[Insight-users] Question about HybridSegmentationFuzzyVoronoi

[Celina Imielinska]

Jane,

  I will e-mail you electronic version of the paper.

  To see Voronoi Diagram mesh, we use our in-house software that is not 
part of the itk. We wanted to illustrate the steps of the method. ITK 
voronoi classification returns a 3D binary mask with segmented slices with 
the volume.

  -Celina



On Thu, 26 Aug 2004, Jane Meinel wrote:

> 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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