[Insight-users] Do RecursiveHessian or
RecursiveGaussian requireisotropic voxels?
Luis Ibanez
luis.ibanez at kitware.com
Mon Nov 21 10:09:36 EST 2005
Hi Jerome,
You are right !
I'm going to tell you that acquiring datasets
with 5mm interslice spacing is just *bad imaging*.
We hope that one day MRI radiologist will start looking
at the orthogonal views of the dataset they acquire and
perceive how much they are violating the Shannon sampling
theorem.
We have heard the standard replies:
* "It would take too long..."
* "The information in the slice is enough..."
The sad reality is that algorithm developers are not
working close enough to those who design the image
acquisition protocols, and radiologist still look
only at slices, not at 3D view of the the datasets
they acquire. So they rarely realize, or care about
the fact that those are not really 3D datasets, they
are just collections of isolated and uncorrelated
slices.
....
About your processing,
Thanks for posting the Phantom dataset,
it make things much clearer.
The cropped region that you posted is unfortunately too
small for performing the processing on it. The reason
is that the vessels that you are looking at are in the
range of 20 pixels in diameter. The sigma that will capture
those features is such that for your cropped image the
border effects will take over the image before we get
the vessels to show up.
(unless, of course, I'm looking at the wrong vessels...)
The application that you want to use is available in
InsightApplications/
DuctExtraction
It uses the Vesselness filter, (based on the Hessian),
and adds some preprocessing by a gradient anisotropic
filter, and post-processing with a region growing algorithm.
If you load your images in, you will have an easy test-bed
for your vessel detection program.
...
The reason why you get better results from the IsoPhantom
(the image that was resampled from the 5mm datasets) is that
the RecursiveGaussian image filter use IIR filters internally
and it bases its computation on 4 consecutive samples. It doesn't
mean, however that the information in the IsoPhantom image is better.
The solution is to go back to your image acquisition friends,
and show them how their datasets look in orthogonal slices.
As Dr. Michael Vannier said once at SPIE:
"Good imaging,
beats good image processing"
....
Please give it a try to the DuctExtraction application, and let us
know if you find any problem with its usage.
Note that the application was assuming that the vessels were bright
over a dark background (since most of the time there is a contrast
agent involved). If this is not the case for your images, you may
want to revert the intensities in your image before feeding it into
this program. (The IntensityWindowingImageFilter may be used for this
purpose).
Best Regards,
Luis
----------------------
SCHMID, Jerome wrote:
>
> Luis Ibanez wrote:
>
> >
> >
> > Hi Jerome,
> >
> > No, the RecursiveGaussian filter does not requires the voxels
> > to be isotropic.
> >
> > The RecursiveGaussian filter takes the Pixel size into account.
> >
> > This means that the computations are performed correctly in
> > physical units.
> >
> > The values reported by the RecursiveGaussian and its associated
> > filters must be interpreted in terms of derivatives with respect
> > to millimeters.
> >
> > Note that if what you are doing is multi-scale analysis (and it
> > sounds like that, since you are searching for the sigma that matches
> > a particular blob...) then you should use the Normalize along scales
> > option, which will make possible for you to compare the results from
> > one scale (sigma) with the results of a different scale (sigma).
>
> Hi Luis,
>
> Thanks for the reply. Well I have seen the option of the normalization
> across scale, but my problem is not related to a mutliscale analysis.
> Anyway I set the flag to true already so it is not correlated I think.
> Moreover you confirm to me that the isotropy should not be a pb as the
> sigma is properly used as a physical unit instead of a voxel one.
>
> >
> >
> > ---
> >
> > Just for the record...
> >
> > What is the size of the Blobs in your experiments in pixels ?
> > and what is the size of those pixels in millimeters ?
> >
>
> I am working on a phantom which has vessels and tumours in a fake liver.
> My objective is the vessels segmentation and by consequence I would like
> to detect them and *reject* the tumours. As I told previously by
> analysing the eigenvalues of the hessian with an adequate scale, one can
> have a "vesselness" value, as showed in one of the recent filter is the
> CVS. (BTW which as a little error that I pointed in the bug tracker ).
>
> Unfortunately, my data has an ugly slice thickness: 5 mm....! I expect
> in the future more accurate data but in the meantime I do some tests...
> So the diameter of a tumour ( a nice ball ) is 15x15x15 in physical
> units which correspond roughly to 21x21x3 in voxels.
