Translationtransformations preserve the size and orientation of the image. Each pixel in the image is displaced the same amount in the same direction.

Rigidtransformations include rotation and translation. Rigid transformations preserve length.

Similarity转换包括各向同性缩放、rotation, and translation. Similarity transformations preserve shape, but not size. When used with a featured-based registration technique, at least two matched pairs of points are required.

Affinetransformations include shear and all supported similarity transformations. Affine transformations preserve parallel lines, but not necessarily angles between lines or distances between points. When used with a featured-based registration technique, at least three matched pairs of points are required.

Projectivetransformations allow tilting in addition to all supported affine transformations. When used with a featured-based registration technique, at least four matched pairs of points are required.

Number of Detected Features. The transformation type determines the minimum number of matched features required to perform a registration. Similarity transformations require two or more matched features. Affine transformations require three or more matched features. Projective transformations require four or more matched features.

Quality of matched features. The quality value is a combination of matched features options.

Rotation. By default, feature-based registration allows the moving image to rotate. However, some imaging scenarios, such as stereoscopy, produce images with identical rotation. If your images have the same rotation, clearing this option can improve the accuracy of the registration.

Normalize. This option scales the pixel values of both images to the same dynamic range.

Apply Gaussian blur. Smoothing the images with a Gaussian blur can help the optimizer find the global maximum or minimum of the solution surface. However, smoothing changes the shape of the surface, and over-smoothing can shift the position of the extrema. Large amounts of blurring are useful when the images are severely misaligned at the start of the registration, to help the optimizer search the correct basin of attraction. Small amounts of blurring are useful when the images start with close alignment.

Align centers. This option provides an initial transformation that aligns the world coordinates of the centers of the two images. Thegeometricoption aligns the geometric centers, based on the spatial referencing information of the images. Thecenter of massoption aligns the centers of mass, calculated from the weighted mean of pixel intensities.

Number of iterations. This value is the number of iterations on each pyramid level.

Pyramid levels. The value represents the number of Gaussian pyramid reduction levels. The maximum number of pyramid levels depends on the size of each dimension in the images. For example, when the shortest dimension of the fixed and moving images is 256 pixels, at most eight pyramid levels can be used. For more information about pyramid reduction, seeimpyramid.

Smoothing. The value represents the standard deviation of Gaussian smoothing and remains the same at each pyramid level. Values are in the range [0.5, 3]. Larger values result in smoother output displacement fields. Smaller values result in more localized deformation in the output displacement field.