[Insight-users] Segmenting Visible Human Data : RGB ConfidenceConnected.

[Luis Ibanez]

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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