[Insight-users] Object Labeling - Attribute representing neighborhood

[Christian Werner]

Thanks for the help! The LabelContourImageFilter turned out to be quite 
useful in general, good to know about it. As for the neighborhood I will 
try your approach, Richard. If I run into any problems I may come back 
to you later.

Best regards,
Christian



Richard Beare wrote:
> Hi,
> I've done something like this in the past to implement translocation
> assay measurement. The idea there is that you start with cell nuclei
> and want to measure expression levels in a donut around them.
>
> As you suggest, simply dilating the objects is a simple way to start,
> but there are issues. One is maintaining the labels, and the other is
> dealing with the situation in which the objects are closer to each
> other than the border thickness you are interested in.
>
> Here's the approach I used:
>
> 1) Binarize your label map
> 2) Compute the distance transform.
> 3) Use the original label map as markers and apply a watershed
> transform using the distance transform as the control image. The
> result of this is a voronoi tesselation (roughly, depending on how
> good your distance transform is) and the labels are preserved by the
> watershed. This also deals with the case of objects being close
> together.
> 4) Finally create a mask by thresholding your distance transform at
> the border thickness you are after. Apply this mask to the watershed
> output. Remove the original seeds if you want only the borders.
>
> I think this should do what you are after.
>
> On Fri, Apr 9, 2010 at 4:55 AM, Luis Ibanez <luis.ibanez at kitware.com> wrote:
>   
>> Hi Christian,
>>
>> Thanks for the detailed description of what you are trying to  do.
>>
>>
>> I'm wondering if the following class is what you are looking for:
>>
>>                    itkLabelContourImageFilter.txx
>>
>> http://public.kitware.com/Insight/Doxygen/html/classitk_1_1LabelContourImageFilter.html
>> "Labels the pixels on the border of the objects in a labeled image. "
>>
>>
>>    Regards,
>>
>>
>>            Luis
>>
>>
>> ----------------------------------------------------------------------------------------
>> On Tue, Apr 6, 2010 at 6:52 AM, Christian Werner
>> <christian.werner at rwth-aachen.de> wrote:
>>     
>>> Hello!
>>>
>>> I am currently working with Gaetens Object Labeling/Measuring. It really
>>> does a good job and I am glad that it seemlessly integrates into my
>>> software. Now that I have analyzed how I could improve my object evalution,
>>> I was looking for a way to qualify the neighborhood of an object. It seems
>>> that this is a very characteristic trait of every ROI. Looking at all
>>> pixels/voxels contained in the bounding box and e.g. take their average
>>> would already give a rough estimate. Objects that e.g. differ in the
>>> variance in the values of their neighboring elements could be easily
>>> distinguished, even if all other attributes would suggest that they are of
>>> the same type (roundness, size, ...). At least this is what I could need for
>>> my analyzing purposes.
>>>
>>> Despite of the "simple" solution of the boundnig box I thought of several
>>> ways to realize this. One could for example dilate all objects to a certain
>>> degree, then subtract the original object to obtain a thick layer
>>> surrounding the object. This layer would be an object itself and you could
>>> just go ahead and qualify this layer as any other object. But this would
>>> mean that you have to somehow make a correspodance between all layers and
>>> all objects that they belong to. Assuming that the labeling itself is
>>> already consistent (layers will get same IDs as the object), everything
>>> would just be fine, but I doubt that you can trust that this is always the
>>> case.
>>>
>>> Maybe there is already another way to qualify the neighborhood? I didn't get
>>> to find such funcionality when looking through the paper.
>>>
>>> Best regards,
>>> Christian
>>>
>>>
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