[Insight-users] Segmenting Visible Human Data : RGB ConfidenceConnected
: VectorThresholdSegmentationLevelSetImageFilter added.
Stefan Lindenau
stefan . lindenau at gmx . de
Mon, 22 Dec 2003 12:40:28 -0500
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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