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

[Martin Styner]

Hi Jisung

Thanks for the modifications.
In regard to the subsampling of the energy-computation (which takes up 
most of the computation time since it has to be computed over the whole 
image, or a subsampled part of the image), I usually used a 
subsampling-factor of 2 (or 3) in each direction (factor 8 speed up). 
But since the subsampling is not just in the first dimension, I don't 
know whether iterator + n would work.  E.g. if subsamling in z is 2, 
then n would be different depending on whether you are iterating within 
a slice or at the slice edge to next slice. Or do I misunderstand the 
iterator-concept? Also, since the subsampling-factors in x,y and z might 
not be the same, this would not work either. Is there a possibility to 
specify in an iterator the subsampling factors or the iterator-step-size 
for each dimension separately and then just use ++iterator?
Best regards
Martin

On Tuesday, February 25, 2003, at 04:05 , Jisung Kim wrote:

> Hi Martin.
>
> Thanks for clarification. I will change the ITK
> documentation to include the right links. And I will
> modify the Legendre polynomial to allow users to
> specify subsampling. Current implementation doesn't
> allow subsampling. I'm think about changing the
> interator of the Legendre polynomial comply to the
> specs of STL Random Iterator, so that users can use
> iterator + n (unsigned int) in addition to ++iterator.
> Do you think it's a valid subsampling method for the
> bias correction? If so, what n would be a good default
> value?
>
> Thanks,
>
> Jisung.
> --- Martin Styner <martin_styner@ieee.org> wrote:
>> 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
>>
> === message truncated ===
>
>
> =====
> 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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----------------------------------------------------
Maya Styner-Shalev, M.D.
Gruppe Gastroenterologie
Murtenstrasse 35
CH - 3010 Bern
Switzerland
Tel work +41 031.632.8489
Tel home +41 031.371.0119
maya_styner@med.unc.edu
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