GPU accelerated image processing for everyone

This reference contains all methods currently available in CLIJ, CLIJ2 and CLIJx for processing neighboring objects.. 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, Labels, Math, Matrices, Measurements, Neighbors, 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 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.

Applies a bilateral filter using a box neighborhood with sigma weights for space and intensity to the input image.

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.

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

Detects local maxima in a given square/cubic neighborhood.

Detects local maxima in a given square/cubic neighborhood.

Detects local maxima in a given square/cubic neighborhood.

Detects local maxima in a given square neighborhood of an input image stack.

Detects local minima in a given square/cubic neighborhood.

Detects local minima in a given square/cubic neighborhood.

Detects local minima in a given square/cubic neighborhood.

Detects local minima in a given square neighborhood of an input image stack.

Computes a binary image with pixel values 0 and 1 containing the binary dilation of a given input image.

Computes a binary image with pixel values 0 and 1 containing the binary dilation of a given input image.

Computes a binary image with pixel values 0 and 1 containing the binary dilation of a given input image.

Scales an image using given scaling factors for X and Y dimensions.

Scales an image using given scaling factors for X and Y dimensions.

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.

Computes a binary image with pixel values 0 and 1 containing the binary erosion of a given input image.

Computes a binary image with pixel values 0 and 1 containing the binary erosion of a given input image.

Computes a binary image with pixel values 0 and 1 containing the binary erosion of a given input image.

Replaces recursively all pixels of value a with value b if the pixels have a neighbor with value b.

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 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).

Applies the Laplace operator (Box neighborhood) to an image.

Applies the Laplace operator (Diamond neighborhood) to an 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 the local maximum of a pixels rectangular neighborhood.

Computes the local maximum of a pixels ellipsoidal neighborhood.

Computes the local maximum of a pixels cube neighborhood.

Computes the local maximum of a pixels spherical neighborhood.

Applies a maximum filter with kernel size 3x3 n times to an image iteratively.

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

Computes the local mean average of a pixels rectangular neighborhood.

Computes the local mean average of a pixels ellipsoidal neighborhood.

Computes the local mean average of a pixels cube neighborhood.

Computes the local mean average of a pixels spherical neighborhood.

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

Computes the local mean average of a pixels ellipsoidal 2D neighborhood in an image stack slice by slice.

Computes the local median of a pixels rectangular neighborhood.

Computes the local median of a pixels ellipsoidal neighborhood.

Computes the local median of a pixels cuboid neighborhood.

Computes the local median of a pixels spherical neighborhood.

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

Computes the local minimum of a pixels rectangular neighborhood.

Computes the local minimum of a pixels ellipsoidal neighborhood.

Computes the local minimum of a pixels cube neighborhood.

Computes the local minimum of a pixels spherical neighborhood.

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

Applies a minimum filter with kernel size 3x3 n times to an image iteratively.

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

Determines neighbors of neigbors from touch matrix and saves the result as a new touch matrix.

Applies a non-local means filter using a box neighborhood with a Gaussian weight specified with sigma to the input image.

For every pixel in source image 1, determine the pixel with the most similar intensity in the local neighborhood with a given radius in source image 2. Write the distance in X, Y and Z in the three corresponding destination images.

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

Applies a minimum filter with kernel size 3x3 n times to an image iteratively.

Applies a minimum filter with kernel size 3x3 n times to an image iteratively.

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