for the mean and standard deviation, 
> > respectively." What do those parameters mean? If I want to segment 
> > another image, how should I set those parameters by myself?
> >
> > *2. In the BrainT1Slice.png case, the voronoi diagram
> classification
> > improves the segmentation a lot after the fuzzy connectedness 
> > segmentation step. I want to know details about the voronoi diagram
> 
> > classification. I have read the paper "Hybrid Segmentation of
> Anotomical 
> > Data", which is written by Celina Imielinska, Dimitris Metaxas,
> Jayaram 
> > Udupa, Yinpeng Jin, Ting Chen, and published in MICCAI 2001. But it
> 
> > doesn't describe very clear about voronoi diagram classification.
> Which 
> > paper should I read in order to understand this algorithm better?
> >
> > *3. I'm impressed deeply by the figures of the paper mentioned
> about,
> > which show the result of the iterations of VD-based algorithm. How
> can I 
> > draw such pictures by ITK classes? I want to know the procedure in 
> > different iterate step of Voronoi Diagram algorithm.
> >
> >
> > Any help is much appreciated! Thanks a lot!
> >
> >
> > Jane
> >
> >
> > ---------------------------------
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