[Insight-users] Segmenting Visible Human Data : RGB ConfidenceConnected.
Luis Ibanez
luis . ibanez at kitware . com
Fri, 05 Dec 2003 11:24:46 -0500
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
>>>>>
>>>>> _______________________________________________
>>>>> Insight-users mailing list
>>>>> Insight-users at itk . org
>>>>> http://www . itk . org/mailman/listinfo/insight-users
>>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>
>>>
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>>
>>
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