[Insight-users] Segmenting Visible Human Data : RGB ConfidenceConnected
: VectorThresholdSegmentationLevelSetImageFilter added.
Stefan Lindenau
stefan . lindenau at gmx . de
Tue, 23 Dec 2003 11:14:44 -0500
Hi Celina,
thank you for your proposal. I haven't tried it yet. Is the segmentation
method you mean, the one that is mentioned in the ITKSoftware Guide?
I think I'll try it after the holidays.
thanks
Stefan
Celina Imielinska wrote:
> Stefan,
>
> have you tried our RGB filters for Simple fuzzy connectedness and
>Voronoi diagram segmentation (classification) to segment color Visible
>Huaman data.... we have good results...
>
> -Celina
>
>
>On Mon, 22 Dec 2003, Stefan Lindenau wrote:
>
>
>
>>Hi Luis,
>>
>>the Doxygen documentation is not containing the Class summary. Maybe
>>this is due to a wrong class tag.
>>
>>Thank you for your comprehensive answer for the Mahalanobis distance
>>
>>Stefan
>>
>>Luis Ibanez wrote:
>>
>>
>>
>>>Hi Stefan,
>>>
>>>Thanks a lot for contributing these classes to ITK.
>>>
>>>They have been commited in the repository:
>>>
>>>http://www . itk . org/Insight/Doxygen/html/classitk_1_1VectorThresholdSegmentationLevelSetFunction . html
>>>
>>>http://www . itk . org/Insight/Doxygen/html/classitk_1_1VectorThresholdSegmentationLevelSetImageFilter . html
>>>
>>>
>>>You will find the new files under
>>>
>>> Insight/Code/Algorithms
>>>
>>>A test has been added under
>>>
>>> Insight/Testing/Code/Algorithms
>>> itkVectorThresholdSegmentationLevelSetImageFilterTest.cxx
>>>
>>>Please let us know if you find any problems
>>>with these classes.
>>>
>>>
>>>---
>>>
>>>
>>>About your question regarding the Mahalanobis distance:
>>>
>>>In the VectorConfidenceConnected class we are also using
>>>the square root of the distance returned by the class
>>>tk::Statistics::MahalanobisDistanceMembershipFunction
>>>http://www . itk . org/Insight/Doxygen/html/classitk_1_1Statistics_1_1MahalanobisDistanceMembershipFunction . html
>>>
>>>
>>>since in this class the returned value of distance
>>>is computed as a quadratic expression. By taking the
>>>square root it is easier to assign threshold values
>>>by reasoning on the linear scale of the pixel values.
>>>(or its vector components).
>>>
>>>
>>>
>>>Thanks a lot for your contribution to ITK !
>>>
>>>
>>> Luis
>>>
>>>
>>>
>>>-------------------------
>>>Stefan Lindenau wrote:
>>>
>>>
>>>
>>>>Hi Luis,
>>>>
>>>>I have generated the Vector ThresholdSegmentationLevelSetImageFilter.
>>>>The files are attached. You can add them to the toolkit.
>>>>
>>>>I wanted to write a test too, but I did not figure out how I could do
>>>>this. Maybe I could do this by modifying the tests for the
>>>>ThresholdSegmentationLevelSetImageFilter, but I don't know how. If
>>>>you have a good starting point I will try to write this, too.
>>>>
>>>>And there is another question regarding the MahalanobisDistance. In
>>>>the filter I used the square root of the Evaluate method of the
>>>>MahalanobisDistanceMembershipFunction. Is this reasonable?
>>>>Are there any resources available on the Internet, where example
>>>>values are given so that it is possible to make estimations. I think
>>>>I got some good values, but I am interested how it is working. I did
>>>>not find much useful by using google.
>>>>
>>>>Thank you
>>>>stefan
>>>>
>>>>Luis Ibanez wrote:
>>>>
>>>>
>>>>
>>>>>Hi Stefan,
>>>>>
>>>>>You are right,
>>>>>Region growing filters that only based on intensity values
>>>>>are prone to producing leaks.
>>>>>
>>>>>You may reduce this tendency by first applying a smoothing
>>>>>filter like the VectorGradientAnisotropic smoothing. This
>>>>>may help, but still it is not possible to guarranty that it
>>>>>will prevent the leaks.
>>>>>
>>>>>In practice a common approach is to use the RegionGrowing
>>>>>methods for producing a very sketchy representation of the
>>>>>object, then solidify this representation using mathematical
>>>>>morphology filters (like dilation-erosion sequences). Then
>>>>>use this as an initialization for level set filters that
>>>>>have better capabilities for dealing with leaks.
