Hello everyone,
I am currently working with SimpleITK for image registration, specifically employing sitk.BSplineTransform and the SetOptimizerAsGradientDescent optimizer.
I’ve been reviewing a Python example that demonstrates B-spline registration (provided below for reference).
My observation is that, in this code, there aren’t any explicit options or parameters for incorporating a regularization term directly within the SetOptimizerAsGradientDescentLineSearch method or when configuring the registration process. While SetShrinkFactorsPerLevel and SetSmoothingSigmasPerLevel can provide a form of implicit smoothing across multi-resolution levels, I’m looking for a more explicit regularization mechanism.
My question is: Is it possible to directly incorporate a regularization term (e.g., a penalty for excessive deformation or a smoothness prior for the B-spline coefficients) when using sitk.GradientDescent with sitk.BSplineTransform in SimpleITK? If so, could you please advise on how this can be implemented or configured?
Any insights, documentation references, or illustrative examples would be greatly appreciated.
Thank you
#!/usr/bin/env python
from __future__ import print_function
import SimpleITK as sitk
import sys
import os
def command_iteration(method) :
print("{0:3} = {1:10.5f}".format(method.GetOptimizerIteration(),
method.GetMetricValue()))
print("\t#: ", len(method.GetOptimizerPosition()))
def command_multi_iteration(method) :
print("--------- Resolution Changing ---------")
if len ( sys.argv ) < 4:
print( "Usage: {0} <fixedImageFilter> <movingImageFile> <outputTransformFile>".format(sys.argv[0]))
sys.exit ( 1 )
fixed = sitk.ReadImage(sys.argv[1], sitk.sitkFloat32)
moving = sitk.ReadImage(sys.argv[2], sitk.sitkFloat32)
transformDomainMeshSize=[10]*moving.GetDimension()
tx = sitk.BSplineTransformInitializer(fixed,
transformDomainMeshSize )
print("Initial Parameters:");
print(tx.GetParameters())
R = sitk.ImageRegistrationMethod()
R.SetMetricAsMattesMutualInformation(50)
R.SetOptimizerAsGradientDescentLineSearch(5.0, 100,
convergenceMinimumValue=1e-4,
convergenceWindowSize=5)
R.SetOptimizerScalesFromPhysicalShift( )
R.SetInitialTransform(tx)
R.SetInterpolator(sitk.sitkLinear)
R.SetShrinkFactorsPerLevel([6,2,1])
R.SetSmoothingSigmasPerLevel([6,2,1])
R.AddCommand( sitk.sitkIterationEvent, lambda: command_iteration(R) )
R.AddCommand( sitk.sitkMultiResolutionIterationEvent, lambda: command_multi_iteration(R) )
outTx = R.Execute(fixed, moving)
print("-------")
print(outTx)
print("Optimizer stop condition: {0}".format(R.GetOptimizerStopConditionDescription()))
print(" Iteration: {0}".format(R.GetOptimizerIteration()))
print(" Metric value: {0}".format(R.GetMetricValue()))
sitk.WriteTransform(outTx, sys.argv[3])
if ( not "SITK_NOSHOW" in os.environ ):
resampler = sitk.ResampleImageFilter()
resampler.SetReferenceImage(fixed);
resampler.SetInterpolator(sitk.sitkLinear)
resampler.SetDefaultPixelValue(100)
resampler.SetTransform(outTx)
out = resampler.Execute(moving)
simg1 = sitk.Cast(sitk.RescaleIntensity(fixed), sitk.sitkUInt8)
simg2 = sitk.Cast(sitk.RescaleIntensity(out), sitk.sitkUInt8)
cimg = sitk.Compose(simg1, simg2, simg1//2.+simg2//2.)
sitk.Show( cimg, "ImageRegistration1 Composition" )
