[Insight-users] [Re] Correct use of MRIBiasFieldCorrectionFilter ?

[Martin Styner]

Hi Christof, Jinsung

Jinsung is quite right in his answer,  but in my experience there 
shouldn't be too much problems selecting the mean values and the 
standard deviation values, unless the image is quite complex and  the 
values are far off. The image that you provided seems to have only one 
single class, is that correct? Did you model this as a single class 
correction problem or did you also use the background as an additional 
class? If so then you need to mask the background, since the background 
should not be modeled as a class, because the bias field does not affect 
the background. As single class problem, the class parameter values 
should not be too sensitive (I only adjust the other values e.g. c_grow 
in few exceptional cases) , but also I never used the bias correction in 
images with only one class.
Can you tell us the classes and their values that you chose for the 
particular image you attached to your email?
In regard to the use of other basis functions: It is not necessary to 
use Legendre polynomials for the bias correction, any set of basis 
functions should theoretically be applicable. Although I would suggest 
if you try a different set of basis functions, just make sure to use 
orthogonal ones (orthonormal would be even better, Btw, the Legendre 
polynomials are orthogonal and not orthonormal).

When browsing through the itk-class description, I saw that the 
inter-slice brightness correction is applied by default. In general, 
this is actually not necessary (only for older MRI's it should be 
necessary to do this). So, maybe turning off the inter-slice correction 
could help.

Btw, since I am not at UNC anymore: can someone update the links to the 
papers in the doxygen documentation?
for the TMI paper: 
http://www.cs.unc.edu/~gerig/publications/TMI00-Styner-Bias.pdf
for the TR: 
http://www.vision.ee.ethz.ch/db_queries/get_abstract.cgi?techreps:Styner97a

One more question for Jinsung: Is the itk implementation using 
sub-sampling for the energy-computation? If so, which sub-sampling is it 
using. I think, it would be good if one could actually adjust the 
subsampling factor (in order to speed up the computation). Otherwise, I 
think you did a great job with this itk-class.

Regards
Martin


Jisung Kim wrote:
>>>Hi there,
>>
> 
> Hi. I'm the person who ported the algorithm to ITK.
> Martin Styner would know much better than me. However,
> I will try to answer your questions.
> 
> 
>>>I am using the MRIBiasFieldCorrectionFilter to
>>>correct the bias field of a MRI 
>>>surface coil (see attached image).
>>
> 
>>>The class itself works fine, I am able to input and
>>>output all the 
>>>parameters,the image and the masks.
>>
> 
>>>However, so far I was not able to obtain a good
>>>correction. The estimated bias 
>>>field is incorrect. So, I must be doing something
>>>wrong. (The filter finds 
>>>local minima instead of the global minimum).
>>
> 
> It's hard to get a good result when you don't know
> good initial statistics of the tissue classes. Martin
> Styner suggested in his paper to select a small region
> where pixels of all the tissue classes exist and then
> pick the means of intensities values of tissue classes
> in that region. Selecting the standard deviations for
> the tissue classes is a little bit trickier than
> selecting means. You can use the EnergyTablePlotter
> utility that will be under the "your ITK binary
> directory"/bin directory. you can see the usage by
> typing the command without any commmand line
> arguments. The utility will print energy values in a
> table format. You can pipe that result to a file and
> view energy function plot using mathematical packages
> such as GNU R or matlab. The plot shows a good
> separation between tissue class means. If you increase
> the standard deviations, the the picks between tissue
> class means would be lower. You want the picks are
> higher than 0.5 (in energy value).
> 
> 
>>>Could anybody suggest good values for cshrink,
>>>cgrow, x0 as well as the 
>>>coefficients for the legendre polynoms? I tried
>>>without success the parameter 
>>>value ranges suggested in the references.
>>
> 
> I think if you have good statistics of tissue classes,
> then you might get a reasonable result without
> changing the shrink factors. However, if you want to
> the optimization scheme works in a finer scales, use
> smaller values.
> 
> 
>>>Does anybody have experience with this filter?
>>
> 
>>>Is it possible to use a basis set other than the
>>>Legendre Polynoms (i.e. 
>>>Gaussians?)
>>
> 
> I am not sure whether current implementaion of the
> filter works with basis functions other than Legendre
> Plynoms. However, since the ITK optimization framework
> expects a class that has the interfaces defined by the
> SingleValuedCostFunction, it wouldn't be hard to use
> other base functions. I might have to double check it
> and change a little bit.
> 
> Thanks,
> 
> 
>>>It would be a pity if this elegant correction
>>>algorithm only worked for the 
>>>examples given in the two references ....
>>
> 
>>>Thanks
>>
> 
>>>Christof
>>
> 
>>>Christof Karmonik, PhD
>>>Baylor College of Medicine/The Methodist Hospital
>>>Mailstation M217
>>>6565 Fannin
>>>Houston, TX 77030
>>>Pager: (713) 708-2232
>>>Fax: (713) 790-4688
>>
> 
> =====
> Jisung Kim
> bahrahm@yahoo.com
> 106 Mason Farm Rd.
> 129 Radiology Research Lab., CB# 7515
> Univ. of North Carolina at Chapel Hill
> Chapel Hill, NC 27599-7515
> 
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-- 
---------------------------------------------------
Martin Styner, PhD. Ing. ETH
Group Head - Medical Image Analysis (MIA)
M.E.Mueller Research Center for Orthopaedic Surgery
Institute for Surgical Technology and Biomechanics
University of Bern
Murtenstr. 35,  P.O. Box 8354
CH-3001 Bern, Switzerland
Tel:   +41 31 632 0940
Fax:   +41 31 632 4951
Email: Martin.Styner@MEMcenter.unibe.ch
http://www.MEMcenter.unibe.ch
---------------------------------------------------