GPU accelerated image processing for everyone

This reference contains all methods currently available in CLIJ, CLIJ2 and CLIJx. 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)

Method is available in clEsperanto (experimental)

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

[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]

Computes the absolute value of every individual pixel x in a given image.

Determines the absolute difference pixel by pixel between two images.

Computes the absolute value of every individual pixel x in a given image.

Adds a scalar value s to all pixels x of a given image X.

Calculates the sum of pairs of pixels x and y of two images X and Y.

Calculates the sum of pairs of pixels x and y from images X and Y weighted with factors a and b.

Converts a adjacency matrix in a touch matrix.

Applies an affine transform to a 3D image.

Applies an affine transform to a 2D image.

Applies an affine transform to a 3D image.

Deforms an image according to distances provided in the given vector images.

Deforms an image stack according to distances provided in the given vector image stacks.

Deforms an image according to distances provided in the given vector images.

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

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

Determines the maximum projection of an image stack along Z.

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

The automatic thresholder utilizes the threshold methods from ImageJ on a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method. Enter one of these methods in the method text field: [Default, Huang, Intermodes, IsoData, IJ_IsoData, Li, MaxEntropy, Mean, MinError, Minimum, Moments, Otsu, Percentile, RenyiEntropy, Shanbhag, Triangle, Yen]

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.

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

Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of pixels x and y with the binary AND operator &. All pixel values except 0 in the input images are interpreted as 1.

Determines pixels/voxels which are on the surface of binary objects and sets only them to 1 in the destination image. All other pixels are set to 0.

Fills holes (pixels with value 0 surrounded by pixels with value 1) in a binary image.

Fills holes (pixels with value 0 surrounded by pixels with value 1) in a binary image stack slice by slice.

Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of pixels x and y with the binary intersection operator &. All pixel values except 0 in the input images are interpreted as 1.

Computes a binary image (containing pixel values 0 and 1) from an image X by negating its pixel values x using the binary NOT operator !

Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of pixels x and y with the binary OR operator |.

Subtracts one binary image from another.

Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of pixels x and y with the binary union operator |.

Applies a pre-trained CLIJx-Weka model to a 2D image.

Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of pixels x and y with the binary operators AND &, OR | and NOT ! implementing the XOR operator.

Computes the Gaussian blurred image of an image given two sigma values in X and Y.

Computes the Gaussian blurred image of an image given two sigma values in X, Y and Z.

Computes the Gaussian blurred image of an image given two sigma values in X and Y. Thus, the filterkernel can have non-isotropic shape.

Apply a bottom-hat filter for background subtraction to the input image.

Applies a bottom-hat filter for background subtraction to the input image.

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

Acquires an image (in fact an RGB image stack with three slices) of given size using a webcam.

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.

Outputs information about available OpenCL devices.

Resets the GPUs memory by deleting all cached images.

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.

Apply a binary closing to the input image by calling n dilations and n erosions subsequenntly.

Apply a binary closing to the input image by calling n dilations and n erosions subsequently.

Computes the color deconvolution of an 8bit RGB stack color image with a given 3x3 matrix of color vectors. Note: The input image has to be a stack with three z-slices corresponding to the red, green and blue channel.)

Combines two images or stacks in X.

Combines two images or stacks in Y.

Concatenates two stacks in Z.

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.

Convert the input image to a float image with 32 bits per pixel.

Converts a three channel image (stack with three slices) to a single channel image (2D image) by multiplying with factors 0.299, 0.587, 0.114.

Convert image to 32-bit float pixel type.

Convert image to 8-bit unsigned byte pixel type.

Convert image to 16-bit unsigned short pixel type.

Convert the input image to a unsigned integer image with 16 bits per pixel.

Convert the input image to a unsigned integer image with 8 bits per pixel.

Convolve the image with a given kernel image.

Copies an image.

This method has two purposes: It copies a 2D image to a given slice z position in a 3D image stack or It copies a given slice at position z in an image stack to a 2D image.

Computes the cosinus of all pixels value x.

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.

Allocated memory for a new 2D image in the GPU memory.

Allocated memory for a new 3D image in the GPU memory.

Crops a given rectangle out of a given image.

Crops a given sub-stack out of a given image stack.

Performs cross correlation analysis between two images.

Executes a custom operation wirtten in OpenCL on a custom list of images.

Applies a cylinder transform to an image stack assuming the center line goes in Y direction in the center of the stack.

Applies particle image velocimetry to two images and registers them afterwards by warping input image 2 with a smoothed vector field.

Determines a maximum projection of an image stack and does a color coding of the determined arg Z (position of the found maximum).

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.

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.

Applies Gaussian blur to the input image twice with different sigma values resulting in two images which are then subtracted from each other.

Applies Gaussian blur to the input image twice with different sigma values resulting in two images which are then subtracted from each other.

Applies Gaussian blur to the input image twice with different sigma values resulting in two images which are then subtracted from each other.

Determines the absolute difference between two images and sets all pixels to 1 where it is above a given tolerance, and 0 otherwise.

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.

