[Insight-users] using itkVoronoiSegmentationImageFilter ?
Jay Udupa
jay@mipg.upenn.edu
Mon, 21 Oct 2002 17:29:56 -0400
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Hmmm... Without spending too much time on this issue, I wanted to make
one thing clear. SimpleFuzzyConnectedness implemented by Columbia is
what the name says. It does not have the full bells and whistles of the
fuzzy connectedness (FC) method that has been published by us at Penn.
There are over 40 journal publications by us alone on the FC method and
its over 10 clinical applications wherein 1000s of images have been
routinely processed and evaluation has been carried out carefully in
each application.
Jay Udupa
____
Yinpeng Jin wrote:
> VoronoiSegmenationImageFilter is a region-based classification,
> split-and-merge like algorithm.you are perfectly right, it picks up
> all the similar color regions in whole image. It works well if you
> have multiple objects in the image to segment, it was used because
> SimpleFuzzyConnectedness can only pick up one connected
> component.there is another version of FuzzyConnectedness, which is
> able to claim multiple objects (VectoriorFuzzyConnectedness)And to use
> Deformable Models is definitedly a good idea, actually, all those
> three methods had been tested for combining together to build a hybrid
> segmentation framework.I bet Celina, Jay and Dimitris can explain the
> idea better, for your reference, please look at following paper in
> MICCAI 2001:C. Imielinska, D. Metaxas, J. Udupa, Y.Jin and T. Chen,
> "Hybrid Segmentation Methods of Anatomical Data." Proceedings of The
> Fourth International Conference on Medical Image Computing and
> Computer Assisted Interventions (MICCAI 2001), pp. 1058-1066, October
> 2001, Utrecht Netherlands.
>
> ----- Original Message -----
> From: Seungbum Koo
> To: Yinpeng Jin
> Cc: insight-users@public.kitware.com
> Sent: Monday, October 21, 2002 4:51 PM
> Subject: Re: Re: [Insight-users] using
> itkVoronoiSegmentationImageFilter ?
> Hi,
> Thanks for the help. It worked and segmented but not as I
> expected. I don't understand well about
> VoronoiSegmentationImageFilter but it seems to segment all
> similar color regions in whole image as the seed region. I
> just wanted to find more exact boundary of that found using
> FuzzyConnectednessScalarFilter.
> Anyway I think the VoronoiSegmentationImageFilter worked
> fine. What do you think about using DeformableMeshFilter
> instead of VoronoiSegmentationImageFilter?
>
> regards
> Seungbum Koo
>
> > Title : Re: [Insight-users] using
> itkVoronoiSegmentationImageFilter ?
> > Date : Sun, 20 Oct 2002 13:34:32 -0400
> > From : "Yinpeng Jin"
> > To : Seungbum Koo,
> >
> > if you use takeaprior, then you don't want to setMean and
> setVar, those two parameters will be calculated from the
> binary mask.
> > and
> > try to use
> > m_voronoiFilter->SetMeanPercentError(PERCENT);
> > m_voronoiFilter->SetVarPercentError(VARPERCENT);
> > in stead of
> > m_voronoiFilter->SetMeanTolerance(10);
> > m_voronoiFilter->SetVarTolerance(20);
> >
> > they are trying to manipulate the same parameter, but
> usually are more intuitive to figure.
> > the MeanPercentError could usually be set between 0.1 to
> 0.3
> > and the VarPercentError could be between 1 to 3. they
> don't depends on your pixel intensity range, while the
> MeanTolerance and VarTolerance usually do.
> > Also, you can first output your m_binaryImage to see if it
> is something reasonable. the VoronoisegmentationImagefilter
> will need something at least represents
> > parts of your target object as the a prior.
> > Try the above, and let me know what happens.
> > Yinpeng.
> >
> >
> >
> > ----- Original Message -----
> > From: Seungbum Koo
> > To: insight-users@public.kitware.com
> > Sent: Sunday, October 20, 2002 12:45 AM
> > Subject: [Insight-users] using
> itkVoronoiSegmentationImageFilter ?