>>>>>
>>>>>The ThresholdLevelSet filter is certainly one of the best
>>>>>first options to try out. Unfortunately this filter is
>>>>>not yet extended to RGB data. In make sense, as you
>>>>>suggested, to use the Mahalanobis distance in this case
>>>>>for controling the Threholding values.
>>>>>
>>>>>The good news for you is that is should be relatively easy
>>>>>to get this RGB level set filter done. You simply need to
>>>>>create a class
>>>>>
>>>>>
>>>>> itkVectorThresholdSegmentationLevelSetFunction
>>>>>
>>>>>based on the code of the current
>>>>>
>>>>> itkThresholdSegmentationLevelSetFunction
>>>>>
>>>>>
>>>>>whose only goal is to compute the Speed image used by the
>>>>>level set.
>>>>>
>>>>>Once you have this new class, you can easily create the
>>>>>
>>>>> VectorThresholdSegmentationLevelSetImageFilter
>>>>>
>>>>>by copy/pasting code from the current class:
>>>>>
>>>>> ThresholdSegmentationLevelSetImageFilter
>>>>>
>>>>>
>>>>>
>>>>>Notice that the level set code only sees this speed image,
>>>>>not the original RGB data.
>>>>>
>>>>>The only trick here is that you will have to compute the
>>>>>mean and covariance of a "sample region" in order to
>>>>>feed the Mahalanobis distance function. You may also want
>>>>>to look at the speed function before you start using the
>>>>>level set method. A quick look at the speed functions
>>>>>will give you a feeling on the chances of segmenting the
>>>>>region using the level set method. A high quality speed
>>>>>image is a fundamental requirement for getting good results
>>>>>from level set methods.
>>>>>
>>>>>
>>>>>Please let us know if you have any problems in writing
>>>>>this class. We will also be very interested in adding it
>>>>>to the toolkit :-)
>>>>>
>>>>>
>>>>>Thanks
>>>>>
>>>>>
>>>>>
>>>>> Luis
>>>>>
>>>>>
>>>>>
>>>>>-------------------------------
>>>>>Stefan Lindenau wrote:
>>>>>
>>>>>
>>>>>
>>>>>>Hi Luis,
>>>>>>
>>>>>>ok I have read the parts of the software guide that you mentioned
>>>>>>again.
>>>>>>
>>>>>>Now I want to realize the segmentation of the Visible Human Data by
>>>>>>using the VectorConfidenceConnectedImageFilter to get the mean
>>>>>>vector and the covariant matrix of my tissue. I cannot use the
>>>>>>segmentation of this filter directly because it is leaking.
>>>>>>With this data I want to initialize a Levelset filter that is
>>>>>>almost similar to the ThresholdLevelset filter, but it should use
>>>>>>the Mahalanobis distance for generating the speed image.
>>>>>>
>>>>>>I think that I have to write this LevelsetFilter by myself or is
>>>>>>there a implementation for such a problem available?
>>>>>>
>>>>>>
>>>>>>Thanks
>>>>>>Stefan
>>>>>>
>>>>>>Luis Ibanez wrote:
>>>>>>
>>>>>>
>>>>>>
>>>>>>>Hi Stefan,
>>>>>>>
>>>>>>>When you use ConfidenceConnected you only need to provide the
>>>>>>>multiplier
>>>>>>>for the variance. The range of intensities is computed by the filter
>>>>>>>based on the mean and the variance of intensities around the seed
>>>>>>>points.
>>>>>>>
>>>>>>>The range is simply:
>>>>>>>
>>>>>>> lower limit = mean - standardDeviation * multiplier
>>>>>>> upper limit = mean + standardDeviation * multiplier
>>>>>>>
>>>>>>>The mean and standardDeviation are computed by the filter.
>>>>>>>You only need to tune the value of the multiplier, and
>>>>>>>experiement with the number of iterations.
>>>>>>>
>>>>>>>This holds for RGB confidence connected, where instead of a scalar
>>>>>>>mean
>>>>>>>you have a mean vector of three components (RGB components), and
>>>>>>>instead
>>>>>>>of standardDeviation you have a covariance matrix, intead of lower
>>>>>>>and
>>>>>>>upper limits the filter computes the Mahalanobis distance in RGB
>>>>>>>space.
>>>>>>>Therefore you only need to provide the value for the multiplier.
>>>>>>>
>>>>>>>You may want to read again the description of this method in the
>>>>>>>SoftwareGuide.
>>>>>>>
>>>>>>> http://www . itk . org/ItkSoftwareGuide . pdf
>>>>>>>
>>>>>>>It is in Section 9.1.3.
>>>>>>>In particular look at equation 9.2 in pdf-page 348.
