[Insight-users] Question about HybridSegmentationFuzzyVoronoi

[Celina Imielinska]

  this is just a short paper from the 1998 Visible Human conference. I will 
e-mail Jane full jornal paper (possibly) later today,

  -Celina


On Thu, 26 Aug 2004, Frederic Perez wrote:

>
> 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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