Image Registration Method 3

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', 'displaceMeth3.hdf5')

produces the text and images below.

Output Text

Text Output (click triangle to collapse)

Estimated Scales:  (29682.41582933708, 29682.168476696, 1.0000000000010232, 0.999999999998181)
  0 = -0.99671 : (1.0001225188582936, -4.8155319621057874e-05, 14.24580144271548, 18.231972935092553)
  1 = -0.90442 : (1.0001395698120492, -5.723494632868289e-05, 13.436891660253242, 17.644040146872914)
  2 = -0.97294 : (1.0000524825397714, -3.538901200507155e-05, 12.784457666827517, 16.886194597858392)
  3 = -0.99664 : (1.000078552246351, -3.493898453141627e-05, 13.248020397809663, 17.07357013992443)
  4 = -0.99617 : (1.0000080585357958, -3.574957852612509e-05, 13.003964467172034, 17.01937883295938)
  5 = -0.99998 : (0.9999690242080485, -8.938378761798914e-06, 12.941455002408405, 16.77731982244243)
  6 = -0.99743 : (0.9999729946034386, -1.4195394490341945e-05, 12.980498953463153, 16.89606564275891)
  7 = -0.99946 : (0.9999822072360259, -1.9442192886012526e-05, 13.009300985166485, 17.01770216218198)
  8 = -0.99998 : (1.0000081565952237, -1.536015920575109e-05, 12.97497046153363, 16.965475115002747)
  9 = -0.99990 : (1.000006961859454, -1.5839846033647187e-05, 12.996178272885958, 16.98842704816722)
 10 = -0.99999 : (0.999997104263667, -1.5781032972153657e-05, 13.008157869541614, 17.01728968260679)
 11 = -0.99998 : (0.9999981089260577, -1.480066274538104e-05, 13.000149494110696, 17.003873030789478)
 12 = -1.00000 : (1.0000035245702283, -1.1964202117479094e-05, 12.999557108219037, 16.988259265497895)
 13 = -0.99999 : (1.0000015232779387, -1.2092316764425097e-05, 12.999892054939323, 16.996064581822232)
 14 = -1.00000 : (0.9999991283248564, -1.1669994922994523e-05, 13.00013101903772, 17.003873425942555)
 15 = -1.00000 : (0.9999999572679382, -1.1067338273415268e-05, 12.99999566483145, 16.99996952183884)
 16 = -1.00000 : (1.0000015262190347, 2.7428357741776532e-05, 13.001481019896447, 17.00123717053477)
 17 = -1.00000 : (1.0000010761172178, 2.70338949452921e-05, 13.00064749562188, 17.000728337318556)
 18 = -1.00000 : (1.0000004942557568, 2.6224543927636213e-05, 12.999879933384591, 17.000124581130446)
 19 = -1.00000 : (0.9999997860745542, 2.334309634129523e-05, 13.000293057931712, 16.99986431610512)
 20 = -1.00000 : (0.9999997971406921, 2.288036936123565e-05, 13.000070149678, 16.99996389693141)
 21 = -1.00000 : (0.9999999481117745, 2.1399925381340785e-05, 12.999868757877147, 17.000101896108283)
 22 = -1.00000 : (0.999999997953508, 2.0861222466294866e-05, 12.999980029544846, 17.000051701641695)
 23 = -1.00000 : (1.0000000486509963, 1.633859430051924e-05, 13.000031070402398, 16.999940906626833)
-------
itk::simple::Similarity2DTransform
 Similarity2DTransform (0x5602d8e0afa0)
   RTTI typeinfo:   itk::Similarity2DTransform<double>
   Reference Count: 3
   Modified Time: 2649
   Debug: Off
   Object Name: 
   Observers: 
     none
   Matrix: 
     1 -1.63386e-05 
     1.63386e-05 1 
   Offset: [13.0022, 16.9981]
   Center: [111.204, 131.591]
   Translation: [13, 16.9999]
   Inverse: 
     1 1.63386e-05 
     -1.63386e-05 1 
   Singular: 0
   Angle       = 1.63386e-05
   Scale =1

Optimizer stop condition: RegularStepGradientDescentOptimizerv4: Step too small after 24 iterations. Current step (6.10352e-05) is less than minimum step (0.0001).
 Iteration: 25
 Metric value: -0.9999999568784146

Input Images

Fixed Image

Moving Image

Output Image

Composition Image

Code

PythonR

#!/usr/bin/env python
""" A SimpleITK example demonstrating image registration using the
    correlation metric and the center of mass initial transformation
    estimation method. """

import sys
import os
import SimpleITK as sitk


def command_iteration(method):
    """ Callback invoked when the optimization has an iteration """
    if method.GetOptimizerIteration() == 0:
        print("Estimated Scales: ", method.GetOptimizerScales())
    print(
        f"{method.GetOptimizerIteration():3} "
        + f"= {method.GetMetricValue():7.5f} "
        + f": {method.GetOptimizerPosition()}"
    )


def main(args):
    """ A SimpleITK example demonstrating image registration using the
        correlation metric and the center of mass initial transformation
        estimation method. """
    if len(args) < 3:
        print(
            "Usage:",
            "ImageRegistrationMethod3"
            "<fixedImageFilter> <movingImageFile>  <outputTransformFile>",
        )
        sys.exit(1)

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

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

    R = sitk.ImageRegistrationMethod()

    R.SetMetricAsCorrelation()

    R.SetOptimizerAsRegularStepGradientDescent(
        learningRate=2.0,
        minStep=1e-4,
        numberOfIterations=500,
        gradientMagnitudeTolerance=1e-8,
    )
    R.SetOptimizerScalesFromIndexShift()

    tx = sitk.CenteredTransformInitializer(fixed, moving, sitk.Similarity2DTransform())
    R.SetInitialTransform(tx)

    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)