[Insight-users] Performance of 3D Watershed?

[Joshua Cates]

Hi Harri,

Glad you got this working.  For visualization, I would suggest mapping the
unsigned long labels to different RGB values and looking at slices of your
data.  (If quantize to 256 graylevels, but you will probably 
be losing a lot of information.)

Josh.
______________________________
 Josh Cates			
 School of Computer Science	
 University of Utah
 Email: cates@sci.utah.edu
 Phone: (801) 587-7697
 URL:   www.cs.utk.edu/~cates


On Wed, 23 Oct 2002, Harri Tapio Jaalinoja wrote:

> 
> Hi Joshua,
> 
> thanks for your help! Right endianness did the trick.
> 
> I have yet to figure out how to visualize the structure (we use OpenDX),
> but apart from fine tuning the filtering values, the segmentation
> code seems to be in place. So easy?
> 
> Thanks,
> Harri
> 
> On Tue, 22 Oct 2002, Joshua Cates wrote:
> 
> > Hi,
> >
> > My first guess as to what is happening is that you are processing a data
> > set which contains too much detail for the algorithm to handle in a
> > reasonable time frame.  The complexity of the watersheds algorithm is
> > nonlinear, so too much detail in an image (regardless of image size) will
> > cause executions times to blow up from seconds to hours.  (Have you
> > checked the endianness of your data?  Reading in unsigned short data using
> > the wrong byte order will give very noisy looking data.)
> >
> > Good preprocessing to remove noise and uninteresting detail is critical to
> > getting a good result with the watersheds technique.  Consider more
> > iterations of the GradientMagnitudeAnisotropicDiffusionImageFilter (10 is
> > a good number to try, with conductance term 1.0).  Also, cropping the data
> > to contain only the region you are interested in is helpful.  The
> > watersheds algorithm operates on the entire image at once, so any areas of
> > the image which are uninteresting are only going to slow down the process
> > for you.
> >
> > That said, I'm surprised that the algorithm is bogging down without using
> > up more of your memory.  Is the amount of free memory you show during
> > processing?  I have run the watersheds algorithm on MRI volumes larger
> > than your data (256x256x123) by smoothing first with
> > GradientAnisotropicDiffusionImageFilter 10x 1.0 conductance.  After
> > extracting edge features, the watersheds finishes in roughly 30 seconds.
> >
> > Hope this helps,
> >
> > Josh.
> >
> > ______________________________
> >  Josh Cates
> >  School of Computer Science
> >  University of Utah
> >  Email: cates@sci.utah.edu
> >  Phone: (801) 587-7697
> >  URL:   www.cs.utk.edu/~cates
> >
> >
> > On Tue, 22 Oct 2002, Harri Tapio Jaalinoja wrote:
> >
> > >
> > > Hi!
> > >
> > > I am running code based on the watershed example to segment a 101^3 cube
> > > of electron microscopy density data (type = short).
> > >
> > > The problem is that the code seems to stall my machine.
> > > The progress command meter writes first 3 dots almost right away, the 4th
> > > after a while, but the 5th I never saw.
> > >
> > > I am not using the gradient magnitude image filter because the density
> > > values as such should be a valid height function.
> > >
> > > I don't have a smaller data cube at the moment I could experiment with.
> > > I can simply read data from the existing file, to fill in a smaller cube.
> > > This of course messes up the structure, but might give some idea about the
> > > processing. A cube of 33^3 already stalls the machine, 32^3 is processed
> > > fairly quickly.
> > >
> > > My processor:
> > > model name      : AMD Athlon(TM) XP2200+
> > > stepping        : 0
> > > cpu MHz         : 1800.110
> > >
> > > Available memory:
> > > [hajaalin@gene work]$ free -m
> > >              total       used       free     shared    buffers     cached
> > > Mem:          1008        170        837          0         15         77
> > > -/+ buffers/cache:         77        931
> > > Swap:          243         11        231
> > >
> > > I am running Mandrake Linux 9.0.
> > >
> > > Here's some info about the data
> > > Number of images:               1
> > > Dimensions:                     101 101 101 voxels
> > > Page dimensions:                101 101 101 voxels
> > > Channels:                       1
> > > Data type:                      short (size = 2)
> > > Color model:                    Gray scale
> > > Voxel units/sampling:           8.89903 8.89903 8.89903 A/voxel
> > > Min, max, ave, std:             -1813.35 2149.04 11.9238 0
> > >
> > > I have tried various values for the anisotropic diffusion filter. At all
> > > values a cube size can be found that is no longer processed (at least
> > > nowhere as quickly as the one step smaller cube). The biggest cube (32^3)
> > > was in fact processed without the filter.
> > >
> > > With some values I get this:
> > > ..........Exception caught during processing.
> > > Unknown
> > > itk::ERROR: itk::watershed::SegmentTreeGenerator::MergeSegments:: An
> > > unexpected and fatal error has occurred. This is probably the result of
> > > overthresholding of the input image.
> > >
> > > which at least clearly tells that something is going wrong.
> > >
> > > Based on your experience, what do you make of the numbers and ramblings
> > > above? Any ideas I could experiment with?
> > >
> > > Thanks for your help!
> > >
> > > Harri
> > >
> > > _______________________________________________
> > > Insight-users mailing list
> > > Insight-users@public.kitware.com
> > > http://public.kitware.com/mailman/listinfo/insight-users
> > >
> >
> >
> 
> 
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