Image Registration Method 4

Overview

If you are not familiar with the SimpleITK registration framework we recommend that you read the registration overview before continuing with the example.

Example Run

Running the Python code with the following inputs:

main('BrainProtonDensitySliceBorder20.png', 'BrainProtonDensitySliceShifted13x17y.png', 'displaceMeth4.hdf5')

produces the text and images below.

Output Text

Text Output (click triangle to collapse)

  0 =   -0.34741 : (0.7489820589143295, 0.6625902771882876)
  1 =   -0.35794 : (1.1941170159786205, 1.5580537716979028)
  2 =   -0.36440 : (1.9051160412039123, 2.261246765212973)
  3 =   -0.37602 : (2.871872087249055, 2.516947268605443)
  4 =   -0.38470 : (3.6200955730206092, 3.180394037638094)
  5 =   -0.39311 : (4.4612447257705234, 3.7211972392078527)
  6 =   -0.40514 : (5.141679502808506, 4.454005886944266)
  7 =   -0.41723 : (5.907661254726357, 5.096868201696701)
  8 =   -0.43019 : (6.5560275316669205, 5.8581967581861)
  9 =   -0.44583 : (7.330357208802286, 6.49097914661631)
 10 =   -0.46806 : (7.997195598009237, 7.2361815101927265)
 11 =   -0.49074 : (8.534950379630756, 8.079282805911808)
 12 =   -0.50826 : (9.118912175325061, 8.8910639470881)
 13 =   -0.53101 : (9.63045776525243, 9.750320084352104)
 14 =   -0.56404 : (10.098990332067213, 10.633766310125893)
 15 =   -0.59001 : (10.630601974691096, 11.480754539930025)
 16 =   -0.63413 : (11.116659778627087, 12.354681202318194)
 17 =   -0.67299 : (11.473938476275661, 13.288679024696028)
 18 =   -0.72745 : (12.148672165019144, 14.02674030214839)
 19 =   -0.79327 : (12.617660772785365, 14.90994451737016)
 20 =   -0.87309 : (12.98085733261639, 15.841657058300521)
 21 =   -1.01699 : (13.042825456798223, 16.83973518729914)
 22 =   -1.35611 : (12.739470845218626, 17.79261292323035)
 23 =   -1.08571 : (12.918604881493943, 17.32580354345343)
 24 =   -1.27645 : (13.083719467055985, 16.853853150260804)
 25 =   -1.35380 : (12.94726124517934, 17.063326665728187)
 26 =   -1.38892 : (13.035189536430005, 16.974480643882874)
 27 =   -1.39956 : (12.982485296781629, 17.00807399327061)
 28 =   -1.40347 : (13.011657891793998, 16.996870324217827)
 29 =   -1.40390 : (12.9964082376448, 17.00027453827247)
 30 =   -1.40429 : (13.004158163744622, 16.999287724360276)
 31 =   -1.40426 : (13.00026408587451, 16.999595858428517)
 32 =   -1.40432 : (13.002215400131234, 16.99951177551032)
-------
itk::simple::TranslationTransform
 TranslationTransform (0x559380484e10)
   RTTI typeinfo:   itk::TranslationTransform<double, 2u>
   Reference Count: 2
   Modified Time: 13963
   Debug: Off
   Object Name: 
   Observers: 
     none
   Offset: [13.0022, 16.9995]

Optimizer stop condition: RegularStepGradientDescentOptimizerv4: Step too small after 33 iterations. Current step (0.000976562) is less than minimum step (0.001).
 Iteration: 34
 Metric value: -1.4042980571486046

Input Images

Fixed Image

Moving Image

Output Image

Composition Image

Code

PythonR

#!/usr/bin/env python

import SimpleITK as sitk
import sys
import os


def command_iteration(method):
    print(
        f"{method.GetOptimizerIteration():3} "
        + f"= {method.GetMetricValue():10.5f} "
        + f": {method.GetOptimizerPosition()}"
    )


def main(args):
    if len(args) < 3:
        print(
            "Usage:",
            "ImageRegistrationMethod4",
            "<fixedImageFilter> <movingImageFile>",
            "<outputTransformFile> <numberOfBins> <samplingPercentage>",
        )
        sys.exit(1)

    fixed = sitk.ReadImage(args[1], sitk.sitkFloat32)
    moving = sitk.ReadImage(args[2], sitk.sitkFloat32)

    numberOfBins = 24
    samplingPercentage = 0.10

    if len(args) > 4:
        numberOfBins = int(args[4])
    if len(args) > 5:
        samplingPercentage = float(args[5])

    R = sitk.ImageRegistrationMethod()
    R.SetMetricAsMattesMutualInformation(numberOfBins)
    R.SetMetricSamplingPercentage(samplingPercentage, sitk.sitkWallClock)
    R.SetMetricSamplingStrategy(R.RANDOM)
    R.SetOptimizerAsRegularStepGradientDescent(1.0, 0.001, 200)
    R.SetInitialTransform(sitk.TranslationTransform(fixed.GetDimension()))
    R.SetInterpolator(sitk.sitkLinear)

    R.AddCommand(sitk.sitkIterationEvent, lambda: command_iteration(R))

    outTx = R.Execute(fixed, moving)

    print("-------")
    print(outTx)
    print(f"Optimizer stop condition: {R.GetOptimizerStopConditionDescription()}")
    print(f" Iteration: {R.GetOptimizerIteration()}")
    print(f" Metric value: {R.GetMetricValue()}")

    sitk.WriteTransform(outTx, args[3])

    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 // 2.0 + simg2 // 2.0)

    return {"fixed": fixed,
            "moving": moving,
            "composition": cimg}