[Insight-users] Re: SetFixedImageRegion : Max and OR filters

[Luis Ibanez]

Hi Wei,

You will find the OR and MAX image filters in the Doxygen documentation

http://www.itk.org/Insight/Doxygen/html/classitk_1_1OrImageFilter.html
http://www.itk.org/Insight/Doxygen/html/classitk_1_1MaximumImageFilter.html

If you want to implement another blending strategy, you will only
need to redefine the line where the functor value is computed.


   Regards,


     Luis



--------------------
Wei-Lin Bee wrote:
> Dear Luis,
> 
> Following your instructions, I modified my program as attachment.
> 
> One thing I didn't understand in D) is what is OR filter or the MAX image
> filters? Is it located at somewhere in itkSoftwareGuide?
> 
> 
> 
> Thanks.
> 
> 
> 
> Best,
> Tiger
> 
> 
> Quoting Luis Ibanez <luis.ibanez at kitware.com>:
> 
> 
>>Hi Wei,
>>
>>In order to combine both the Fixed and Resampled Moving
>>image in a single image, you must perform two resamplings.
>>
>>Please do the following:
>>
>>A) First you must estimate the size of the image that will
>>    be able to contain the composition of the fixed and the
>>    resampled moving image.
>>
>>B) Use the ResampleImageFilter and especify that size as
>>    the output image size. use the Identity transformation
>>    and provide the fixed image as input. That will map the
>>    fixed image into the frame of the final combined image.
>>
>>C) Use *another* ResampleImageFilte, set the same output
>>    size to the size of the combined image that you computed
>>    in (A), set the transform to the one that you got from
>>    your registration, set the moving image as the input to
>>    this filter, and execute the filter.
>>
>>D) Finaly, take the two images resulting from (B) and (C)
>>    and merge them using the OR filter or the MAX image
>>    filters.  You may eventually prefer to implement a
>>    blending filter for this task.
>>
>>
>>Regards,
>>
>>
>>    Luis
>>
>>
>>
>>--------------------
>>Wei-Lin Bee wrote:
>>
>>
>>>Hi Luis,
>>>
>>>Thank you very much for your instruction.
>>>
>>>I've change MutualInformationImageToImageMetric to
>>
>>MeanSquaresImageToImageMetric
>>
>>>and replaced related parameters. Now the program seems work well.
>>>
>>>
>>>One more question:
>>>
>>>The output image seems to be resampled moving image at the time. How to
>>
>>combine
>>
>>>fixed image and moving image to a stitched one as output image?
>>>
>>>
>>>Thanks abound!
>>>
>>>
>>>Best,
>>>Tiger
>>>
>>>ps. again, the attachment is my program for your reference...
>>>
>>>
>>>
>>>Quoting Luis Ibanez <luis.ibanez at kitware.com>:
>>>
>>>
>>>
>>>>Hi Wei,
>>>>
>>>>Thanks for your detailed report.
>>>>
>>>>Your code seems to be in good shape.
>>>>
>>>>The run-time problem that you are facing is
>>>>an exception being thrown when you invoke the
>>>>method GetValue() in the MutualInformation metric.
>>>>
>>>>You *must* put GetValue() inside a try/catch block
>>>>so you will be able to know when an exception is
>>>>thrown.  Please look at the ITK SoftwareGuide,
>>>>
>>>>   http://www.itk.org/ItkSoftwareGuide.pdf
>>>>
>>>>there are multiple examples on how to catch exceptions.
>>>>
>>>>
>>>>Once you add the try/catch block, this particular
>>>>exception will show you the following message:
>>>>
>>>>
>>>>itk::ExceptionObject (0x8199f10)
>>>>Location: "Unknown"
>>>>File:
>>>>
>>>
>>>
> /home/ibanez/src/Insight/Code/Algorithms/itkMutualInformationImageToImageMetric.txx
> 
>>>>Line: 155
>>>>Description: itk::ERROR: MutualInformationImageToImageMetric(0x8139878):
>>>>All the sampled point mapped to outside of the moving image
>>>>
>>>>
>>>>Which means that MutualInformation was not able to find enough sampling
>>>>point in the subregion in which you are performing this computation.
>>>>
>>>>
>>>>As you probably know, after reading the Registration chapter
>>>
>>>>from the SoftwareGuide, MutualInformation samples the image
>>>
>>>>randomly in order to compute the joint histogram of gray levels.
>>>>When not enough samples fall inside the FixedImageRegion, this
>>>>exception is thrown, because the estimation is expected to be
>>>>very poor with such a small number of samples.
>>>>
>>>>
>>>>You could go around this by increasing the number of samples....
>>>>but in practice, what we could suggest you is to NOT use Mutual
>>>>Information as the metric for this task. This is based on two
>>>>arguments:
>>>>
>>>>
>>>>1) Your images are of the same modality.
>>>>
>>>>2) Your images are almost black with scattered bright signals
>>>>   (we assume they are cells in fluorescent microscopy).
>>>>   In that regard, information is only carried by the cells, not
>>>>   the background. The chances that some of the samples from Mutual
>>>>   Information will fall inside a cell are very small. In most cases
>>>>   your image will appear simply as a black field to the IM Metric.
>>>>
>>>>
>>>>Please replace Mutual Information with a metric better suited for
>>>>this problem, such as Normalized Correlation, or MeanSquares.
>>>>
>>>>
>>>>BTW: Now you will not need to compute 100 times the metric and make
>>>>an average from it. You will not need the Normalization filters
>>>>either, since NormalizedCorrelation is invariant to linear changes
>>>>in the intensity levels.
>>>>
>>>>
>>>>Also, remember to put always *try/catch* blocks around the pieces
>>>>of code that can potentially throw exceptions.
>>>>
>>>>
>>>>
>>>>  Regards,
>>>>
>>>>
>>>>
>>>>     Luis
>>>>
>>>>
>>>>
>>>>--------------------
>>>
>>
>>
>>
> 
> 
> 
> 
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