GPU accelerated image processing for everyone

This reference contains all methods currently available in CLIJ, CLIJ2 and CLIJx for processing labelled images.. Read more about CLIJs release cycle

**Please note:** CLIJ is deprecated. Make the transition to CLIJ2.

Method is available in CLIJ (deprecated release)

Method is available in CLIJ2 (stable release)

Method is available in CLIJx (experimental release)

**Categories:** Binary, Filter, Graphs, Labels, Math, Matrices, Measurements, Projections, Transformations

[A], B,[C],[D],[E],[F],[G], H, I, J, K,[L],[M], N, O,[P], Q, R,[S],[T], U,[V], W, X, Y, Z

Takes a label map, determines distances between all centroids and replaces every label with the average distance to the n closest neighboring labels.

Takes a label map, determines which labels touch and replaces every label with the average distance to their neighboring labels.

Determines the centroids of the background and all labels in a label image or image stack.

Determines the centroids of all labels in a label image or image stack.

Analyses a label map and if there are gaps in the indexing (e.g. label 5 is not present) all subsequent labels will be relabelled.

Performs connected components analysis to a binary image and generates a label map.

Performs connected components analysis inspecting the box neighborhood of every pixel to a binary image and generates a label map.

Performs connected components analysis inspecting the diamond neighborhood of every pixel to a binary image and generates a label map.

Performs connected components analysis to a binary image and generates a label map.

Takes a touch matrix as input and delivers a vector with number of touching neighbors per label as a vector.

Determines maximum regions in a Gaussian blurred version of the original image.

Takes a labelmap and returns an image where all pixels on label edges are set to 1 and all other pixels to 0.

Starting from a label map, draw lines between touching neighbors resulting in a mesh.

Starting from a label map, draw lines between touching neighbors resulting in a mesh.

Starting from a label map, draw lines between touching neighbors resulting in a mesh.

This operation removes labels from a labelmap and renumbers the remaining labels.

Removes all labels from a label map which touch the edges of the image (in X, Y and Z if the image is 3D).

This operation follows a ray from a given position towards a label (or opposite direction) and checks if there is another label between the label an the image border.

Removes labels from a label map which are not within a certain size range.

This operation follows a ray from a given position towards a label (or opposite direction) and checks if there is another label between the label an the image border.

This operation removes labels from a labelmap and renumbers the remaining labels.

This operation removes labels from a labelmap and renumbers the remaining labels.

Takes a label map image and dilates the regions using a octagon shape until they touch.

Extend labels with a given radius.

Determine maxima with a given tolerance to surrounding maxima and background and label them.

Finds and labels local maxima with neighboring maxima and background above a given tolerance threshold.

Takes two labelmaps with n and m labels and generates a (n+1)*(m+1) matrix where all pixels are set to 0 exept those where labels overlap between the label maps.

Takes two labelmaps with n and m labels_2 and generates a (n+1)*(m+1) matrix where all labels_1 are set to 0 exept those where labels_2 overlap between the label maps.

Take a labelmap and a vector of values to replace label 1 with the 1st value in the vector.

Take a labelmap and a column from the results table to replace label 1 with the 1st value in the vector.

Takes a label map with n labels and generates a (n+1)*(n+1) matrix where all pixels are set the number of pixels where labels touch (diamond neighborhood).

Takes a labelmap with n labels and generates a (n+1)*(n+1) matrix where all pixels are set to 0 exept those where labels are touching.

Inspired by Grayscale attribute filtering from MorpholibJ library by David Legland & Ignacio Arganda-Carreras. This plugin will remove components in a grayscale image based on user-specified area (for 2D: pixels) or volume (3D: voxels). For each gray level specified in the number of bins, binary images will be generated, followed by exclusion of objects (labels) below a minimum pixel count. All the binary images for each gray level are combined to form the final image. The output is a grayscale image, where bright objects below pixel count are removed. It is recommended that low values be used for number of bins, especially for large 3D images, or it may take long time.

Takes a label map, determines for every label the maximum distance of any pixel to the centroid and replaces every label with the that number.

Takes a label map, determines for every label the maximum distance of any pixel to the centroid and replaces every label with the that number.

Takes an image and a corresponding label map, determines the mean intensity per label and replaces every label with the that number.

Takes a label map, determines the number of pixels per label and replaces every label with the that number.

Transforms a binary image with single pixles set to 1 to a labelled spots image.

Takes an image and a corresponding label map, determines the standard deviation of the intensity per label and replaces every label with the that number.

Masks a single label in a label map.

Takes a labelled image and dilates the labels using a octagon shape until they touch.

Generates a coordinate list of points in a labelled spot image.

Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point and replaces every label with the maximum distance of touching labels.

Takes a label map, determines which labels touch, the distance between their centroids and the maximum distancebetween touching neighbors. It then replaces every label with the that value.

Takes a label map, determines which labels touch, determines for every label with the number of touching neighboring labels and replaces the label index with the local maximum of this count.

Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point and replaces every label with the mean distance of touching labels.

Takes a label map, determines which labels touch, the distance between their centroids and the mean distancebetween touching neighbors. It then replaces every label with the that value.

Takes a label map, determines which labels touch and how much, relatively taking the whole outline of each label into account, and determines for every label with the mean of this value and replaces the label index with that value.

Takes a label map, determines which labels touch, determines for every label with the number of touching neighboring labels and replaces the label index with the local mean of this count.

Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point and replaces every label with the median distance of touching labels.

Takes a label map, determines which labels touch, the distance between their centroids and the median distancebetween touching neighbors. It then replaces every label with the that value.

Takes a label map, determines which labels touch, determines for every label with the number of touching neighboring labels and replaces the label index with the local median of this count.

Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point and replaces every label with the minimum distance of touching labels.

Takes a label map, determines which labels touch, the distance between their centroids and the minimum distancebetween touching neighbors. It then replaces every label with the that value.

Takes a label map, determines which labels touch, determines for every label with the number of touching neighboring labels and replaces the label index with the local minimum of this count.

Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point and replaces every label with the standard deviation distance of touching labels.

Takes a label map, determines which labels touch, the distance between their centroids and the standard deviation distancebetween touching neighbors. It then replaces every label with the that value.

Takes a label map, determines which labels touch, determines for every label with the number of touching neighboring labels and replaces the label index with the local standard deviation of this count.

Computes a masked image by applying a label mask to an image.

Takes a pointlist with dimensions n times d with n point coordinates in d dimensions and labels corresponding pixels.

Pulls all labels in a label map as ROIs to a list.

Pulls all labels in a label map as ROIs to the ROI manager.

Takes a label map (seeds) and an input image with gray values to apply the watershed algorithm and split the image above a given threshold in labels.

Determines bounding box, area (in pixels/voxels), min, max and mean intensity of background and labelled objects in a label map and corresponding pixels in the original image.

Determines bounding box, area (in pixels/voxels), min, max and mean intensity of labelled objects in a label map and corresponding pixels in the original image.

Takes a label map, determines which labels touch and replaces every label with the number of touching neighboring labels.

Trains a Weka model using functionality of Fijis Trainable Weka Segmentation plugin.

Trains a Weka model using functionality of Fijis Trainable Weka Segmentation plugin.

Takes a binary image, labels connected components and dilates the regions using a octagon shape until they touch.