Affine (object centroids)

Apply slice-by-slice (2D) affine-based image registration to a multi-dimensional stack.

Description

Apply slice-by-slice (2D) affine-based image registration to a multi-dimensional stack. Images can be aligned relative to the first frame in the stack, the previous frame or a separate image in the workspace. The registration transform can also be calculated from a separate stack to the one that it will be applied to. Registration can be performed along either the time or Z axes. The non-registered axis (e.g. time axis when registering in Z) can be "linked" (all frames given the same registration) or "independent" (each stack registered separately).

This module uses centroids of previously-detected objects as the reference points for image alignment

Parameters

Parameter	Description
Input image	Image from workspace to apply registration to.
Apply to input image	When selected, the post-operation image will overwrite the input image in the workspace. Otherwise, the image will be saved to the workspace with the name specified by the "Output image" parameter.
Output image	If "Apply to input image" is not selected, the post-operation image will be saved to the workspace with this name.
Registration axis	Controls which stack axis the registration will be applied in. For example, when "Time" is selected, all images along the time axis will be aligned. Choices are: Time, Z.
Other axis mode	For stacks with non-registration axis lengths longer than 1 (e.g. the "Z" axis when registering in time) the behaviour of this other axis is controlled by this parameter: "Independent" Each non-registration axis is registered independently. For example, applying separate Z-registrations for each timepoint of a 4D stack. "Linked" All elements of the non-registration axis are registered with a single transform. For example, applying the same registration at a timepoint to all slices of a 4D stack.
Fill mode	Controls what intensity any border pixels will have. "Borders" in this case correspond to strips/wedges at the image edge corresponding to regions outside the initial image (e.g. the right-side of an output image when the input was translated to the left). Choices are: Black, White.
Show detected points	When enabled, the points used for calculation of the registration will be added as an overlay to the input image and displayed.
Enable multithreading	When selected, certain parts of the registration process will be run on multiple threads of the CPU. This can provide a speed improvement when working on a computer with a multi-core CPU.
Reference mode	Controls what reference image each image will be compared to: "First frame" All images will be compared to the first frame (or slice when in Z-axis mode). For image sequences which continuously evolve over time (e.g. cells dividing) this can lead to reduced likelihood of successfully calculating the transform over time. "Previous N frames" Each image will be compared to the N frames (or slice when in Z-axis mode) immediately before it (number of frames specified by "Number of previous frames"). These reference frames are consolidated into a single reference image using a projection based on the statistic specified by "Previous frames statistic". This mode copes better with image sequences which continuously evolve over time, but can also lead to compounding errors over time (errors in registration get propagated to all remaining slices). "Specific image" All images will be compared to a separate 2D image from the workspace. The image to compare to is selected using the "Reference image" parameter.
Number of previous frames	Number of previous frames (or slices) to use as reference image when "Reference mode" is set to "Previous N frames". If there are insufficient previous frames (e.g. towards the beginning of the stack) the maximum available frames will be used. Irrespective of the number of frames used, the images will be projected into a single reference image using the statistic specified by "Previous frames statistic".
Previous frames statistic	Statistic to use when combining multiple previous frames as a reference ("Reference mode" set to "Previous N frames").
Reference image	If "Reference mode" is set to "Specific image" mode, all input images will be registered relative to this image. This image must only have a single channel, slice and timepoint.
Calculation source	Controls whether the input image will be used to calculate the registration transform or whether it will be determined from a separate image: "External" The transform is calculated from a separate image from the workspace (specified using "External source"). This could be an image with enhanced contrast (to enable better feature extraction), but where the enhancements are not desired in the output registered image. When "Other axis mode" is set to "Linked", the external image must be the same length along the registration axis and have single-valued length along the non-registration axis. However, when set to "Independent", the external image must have the same axis lengths for both the registration and non-registration axes. "Internal" The transform is calculated from the input image.
External source	If "Calculation source" is set to "External", registration transforms will be calculated using this image from the workspace. This image will be unaffected by the process.
Calculation channel	If calculating the registration transform from a multi-channel image stack, the transform will be determined from this channel only. Irrespectively, for multi-channel image stacks, the calculated transform will be applied equally to all channels.
Transformation mode	Controls the type of registration being applied: "Affine (trans., rot., scale, shear)" Applies the full affine transformation, whereby the input image can undergo translation, rotation, reflection, scaling and shear. "Rigid (trans., rot.)" Applies only translation and rotation to the input image. As such, all features should remain the same size. "Similarity (trans., rot., iso-scale)" Applies translation, rotating and linear scaling to the input image. "Translation" Applies only translation (motion within the 2D plane) to the input image.
Test flip (mirror image)	When selected, alignment will be tested for both the "normal" and "flipped" (mirror) states of the image. The state yielding the lower cost to alignment will be retained.
Independent rotation	When selected, the image will be rotated multiple times, with registration optimised at each orientation. The orientation with the best score will be retained. This is useful for algorithms which perform poorly with rotated features (e.g. block matching). The increment between rotations is controlled by "Orientation increment (degs)".
Orientation increment (degs)	If "Independent rotation" is enabled, this is the angular increment between rotations. The increment is specified in degree units.
Show transformation(s)	When selected, the affine transform will be displayed in the results table. Fixed affine transform values such as these can be applied using the "Affine (fixed transform)" module.
Clear between images	If "Show transformation(s)" is enabled, this parameter can be used to reset the displayed affine transform in the results table. If this option isn't selected, the new transform will be added to the bottom of the results table.
Input objects	Centroids for these objects will be used as the references for image alignment.
Maximum separation (px)	Maximum spatial separation between object centroids for them to be linked and used in the alignment.
Maximal alignment error (px)	"Matching local descriptors gives many false positives, but true positives are consistent with respect to a common transformation while false positives are not. This consistent set and the underlying transformation are identified using RANSAC. This value is the maximal allowed transfer error of a match to be counted as a good one. Tip: Set this to about 10% of the image size.". Description taken from https://imagej.net/Feature_Extraction
Inlier ratio	"The ratio of the number of true matches to the number of all matches including both true and false used by RANSAC. 0.05 means that minimally 5% of all matches are expected to be good while 0.9 requires that 90% of the matches were good. Only transformations with this minimal ratio of true consent matches are accepted. Tip: Do not go below 0.05 (and only if 5% is more than about 7 matches) except with a very small maximal alignment error to avoid wrong solutions.". Description taken from https://imagej.net/Feature_Extraction