[Insight-users] vessel enhancing diffusion filter (release 200)

Wed Jun 11 16:35:24 EDT 2008

Oleksandr,

I assumed you had a 3D dataset. Yes, in 3D the Hessian has 9 components 
put they
are repeated so you essentially only need to compute 6 of them. My original
dataset was of 512x512x164 and I cropped/downsampled to 175x150x82 and the
filter worked fine on a PC with 3GB of RAM. I guess that for larger datasets
Luis recommended 64 bit machines but I'm not sure.

Federico
Quoting Oleksandr Dzyubak <adzyubak at gmail.com>:

> Hi Frederico,
>
> In 2-D case the Hessian matrix has 4 components.
> If we are talking about 3-D, then it should be 9.
> In general case it has to be 2^d, where d is your dimension.
>
> I cropped and downsapmled my image just for the computation time saving sake.
> In reality, my images would be much larger.
> And as Luis mentioned in some of his emails a while ago,
> there should be way/room for memory distributed calculations.
> That is especially important for the cases like this one.
>
> BTW, what was the pixel size of your image?
>
> Thanks,
>
> Alex
>
>
> fjlb at duke.edu wrote:
>> Oleksandr,
>>
>> I believe that the 6 in the formula is due to the fact that the 
>> Hessian matrix
>> contains six different partial derivatives, hence it needs to store 
>> 6 copies of
>> your volume in memory. I have used the filter and I had to crop and 
>> downsample
>> my volumes for it to work. Try downsampling your volumes and it 
>> should not be a
>> problem.
>>
>>
>> Federico
>>
>> Quoting Oleksandr Dzyubak <adzyubak at gmail.com>:
>>
>>> Hi Luis,
>>>
>>> First of all many thanks all of you for such a good filter, "Vessel 
>>> enhancing diffusion filter".
>>> With great pleasure I read the article from the the IJ distribution 
>>> and the references therein.
>>>
>>> Since I am working on vessels, of course, I was tempted to give 
>>> that filter a try. So I did.
>>> As well as Laura, I got the same error message and following your advice
>>> and formula, I calculated the memory request.
>>>
>>> I tested the filter using an image 565x440x100 pixels.
>>> BTW, why 6 in your formula "sizeof(double) x 6 bytes per pixel"?
>>> Do you store some intermediate results all the time?
>>> Lets calculate memory. In my case sizeof(double)=8.
>>>
>>> octave:2> 8*6*(565*440*100)
>>> ans = 1193280000
>>>
>>> OK. Filter + ImageItself = 1.19 +  0.0497 ~ 1.2397 GB
>>>
>>> I have 2 GB + 2 GB (swap). As you say, I almost hit the limit but 
>>> some piece is still left.
>>> Does a swap part count? I cropped the original image (which is 100 
>>> times larger then the one I used)
>>> down to 47MB just to test the filter and even with such a small 
>>> image size the filter fails to allocate memory?
>>>
>>> Does it mean that this filter has no use for boxes with limited resources?
>>>
>>> BTW, the only 
>>> "itkAnisotropicDiffusionVesselEnhancementImageFilterTest" fails.
>>> The other one, 
>>> "itkMultiScaleHessianSmoothed3DToVesselnessMeasureImageFilterTest" 
>>> works fine.
>>>
>>> Just by luck or you implemented another memory model?
>>>
>>> Thanks,
>>>
>>> Alex
>>>
>>> Luis Ibanez wrote:
>>>>
>>>> Hi Laura,
>>>>
>>>> This is annoying, but normal.
>>>>
>>>> This code computes Hessians of images, which in 3D requires
>>>> the allocation of sizeof(double) x 6 bytes per pixel.
>>>>
>>>> That is, you will need 48 bytes per pixel of your image
>>>> in order to store the resulting Hessian alone. There will be
>>>> of course additional intermediate allocations.
>>>>
>>>> If you need to process large images you may need a 64bits
>>>> machine with a larger memory...
>>>>
>>>> What is the actual size (in pixels) of the image that
>>>> you are processing ?
>>>>
>>>> Can you process selected regions of the image ?
>>>>
>>>> Usually there is a lot of empty (or at least, non interesting)
>>>> space in medical images. You could use the RegionOfInterest
>>>> filter to reduce the vessel enhancing processing to smaller
>>>> section of the image.
>>>>
>>>>
>>>> Please let us know,
>>>>
>>>>
>>>>     Thanks
>>>>
>>>>
>>>>        Luis
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> ---------------------------------
>>>> Laura Fernandez de Manuel wrote:
>>>>> Hi all,
>>>>>  We have been checking the implementation of the "Vessel 
>>>>> Enhancing Diffusion Filter" depicted here:
>>>>>  http://insight-journal.org/midas/handle.php?handle=1926/558
>>>>>  although it works properly with the example 3D images provided 
>>>>> (ranging from around 30 to 250 KB) we didn't succeed to make it 
>>>>> work in images any larger (5MB images failed already for 
>>>>> instance). We work in a system with 4GB RAM so we don't know 
>>>>> which can be the source of the "Failed to allocate memory for 
>>>>> image" errors that we get. Here, I attach the error message we 
>>>>> get:
>>>>>  
>>>>> ------------------------------------------------------------------------------------------------------ ./itkAnisotropicDiffusionVesselEnhancementImageFilterTest.exe image00_2.mhd 
>>>>> image_2Enhanced.mhd
>>>>> Reading input image : image00_2.mhd
>>>>> Enhancing vessels.........: image00_2.mhd
>>>>> Iteration:      0
>>>>> Computing vesselness for scale with sigma= 0.2
>>>>> Exception caught:
>>>>> itk::ExceptionObject (0138FB20)
>>>>> Location: "class itk::SymmetricSecondRankTensor<double,3> 
>>>>> *__thiscall itk::ImportImageContainer<unsigned long,class 
>>>>> itk::SymmetricSecondRankTensor<double,3> >:: AllocateElements 
>>>>> (unsigned long) const"
>>>>> File: itk3.6.0\code\common\itkImportImageContainer.txx
>>>>> Line: 193
>>>>> Description: Failed to allocate memory for image.
>>>>> ------------------------------------------------------------------------------------------------------  Thanks a 
>>>>> lot!
>>>>>  carlos & laura
>>>>>
>>>>>   
>>>>> ------------------------------------------------------------------------ 
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