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imregister

Intensity-based image registration

Syntax

moving_reg = imregister(moving,fixed,transformType,optimizer,metric)
[moving_reg,R_reg] = imregister(moving,Rmoving,fixed,Rfixed,transformType,optimizer,metric)
___ = imregister(___,Name,Value)

Description

example

moving_reg = imregister(moving,fixed,transformType,optimizer,metric) transforms the 2-D or 3-D image, moving, so that it is registered with the reference image, fixed. Both moving and fixed images must be of the same dimensionality, either 2-D or 3-D. transformType is a character vector that defines the type of transformation to perform. optimizer is an object that describes the method for optimizing the metric. metric is an object that defines the quantitative measure of similarity between the images to optimize. Returns the aligned image, moving_reg.

[moving_reg,R_reg] = imregister(moving,Rmoving,fixed,Rfixed,transformType,optimizer,metric) transforms the spatially referenced image moving so that it is registered with the spatially referenced image fixed. Rmoving and Rfixed are spatial referencing objects that describe the world coordinate limits and the resolution of moving and fixed.

___ = imregister(___,Name,Value) specifies additional options with one or more Name,Value pair arguments.

Examples

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Read two images. This example uses two magnetic resonance (MRI) images of a knee. The fixed image is a spin echo image, while the moving image is a spin echo image with inversion recovery. The two sagittal slices were acquired at the same time but are slightly out of alignment.

fixed = dicomread('knee1.dcm');
moving = dicomread('knee2.dcm');

View the misaligned images.

imshowpair(fixed, moving,'Scaling','joint')

Create the optimizer and metric, setting the modality to 'multimodal' since the images come from different sensors.

[optimizer, metric] = imregconfig('multimodal')
optimizer = 
  registration.optimizer.OnePlusOneEvolutionary

  Properties:
         GrowthFactor: 1.050000e+00
              Epsilon: 1.500000e-06
        InitialRadius: 6.250000e-03
    MaximumIterations: 100
metric = 
  registration.metric.MattesMutualInformation

  Properties:
    NumberOfSpatialSamples: 500
     NumberOfHistogramBins: 50
              UseAllPixels: 1

Tune the properties of the optimizer to get the problem to converge on a global maxima and to allow for more iterations.

optimizer.InitialRadius = 0.009;
optimizer.Epsilon = 1.5e-4;
optimizer.GrowthFactor = 1.01;
optimizer.MaximumIterations = 300;

Perform the registration.

movingRegistered = imregister(moving, fixed, 'affine', optimizer, metric);

View the registered images.

figure
imshowpair(fixed, movingRegistered,'Scaling','joint')

Input Arguments

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Image to be registered, specified as a 2-D or 3-D grayscale image.

Data Types: single | double | int8 | int16 | int32 | uint8 | uint16 | uint32

Spatial referencing information associated with the image to be registered, specified as an imref2d or imref3d object.

Reference image in the target orientation, specified as a grayscale image.

Data Types: single | double | int8 | int16 | int32 | uint8 | uint16 | uint32

Spatial referencing information associated with the reference (fixed) image, specified as an imref2d or imref3d object.

Geometric transformation to be applied to the moving image, specified as one of the following values:

ValueDescription
'translation'(x,y) translation in 2-D, or (x,y,z) translation in 3-D.
'rigid'Rigid transformation consisting of translation and rotation.
'similarity'Nonreflective similarity transformation consisting of translation, rotation, and scale.
'affine'Affine transformation consisting of translation, rotation, scale, and shear.

The 'similarity' and 'affine' transformation types always involve nonreflective transformations.

Method for optimizing the similarity metric, specified as a RegularStepGradientDescent or OnePlusOneEvolutionary optimizer object.

Image similarity metric to be optimized during registration, specified as a MeanSquares or MattesMutualInformation metric object.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'DisplayOptimization',1 enables the verbose optimization mode.

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Verbose optimization flag, specified as the comma-separated pair consisting of 'DisplayOptimization', and the logical value true or false. Controls whether imregister displays optimization information in the command window during the registration process.

Data Types: logical

Starting geometric transformation, specified as the comma-separated pair consisting of 'InitialTransformation' and an affine2d or affine3d object.

Number of pyramid levels used during the registration process, specified as the comma-separated pair consisting of 'PyramidLevels' and a positive integer.

Example: 'PyramidLevels',4 sets the number of pyramid levels to 4.

Data Types: double

Output Arguments

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Transformed image, returned as a matrix. Any fill pixels introduced that do not correspond to locations in the original image are 0.

Spatial referencing information associated with output image, returned as an imref2d or imref3d object.

Tips

  • Both imregtform and imregister use the same underlying registration algorithm. imregister performs the additional step of resampling moving to produce the registered output image from the geometric transformation estimate calculated by imregtform. Use imregtform when you want access to the geometric transformation that relates moving to fixed. Use imregister when you want a registered output image.

  • Create an optimizer and metric with the imregconfig function before calling imregister. Getting good results from optimization-based image registration usually requires modifying optimizer or metric settings for the pair of images being registered. The imregconfig function provides a default configuration that should only be considered a starting point. For example, if you increase the number of iterations in the optimizer, reduce the optimizer step size, or change the number of samples in a stochastic metric, the registration improves to a point, at the expense of performance. See the output of imregconfig for more information on the different parameters that you can modify.

  • If the spatial scaling of your images differs by more than 10%, resize them with imresize before registering them.

  • Use imshowpair or imfuse to visualize the results of registration.

  • You can use imregister in an automated workflow to register several images.

  • When you have spatial referencing information about the image to be registered, specify the information to imregister using spatial referencing objects. This helps imregister converge to better results more quickly because scale differences can be taken into account.

Introduced in R2012a

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