>>>>>>>
>>>>>>>We used the RGB Confidence connected filter for producing most of the
>>>>>>>segmentations shown in the cover of the SoftwareGuide printed
>>>>>>>version.
>>>>>>>
>>>>>>>The code we used for creating the cover is available in
>>>>>>>
>>>>>>> InsightDocuments/SoftwareGuide/Cover/Source
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>Regards,
>>>>>>>
>>>>>>>
>>>>>>> Luis
>>>>>>>
>>>>>>>
>>>>>>>------------------------
>>>>>>>Stefan Lindenau wrote:
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>>Hi Luis,
>>>>>>>>
>>>>>>>>I tried to get the example of Josh working but I failed on VC6
>>>>>>>>and Cygwin to compile it. At the moment I want to give your
>>>>>>>>suggestion with the ConfidenceConnected and the
>>>>>>>>ThresholdConnected filter a try.
>>>>>>>>I read the Software Guide and I think that I am now knowing how
>>>>>>>>these filters are working. The only thing that I do not
>>>>>>>>understand is how I can get the intensity range values from the
>>>>>>>>ConfidenceConnected filter. I can get/set the multiplier, but I
>>>>>>>>see no access method to these values.
>>>>>>>>
>>>>>>>>Maybe I could get them by comparing the input image of the
>>>>>>>>ConfidenceConnectedFilter and the output Image, but this seems a
>>>>>>>>bit to complicated to me. Is there a more elegant solution? Did I
>>>>>>>>miss a method?
>>>>>>>>
>>>>>>>>Thank you
>>>>>>>>Stefan
>>>>>>>>
>>>>>>>>P.S.: as I have progressed with my work I have seen that the data
>>>>>>>>I need can be reduced to 500MB (unsigned char RGB).
>>>>>>>>
>>>>>>>>Luis Ibanez wrote:
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>>Hi Stefan,
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>The reason for postprocessing the joint regions is that
>>>>>>>>>if you take two contiguous pieces of the image and run
>>>>>>>>>level sets on each one, the level sets will evolve in
>>>>>>>>>a different way at each side of the boundary, and it
>>>>>>>>>is likely that if you try to put the two level sets
>>>>>>>>>together just by joining the two blocks of data, the
>>>>>>>>>zero set surface will not be contiguous from one block
>>>>>>>>>to the next.
>>>>>>>>>
>>>>>>>>>I would anticipate that some smoothing will be needed
>>>>>>>>>for ironing out any discontinuity in the connection.
>>>>>>>>>taking the joint region (a region around the boundary
>>>>>>>>>of the two block and running some more iterations of
>>>>>>>>>the level set there may help to smooth out the transition
>>>>>>>>>between the blocks.
>>>>>>>>>
>>>>>>>>>You could certainly attempt this post-processing-smoothing
>>>>>>>>>with other methods. For example, a simple median filter
>>>>>>>>>has proved to be powerful enough for smoothing out
>>>>>>>>>transitions and it will be a much faster approach too.
>>>>>>>>>
>>>>>>>>>You may want to start by trying Josh's suggestion since
>>>>>>>>>he and his group are the ones who experimented more
>>>>>>>>>deeply into this issue.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>Please let of know of your findings,
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Thanks
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Luis
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>-----------------------
>>>>>>>>>Stefan Lindenau wrote:
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>>Hi Luis,
>>>>>>>>>>
>>>>>>>>>>thank you for your quick and comprehensive answer. I will just
>>>>>>>>>>have to cut the image in pieces.
>>>>>>>>>>
>>>>>>>>>>Only one thing I still do not understand:
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>>If you use level sets, you could post process
>>>>>>>>>>>the joint regions between your contiguous pieces
>>>>>>>>>>>in order to smooth out the potential differences
>>>>>>>>>>>between the level set obtained in one region and
>>>>>>>>>>>the level set obtained in the facing region.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>Why is it dependend on the level sets to postprocess the the
>>>>>>>>>>joint region. In my comprehension I will just cut the data in
>>>>>>>>>>big pieces,process it and put it together just after the
>>>>>>>>>>processing. Then such a postprocessing should be possible with
>>>>>>>>>>any of the methods. Or did I ignore some facts?
>>>>>>>>>>
>>>>>>>>>>Maybe I can get it working with the streaming example for
>>>>>>>>>>watershed algorithms as Joshua proposed. I will just have to
>>>>>>>>>>test it out.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>thanks
>>>>>>>>>>Stefan
>>>>>>>>>>
>>>>>>>>>>_______________________________________________
>>>>>>>>>>
>>>>>>>>>>
>>>
>>>
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>>>
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