<DIV>Hi Frederic,</DIV>
<DIV>I get this HTML paper. Thank you very much!</DIV>
<DIV> </DIV>
<DIV>Regards,</DIV>
<DIV>Jane<BR><BR><B><I>Frederic Perez <fredericpcx@yahoo.es></I></B> wrote:</DIV>
<BLOCKQUOTE class=replbq style="PADDING-LEFT: 5px; MARGIN-LEFT: 5px; BORDER-LEFT: #1010ff 2px solid"><BR>Hello Jane,<BR><BR>if I'm not wrong, you can find an HTML version of the paper here:<BR>http://www.nlm.nih.gov/research/visible/vhp_conf/imiels/nlmseg.htm<BR><BR>Frederic Perez<BR><BR>--- Jane Meinel <MYITK@YAHOO.COM>escribió: <BR>> Hi Celina,<BR>> I'm appreciated for your detailed answer. In order to understand this<BR>> method better, I should read your old paper:<BR>> Imielinska, C.; Downes, M; and Yuan, W., "Semi-Automated Color<BR>> Segmentation of Anatomical Tissue", Journal of Computerized Medical<BR>> Imaging and Graphics, 24(2000), 173-180, April, 2000<BR>> However, I can not get this paper. Could you please do me a favor and<BR>> send a copy of this paper to me?<BR>> Is it possible to draw the triangle mesh of the middle result of the<BR>> iteration of Voronoid diagram like the figures in your paper? Which<BR>> class of ITK should I
use?<BR>> <BR>> <BR>> Thank you very much!<BR>> <BR>> <BR>> Best regards,<BR>> <BR>> Jane<BR>> <BR>> Celina Imielinska <CI42@COLUMBIA.EDU>wrote:<BR>> <BR>> Jane,<BR>> <BR>> the most detailed description of the Voronoi diagram classifier<BR>> (without the fuzzy connectedness part) you can find in my old paper:<BR>> <BR>> Imielinska, C.; Downes, M; and Yuan, W., "Semi-Automated Color<BR>> Segmentation of Anatomical Tissue", Journal of Computerized Medical<BR>> Imaging and Graphics, 24(2000), 173-180, April, 2000.<BR>> <BR>> in the hybrid method that is a combination of (simple) fuzzy <BR>> connectedness and voronoi classification, we use the simplest version<BR>> of <BR>> otherwise "stand-alone" fuzzy connectedness segmentation (look at<BR>> other <BR>> fuzzy connectedness filters provided by the itk), to derive<BR>> statistics for <BR>> a homogeneity operator for the tissue that we are segmenting. We
do<BR>> need <BR>> a well defined homogeneity operator (in theory, it can be provided by<BR>> "any" method that can do it "well") to "drive" the subdivisions in<BR>> the<BR>> iterative voronoi classification part of the hybrid method. In the<BR>> voronoi <BR>> classification, random points are thrown at the image, and each<BR>> voronoi <BR>> region, in the voronoi diagram, is classified as <BR>> interior/exterior/boundary depending how "close" it is to the <BR>> characteristics of the homogeneity operator. We iterate the method<BR>> and <BR>> keep subdividing the boundary voronoi regions only, until the method <BR>> converges to the boundary of the object/organ (in the process, we<BR>> keep <BR>> "pushing" the interior inside-out, and the exterior outside-in, and <BR>> squizze the boundary in-between, until stopping ctriteria "kick-in).<BR>> <BR>> The estimated mean and standard deviation and other parameters that<BR>> are
<BR>> automatically computed from a sample 3D region segmented by the <BR>> (simple) fuzzy, can be stored and applied to a new image (same<BR>> tissue, <BR>> same image modality etc.). This method hinges on the "quality" of the<BR>> <BR>> homegeneity operator. We can store the homogeneity operators as a<BR>> database <BR>> for same tissue/organ, same image modality, etc.<BR>> <BR>> if you need more details, please let us know (Yinpeng Jin<BR>> yj76@columbia <BR>> can answer all questions, too),<BR>> <BR>> -Celina<BR>> <BR>> <BR>> <BR>> On Thu, 26 Aug 2004, Jane Meinel wrote:<BR>> <BR>> > Dear itk-users,<BR>> > I tried the example of HybridSegmentationFuzzyVoronoi. It is quite<BR>> good<BR>> > image segmentation frame.<BR>> > Now I have some questions about this example:<BR>> ><BR>> > *1. In the example image case BrainT1Slice.png, the parameters are:<BR>> 140 125<BR>> > 140 25
0.2 2.0. Among them, (140, 125) is the seed position. It is <BR>> > obviously. However, "140 and 25 are the estimated mean and standard<BR>> <BR>> > deviation, respectively, of the object to be segmented. Finally,<BR>> 0.2 <BR>> > and 2.0 are the tolerance for the mean and standard deviation, <BR>> > respectively." What do those parameters mean? If I want to segment <BR>> > another image, how should I set those parameters by myself?<BR>> ><BR>> > *2. In the BrainT1Slice.png case, the voronoi diagram<BR>> classification<BR>> > improves the segmentation a lot after the fuzzy connectedness <BR>> > segmentation step. I want to know details about the voronoi diagram<BR>> <BR>> > classification. I have read the paper "Hybrid Segmentation of<BR>> Anotomical <BR>> > Data", which is written by Celina Imielinska, Dimitris Metaxas,<BR>> Jayaram <BR>> > Udupa, Yinpeng Jin, Ting Chen, and published in
MICCAI 2001. But it<BR>> <BR>> > doesn't describe very clear about voronoi diagram classification.<BR>> Which <BR>> > paper should I read in order to understand this algorithm better?<BR>> ><BR>> > *3. I'm impressed deeply by the figures of the paper mentioned<BR>> about,<BR>> > which show the result of the iterations of VD-based algorithm. How<BR>> can I <BR>> > draw such pictures by ITK classes? I want to know the procedure in <BR>> > different iterate step of Voronoi Diagram algorithm.<BR>> ><BR>> ><BR>> > Any help is much appreciated! Thanks a lot!<BR>> ><BR>> ><BR>> > Jane<BR>> ><BR>> ><BR>> > ---------------------------------<BR>> > Do you Yahoo!?<BR>> > Yahoo! Mail - 50x more storage than other providers!<BR>> <BR>> <BR>> ---------------------------------<BR>> Do you Yahoo!?<BR>> New and Improved Yahoo! Mail - Send 10MB
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