>
> I guess that you may say me that due to the huge slice thickness some
> approach may become invalid, but by resampling the volume in iso data I
> get "better" results....
>
> >
> > If you find that the behavior of the filter is incorrect, please
> > post a minimal piece of code that illustrates your point, and if
> > necessary post an input/output image on a web accessible place.
> >
> I will do this:
>
> - Find to this address :
> http://moscao.free.fr/PrivateDownloads/EigenToRGBSrc.zip, a zip that
> contains:
> - an example test that is run like this, "exec InputImage OutputImage
> [sigma]"
> This example compute the hessian with the sigma value, and the
> eigenvalues of the hessian matrix of each voxel. An RGB image is
> eventually created whose each component is an eigenvalue. A useful
> filter is so added to the archive called
> "itkHessianEigenValuesToRGBImageFilter.h".
>
> To this address: http://moscao.free.fr/PrivateDownloads/Phantom.zip , a
> zip archive that contains:
>
> A version of the phantom cropped but *anisotropic* that show a part of
> the liver with a simple vessel and a tumour. NOTE: I am looking for dark
> vessel with bright background.
> Its size: 95x81x14 , spacing: 0.7, 0.7, 5.0
> It is an hdr/img Analyze image. Load it in volview with this orientation
> params: RAS + Posterior + Left. Then in slice 8 (in axial direction)
> there is a tumour at (46,17,40) (physical units)
>
>
> A version of the phantom cropped *isotropic* ( and BTW scaled previously
> to avoid huge data) that show a part of the liver with a simple vessel
> and a tumour.
> Its size: 48x55x44 , spacing: 1.4, 1.4, 1.4
> It is an hdr/img Analyze image. Load it in volview with this orientation
> params: RAS + Posterior + Left. Then in slice 8 (in axial direction)
> there is a tumour at (52,31,38) (physical units)
>
>
> Now run the program with the *first* image and set a sigma between 3 and
> 6. Open the image in volview, find the corresponding tumour and look at
> its eigenvalues: they should be roughly like this:
> 0.005 , 40, 40 ---> NOT a blob...!?
>
> Now run the program with the *second* image and set a sigma between 3
> and 6. Open the image in volview, find the corresponding tumour and look
> at its eigenvalues: they should be roughly like this:
> 3 , 4, 4 ---> Looks more like a blob (i.e. eigenvalues close to each other )
>
>
> Maybe the pb is that one cannot trust the results with such
> images/spacing but I wonder why by passing to isotropric voxels thre
> result are more as expected...?
>
> Thanks!
>
> Jerome
>
>
> >
> > Best Regards,
> >
> >
> > Luis
> >
> >
>
>
>
>
>
> >
> > ----------------------
> > SCHMID, Jerome wrote:
> >
> >> Dear Itk users,
> >>
> >> I am playing with vesselness measure based on eigen values of
> Hessian matrices. I have noticed a quite odd behaviour and I would like
> to know if it is normal.
> >>
> >> Basically the eigenvalues change "a lot" whether or not my input
> image has or not isotropic voxels. The "a lot" changing is related to
> the tubes or blobs detection with the Hessian+eigenValues principle,
> i.e. roughly lambda1 = lambda2 = lambda3 for a blob and lambda1 = 0 and
> lambda1 << 0 and lambda3 << 0 for a tube.
> >>
> >> In my case with a blob and non isotropic image: lambda1 close to 0
> and lambda2/3 << 0. -> odd(?)
> >> In my case with a blob and isotropic image: lambda1 = lambda2 =
> lambda3 (roughly) -> expected
> >>
> >> Knowing that the hessian is computed with the
> RecursiveGaussianImageFilter that is based on the RecursiveGaussian
> filter, I wonder whether the pb is here.
> >>
> >> A note: my sigma for the detection is close to the radius of the
> blob, and it is defined in physical units. I expect that the recursive
> gaussian filter uses sigma in physical units ( which looks to be the
> case, looking at the distinction variance/sigma, isn'it?).
> >>
> >> Any suggestion? Thanks!
> >>
> >> Best Regards,
> >>
> >> Jerome Schmid
> >>
> >> -----------------------------------
> >> Jerome SCHMID
> >> Project Manager/ Engineer
> >> Augmented and Virtual Reality
> >> MIS Centre
> >> Prince of Wales Hospital
> >> Chinese University Of Hong-Kong
> >> -----------------------------------
> >>
> >>
> >> ------------------------------------------------------------------------
> >>
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> >
> >
> >
> >
>
>
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