GPU accelerated image processing for everyone

This reference contains all methods currently available in CLIJ, CLIJ2 and CLIJx for performing measurements in 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.

Determines the average of the n closest points for every point in a distance matrix.

Determines the average of the n far off (most distant) points for every point in a distance matrix.

Takes a touch matrix and a distance matrix to determine the average distance of touching neighbors for every object.

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

Determines the bounding box of all non-zero pixels in a binary image.

Determines the center of mass of an image or image stack.

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.

Determines the number of all pixels in a given image which are not equal to 0.

Counts non-zero pixels in a sphere around every pixel.

Counts non-zero pixels in a sphere around every pixel slice by slice in a stack.

Counts non-zero voxels in a sphere around every voxel.

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

Generates a distance map from a binary image.

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.

Determines the local entropy in a box with a given radius around every pixel.

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 images containing coordinates and builds up a matrix containing distance between the points.

Takes an image and an intensity range to determine a grey value co-occurrence matrix.

Takes an image and assumes its grey values are integers. It builds up a grey-level co-occurrence matrix of neighboring (west, south-west, south, south-east, in 3D 9 pixels on the next plane) pixel intensities.

Takes an image and assumes its grey values are integers. It builds up a grey-level co-occurrence matrix of neighboring (left, bottom, back) pixel intensities.

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.

The automatic thresholder utilizes the threshold methods from ImageJ on a histogram determined on the GPU to determine a threshold value as similar as possible to ImageJ ‘Apply Threshold’ method.

Determines the bounding box of all non-zero pixels in a binary image.

Determines the center of mass of an image or image stack.

Reads out the size of an image [stack] and writes it to the variables ‘width’, ‘height’ and ‘depth’.

Reads out properties of the currently active GPU and write it in the variables ‘GPU_name’, ‘global_memory_in_bytes’ and ‘OpenCL_Version’.

Determines the overlap of two binary images using the Jaccard index.

Determines the maximum of all pixels in a given image.

Determines the mean of all pixels in a given image.

Determines the mean of all pixels in a given image which have non-zero value in a corresponding mask image.

Determines the minimum of all pixels in a given image.

Determines the overlap of two binary images using the Sorensen-Dice coefficent.

Determines the sum of all pixels in a given image.

Determines the histogram of a given image.

Determines the overlap of two binary images using the Jaccard index.

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.

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.

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.

Determines the maximum of all pixels in a given image.

Determines the maximum intensity in an image, but only in pixels which have non-zero values in another mask image.

Takes a touch matrix and a vector of values to determine the maximum value among touching neighbors for every object.

Determines the distance between pairs of closest spots in two binary images.

Determines the mean average of all pixels in a given image.

Determines the mean intensity in a masked image.

Determines the mean intensity in a threshleded image.

Takes a touch matrix and a vector of values to determine the mean value among touching neighbors for every object.

Takes a touch matrix and a vector of values to determine the median value among touching neighbors for every object.

Takes a touch matrix and a distance matrix to determine the shortest distance of touching neighbors for every object.

Determines the minimum of all pixels in a given image.

Determines the minimum intensity in a masked image.

Takes a touch matrix and a vector of values to determine the minimum value among touching neighbors for every object.

Determine the n point indices with shortest distance for all points in a distance matrix.

Determine the n point indices with shortest distance for all points in a distance matrix.

Converts an image into a table.

Determine the shortest distance from a distance matrix.

Determines the overlap of two binary images using the Sorensen-Dice coefficent.

Transforms a spots image as resulting from maximum/minimum detection in an image where every column contains d pixels (with d = dimensionality of the original image) with the coordinates of the maxima/minima.

Determines the standard deviation of all pixels in an image.

Determines the standard deviation of all pixels in an image which have non-zero value in a corresponding mask image.

Takes a touch matrix and a vector of values to determine the standard deviation value among touching neighbors for every object.

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 image size (bounding box), area (in pixels/voxels), min, max and mean intensity of all 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.

Sums all pixels slice by slice and returns them in an array.

Determines the sum of all pixels in a given image.

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

Determines the variance of all pixels in an image.

Determines the variance in an image, but only in pixels which have non-zero values in another binary mask image.