[ITK-users] How to use a binary mask in a registration process ?

[Bradley Lowekamp]

Hello,

So why do you think the masks are not working? What is the output with and without masks? Can you share you data? Is the metric value different with and w/o the masks?

I'd also recommend setting the SamplingStrategy to None until you have the masks working they way you expect.


Brad

On Sep 15, 2015, at 3:22 AM, neon29 <florian.nicolas at ensta-bretagne.org> wrote:

> Bradley Lowekamp wrote
>> Hello,
>> 
>> First verify that you are using the correct methods for setting the masks.
>> You wrote "SetMetricMovingImage" where I think you intended
>> "SetMetricMovingMask". Mistakes are easy to make in code and things like
>> this should be double/triple checked.
>> 
>> You likely need to perform a morphological erosion on the masks. With
>> smoothing and derivative computation, values outside the mask can effect
>> the metric near the boarder. You need to estimate how big the radius of
>> the erosion need to be based on your smoothing sigmas, and your scale.
>> 
>> Without sample code I don't think I can help further.
>> 
>> HTH,
>> Brad
>> 
>> 
>> On Sep 14, 2015, at 11:11 AM, neon29 <
> 
>> florian.nicolas@
> 
>> > wrote:
>> 
>>> Hi everybody!
>>> 
>>> I am currently trying to register sonar images using SimpleITK with
>>> Python.
>>> As the two images I want to register are geo-projected, I would like to
>>> restrict each image to the pixels where I have data (and get rid of the
>>> "background" which consists in '0').
>>> 
>>> I have already computed my two binary masks and I have tried to use it
>>> with
>>> the SetMetricFixedMask() and SetMetricMovingImage() methods but when I
>>> look
>>> at the result, it is the same as when I do not use any mask. Indeed, it
>>> seems that the registration process tend to overlap my masks.
>>> 
>>> I hope someone could help!
>>> 
>>> Florian
>>> 
>>> 
>>> 
>>> --
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> 
> Thanks for your reply!
> 
> Here is the piece of code:
> 
> fixed = sitk.ReadImage('#######/imgRef.png', sitk.sitkFloat32);
> moving = sitk.ReadImage('#######/imgRep.png', sitk.sitkFloat32)
> maskFixed = sitk.ReadImage('#######/maskRef.png', sitk.sitkUInt8)
> maskMoving= sitk.ReadImage('#######/maskRep.png', sitk.sitkUInt8)
> 
> # Handle optimizer
> R = sitk.ImageRegistrationMethod()
> 
> # Restrict the evaluation of the similarity metric thanks to masks
> R.SetMetricFixedMask(maskFixed)
> R.SetMetricMovingMask(maskMoving)
> 
> # Set metric as mutual information using joint histogram
> R.SetMetricAsMattesMutualInformation(numberOfHistogramBins=255)
> 
> # Gradient descent optimizer
> R.SetOptimizerAsRegularStepGradientDescent(learningRate=0.01, minStep=1e-5,
> numberOfIterations=100, gradientMagnitudeTolerance=1e-8)
> 
> #R.SetOptimizerScalesFromPhysicalShift()
> 
> R.SetMetricSamplingStrategy(R.REGULAR) #R.RANDOM
> 
> # Define the transformation (Rigid body here)
> 
> transfo = sitk.CenteredTransformInitializer(fixed, moving,
> sitk.Euler2DTransform())
> 
> R.SetInitialTransform(transfo)
> 
> # Define interpolation method
> R.SetInterpolator(sitk.sitkLinear)
> 
> # Add command to the registration process
> R.AddCommand(sitk.sitkIterationEvent, lambda: command_iteration(R))
> R.AddCommand(sitk.sitkStartEvent, lambda: start_plot())
> R.AddCommand(sitk.sitkEndEvent, lambda: end_plot())
> R.AddCommand(sitk.sitkIterationEvent, lambda: current_plot(R))
> # Perform registration
> outTx = R.Execute(fixed, moving)
> 
> print(outTx)
> print("--------")
> print("Optimizer stop condition:
> {0}".format(R.GetOptimizerStopConditionDescription()))
> print("Number of iterations: {0}".format(R.GetOptimizerIteration()))
> print("--------")
> 
> # Perform transformation and resample the moving image
> 
> # Save transformation as tfm file
> sitk.WriteTransform(outTx,
> '/home/egs/f_nicolas/CODES_THESE/transfo_final.tfm')
> #sitk.Show(transfo.GetDisplacementField(),"Displacement field")
> 
> # Resample moving image according to the last transformation
> resampler = sitk.ResampleImageFilter()
> resampler.SetReferenceImage(fixed)
> resampler.SetInterpolator(sitk.sitkLinear)
> #resampler.SetDefaultPixelValue(100)
> resampler.SetTransform(outTx)
> out = resampler.Execute(moving)
> 
> 
> 
> 
> 
> 
> --
> View this message in context: http://itk-insight-users.2283740.n2.nabble.com/How-to-use-a-binary-mask-in-a-registration-process-tp7587799p7587809.html
> Sent from the ITK Insight Users mailing list archive at Nabble.com.
> _____________________________________
> Powered by www.kitware.com
> 
> Visit other Kitware open-source projects at
> http://www.kitware.com/opensource/opensource.html
> 
> Kitware offers ITK Training Courses, for more information visit:
> http://www.kitware.com/products/protraining.php
> 
> Please keep messages on-topic and check the ITK FAQ at:
> http://www.itk.org/Wiki/ITK_FAQ
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