View source for Projects:RegistrationDocumentation:Evaluation

== Visualizing Registration Results ==
Below we discuss different forms of evaluating registration accuracy.  This is not a straightforward assessment, be it qualitative or quantitative, and the requirements for evaluating registration differ with the type of input data and the questions asked. 

=== Animated GIFs ===
Relatively small differences can be detected, albeit qualitatively, with a quick switching between the two images.
[[Image:RegLib01_GifAnim_reg.gif|200px|left|example animated gif of image pair after registration]] 

=== Color Composite ===
We feed the two images into the R,G or B channel of a true color image and thus create a true color image where misalignment appears in color. This may be prefereable to normal transparency because the different color channels permit a better contrast between the structures of both images in areas where they overlap.
[[Image:RegLib01_ColorAnim.gif|200px|left|example color composite image (also as animated gif), showing before/after registration. Areas of misalignment appear in color.]]

=== Checkerboard ===
Builds a "puzzle-piece" collage of blocks from both images, alternating between the two. Helpful if the two images are from different modality or have different contrast. Continuity of edges becomes very apparent on such images.
[[Image:Case10-FLIRT-axial.jpg|left|200px]]
[[Image:Case10-FLIRT-saggital.jpg|left|200px]]
[[Image:Case10-FLIRT-coronal.jpg|left|200px]]

=== Subtraction Images ===
For intra-subject and intra-modality images only. The pixel-by pixel difference of the registered image pair can provide valuable information about registration quality. This usually requires at least global intensity matching to compensate for overall brightness and contrast differences.
[[Image:RegLib01_Subtraction_reg.jpg|200px|left|example subtraction image. All unchanged and correctly registered anatomy should cancel in subtraction.]]
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=== Fiducial Pairs ===
To the extent that we can reliably identify anatomical landmarks in both images, we can use the residual distance of those landmarks after registration as a metric for registration accuracy. The most common problem with this approach is that (esp. for 3D images) the identification of landmarks is often already less than the registration accuracy we want to measure. This limitation is less of an issue when comparing different registrations to eachother.

=== Label Maps ===
If we have segmented structures in both images, we can use them to evaluate accuracy also. Often this is corroborated by true change, however, i.e. we face the difficult task of discriminating true anatomical change from residual misalignment.
*[http://wiki.slicer.org/slicerWiki/index.php/Modules:RegistrationMetrics-Documentation-3.5 Example Slicer Module on Registration Metrics: Hausdorff Distance]
[[Image:RegistrationMetricsScreenshot.png|200px|Screen shot of Registration Metrics module showing an axial slice with a color map representing alignment error between the two label map images]]