> >
> >
> > Hi,
> >
> > I'm trying to use itkVoronoiSegmentationImageFilter
> combined with itkSimpleFuzzyConnectednessScalarImageFilter.
> >
> > I made a binary image from
> itkSimpleFuzzyConnectednessScalarImageFilter but I couldn't
> figure out how to set itkVoronoiSegmentationImageFilter
> variables. Here is my source code.
> >
> >
> =============================================================
>
> > m_voronoiFilter->SetInput(m_rawImageSource->GetOutput());
> > m_voronoiFilter->TakeAPrior(m_binaryImage);
> > m_voronoiFilter->SetMean(520);
> > m_voronoiFilter->SetVar(20);
> > m_voronoiFilter->SetMeanTolerance(10);
> > m_voronoiFilter->SetVarTolerance(20);
> > // m_voronoiFilter->SetNumberOfSeeds(400); // ??
> > m_voronoiFilter->SetSteps(5);
> > m_voronoiFilter->Update();
> >
> =============================================================
>
> >
> > m_binaryImage is calculated from m_rawImageSource and as I
> expected.
> > But this code just makes a black image... all zeros.
> >
>
[Image] [Image]
[Image]Seungbum Koo
[Image] [Image]
>
>
>
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<br>Hmmm... Without spending too much time on this issue, I wanted to make
one thing clear. SimpleFuzzyConnectedness implemented by Columbia is what
the name says. It does not have the full bells and whistles of the fuzzy
connectedness (FC) method that has been published by us at Penn. There
are over 40 journal publications by us alone on the FC method and its over
10 clinical applications wherein 1000s of images have been routinely processed
and evaluation has been carried out carefully in each application.
<p>Jay Udupa
<br>____
<p>Yinpeng Jin wrote:
<blockquote TYPE=CITE><style></style>
<font face="Arial"><font size=-1>VoronoiSegmenationImageFilter
is a region-based classification, split-and-merge like algorithm.</font></font><font face="Arial"><font size=-1>you
are perfectly right, it picks up all the similar color regions in whole
image. It works well if you have multiple objects in the image to segment,
it was used because SimpleFuzzyConnectedness can only pick up one connected
component.</font></font><font face="Arial"><font size=-1>there is another
version of FuzzyConnectedness, which is able to claim multiple objects
(VectoriorFuzzyConnectedness)</font></font><font face="Arial"><font size=-1>And
to use Deformable Models is definitedly a good idea, actually, all
those three methods had been tested for combining together to build a hybrid
segmentation framework.</font></font><font face="Arial"><font size=-1>I
bet Celina, Jay and Dimitris can explain the idea better, for your reference,
please look at following paper in MICCAI 2001:</font></font><font face="Arial"><font size=-1>C.
Imielinska, D. Metaxas, J. Udupa, Y.Jin and T. Chen, "Hybrid Segmentation
Methods of Anatomical Data." <i>Proceedings of The Fourth International
Conference on Medical Image Computing and Computer Assisted Interventions
(MICCAI 2001)</i>, pp. 1058-1066, October 2001, Utrecht Netherlands.</font></font>
<blockquote
style="BORDER-LEFT: #000000 2px solid; MARGIN-LEFT: 5px; MARGIN-RIGHT: 0px; PADDING-LEFT: 5px; PADDING-RIGHT: 0px">
<div style="FONT: 10pt arial">----- Original Message -----</div>
<div
style="BACKGROUND: #e4e4e4; FONT: 10pt arial; font-color: black"><b>From:</b>
<a href="mailto:koosb2@hanmail.net" title="koosb2@hanmail.net">Seungbum
Koo</a></div>
<div style="FONT: 10pt arial"><b>To:</b> <a href="mailto:yj76@columbia.edu" title="yj76@columbia.edu">Yinpeng
Jin</a></div>
<div style="FONT: 10pt arial"><b>Cc:</b> <a href="mailto:insight-users@public.kitware.com" title="insight-users@public.kitware.com">insight-users@public.kitware.com</a></div>
<div style="FONT: 10pt arial"><b>Sent:</b> Monday, October 21, 2002 4:51
PM</div>
<div style="FONT: 10pt arial"><b>Subject:</b> Re: Re: [Insight-users] using
itkVoronoiSegmentationImageFilter ?</div>
Hi,
<br>Thanks for the help. It worked and segmented but not as I expected.
I don't understand well about VoronoiSegmentationImageFilter but it seems
to segment all similar color regions in whole image as the seed region.
I just wanted to find more exact boundary of that found using FuzzyConnectednessScalarFilter.
<br>Anyway I think the VoronoiSegmentationImageFilter worked fine. What
do you think about using DeformableMeshFilter instead of VoronoiSegmentationImageFilter?
<p>regards
<br>Seungbum Koo
<p>> Title : Re: [Insight-users] using itkVoronoiSegmentationImageFilter
?
<br>> Date : Sun, 20 Oct 2002 13:34:32 -0400
<br>> From : "Yinpeng Jin" <YJ76@COLUMBIA.EDU>
<br>> To : Seungbum Koo<KOOSB2@HANMAIL.NET>,<INSIGHT-USERS@PUBLIC.KITWARE.COM>
<br>>
<br>> if you use takeaprior, then you don't want to setMean and setVar,
those two parameters will be calculated from the binary mask.
<br>> and
<br>> try to use
<br>> m_voronoiFilter->SetMeanPercentError(PERCENT);
<br>> m_voronoiFilter->SetVarPercentError(VARPERCENT);
<br>> in stead of
<br>> m_voronoiFilter->SetMeanTolerance(10);
<br>> m_voronoiFilter->SetVarTolerance(20);
<br>>
<br>> they are trying to manipulate the same parameter, but usually are
more intuitive to figure.
<br>> the MeanPercentError could usually be set between 0.1 to 0.3
<br>> and the VarPercentError could be between 1 to 3. they don't depends
on your pixel intensity range, while the MeanTolerance and VarTolerance
usually do.
<br>> Also, you can first output your m_binaryImage to see if it is something
reasonable. the VoronoisegmentationImagefilter will need something at least
represents
<br>> parts of your target object as the a prior.
<br>> Try the above, and let me know what happens.
<br>> Yinpeng.
<br>>
<br>>
<br>>
<br>> ----- Original Message -----
<br>> From: Seungbum Koo
<br>> To: insight-users@public.kitware.com
<br>> Sent: Sunday, October 20, 2002 12:45 AM
<br>> Subject: [Insight-users] using itkVoronoiSegmentationImageFilter
?
<br>>
<br>>
<br>> Hi,
<br>>
<br>> I'm trying to use itkVoronoiSegmentationImageFilter combined with
itkSimpleFuzzyConnectednessScalarImageFilter.
<br>>
<br>> I made a binary image from itkSimpleFuzzyConnectednessScalarImageFilter
but I couldn't figure out how to set itkVoronoiSegmentationImageFilter
variables. Here is my source code.
<br>>
<br>> =============================================================
<br>> m_voronoiFilter->SetInput(m_rawImageSource->GetOutput());
<br>> m_voronoiFilter->TakeAPrior(m_binaryImage);
<br>> m_voronoiFilter->SetMean(520);
<br>> m_voronoiFilter->SetVar(20);
<br>> m_voronoiFilter->SetMeanTolerance(10);
<br>> m_voronoiFilter->SetVarTolerance(20);
<br>> // m_voronoiFilter->SetNumberOfSeeds(400); // ??
<br>> m_voronoiFilter->SetSteps(5);
<br>> m_voronoiFilter->Update();
<br>> =============================================================
<br>>
<br>> m_binaryImage is calculated from m_rawImageSource and as I expected.
<br>> But this code just makes a black image... all zeros.
<br>>
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