Generates a distance map from a binary image.

Generates a mesh from a distance matric and a list of point coordinates.

Applies Gaussian blur to the input image and divides the original by the result.

Divides two images X and Y by each other pixel wise.

Divides a scalar s by image X pixel wise.

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

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

Scales an image using scaling factors 0.5 for X and Y dimensions. The Z dimension stays untouched.

Draws a box at a given start point with given size. All pixels other than in the box are untouched. Consider using `set(buffer, 0);`

in advance.

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

Draws a line between two points with a given thickness.

Starting from a label map, draw lines between n closest labels for each label resulting in a mesh.

Starting from a label map, draw lines between labels that are closer than a given distance resulting in a mesh.

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

Draws a sphere around a given point with given radii in x, y and z (if 3D).

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

Draws a line between two points with a given thickness.

Determines the centerOfMass of the image stack and translates it so that it stays in a defined position.

Threshold the image stack, determines the centroid of the resulting binary image and translates the image stack so that its centroid sits in a defined position.

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

Determines if two images A and B equal pixel wise.

Determines if an image A and a constant b are equal.

Determines correction factors for each z-slice so that the average intensity in all slices can be made the same and multiplies these factors with the slices.

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.

Takes a label map, determines the centroids of all labels and writes the distance of all labelled pixels to their centroid in the result image. Background pixels stay zero.

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.

Computes base exponential of all pixels values.

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

Extend labels with a given radius.

Extended depth of focus projection maximizing intensity in the local sobel image.

Extended depth of focus projection maximizing local pixel intensity variance.

Returns an image with pixel values most distant from 0:

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.

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

Flips an image in X and/or Y direction depending on if flip_x and/or flip_y are set to true or false.

Flips an image in X, Y and/or Z direction depending on if flip_x, flip_y and/or flip_z are set to true or false.

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

Applies a gamma correction to an image.

Gauss Jordan elimination algorithm for solving linear equation systems.

Computes the Gaussian blurred image of an image given two sigma values in X and Y.

Computes the Gaussian blurred image of an image given two sigma values in X, Y and Z.

Computes the angle in radians between all point coordinates given in two point lists.

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.

Computes the distance between all point coordinates given in two point lists.

Computes the distance in X, Y or Z (specified with parameter axis) between all point coordinates given in two point lists.

Generates a feature stack for Trainable Weka Segmentation.

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.

Generates a feature image for Trainable Weka Segmentation.

Produces a touch-matrix where the n nearest neighbors are marked as touching neighbors.

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.

Produces a touch-matrix where the neighbors within a given distance range are marked as touching neighbors.

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.

Reads out the size of an image [stack] and writes it to the results table in the columns ‘Width’, ‘Height’ and ‘Depth’.

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

Determines the sum of all pixels in a given image.

Computes the gradient of gray values along X.

Computes the gradient of gray values along Y.

Computes the gradient of gray values along Z.

Determines if two images A and B greater pixel wise.

Determines if two images A and B greater pixel wise.

Determines if two images A and B greater or equal pixel wise.

Determines if two images A and B greater or equal pixel wise.

Inspired by Grayscale attribute filtering from MorpholibJ library by David Legland & Ignacio Arganda-Carreras.

Determines the histogram of a given image.

Converts an image into a table.

Apply ImageJ2 / ImageJ Ops Frangi Vesselness filter to an image.

Apply ImageJ2 / ImageJ Ops Gaussian Blur to an image.

Apply ImageJ2 / ImageJ Ops Median filter with a Sphere shape to an image.

Apply ImageJ2 / ImageJ Ops Median filter with a Sphere shape to an image.

Apply ImageJ2 / ImageJ Ops Richardson Lucy Deconvolution to an image.

Apply ImageJ2 / ImageJ Ops Tubeness filter to an image.

Apply ImageJ 3D Suite Connected Components Labeling (Segment 3D) to an image in 3D.

Apply ImageJs Watershed algorithm to a binary image.

Apply ImageJs Gaussian Blur to an image.

Apply ImageJs Variance filter to an image.

Apply ImageJs Watershed algorithm to a binary image.

Copies a single slice into a stack a given number of times.

Apply imglib2 ConnectedComponents (8-connected) to an image to create a label map.

Determines the mean intensity of the image stack and multiplies it with a factor so that the mean intensity becomes equal to a given value.

Determines the mean intensity of all pixel the image stack which are above a determined Threshold (Otsu et al. 1979) and multiplies it with a factor so that the mean intensity becomes equal to a given value.

Invalidates all cached OpenCL programs and kernels.

Computes the negative value of all pixels in a given image.

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

Applies K-Means clustering to an image and a corresponding label map.

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 a label map, determines for every label the mean 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 an image and a corresponding label map, determines the mean intensity in the laplacian of the image per label and replaces every label with the that number.

Takes two label maps, and counts for every label in label map 1 how many labels overlap with it in label map 2.

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

Takes two label maps, and counts for every label in label map 1 how many labels are in a given distance range to it in label map 2.

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.

Takes a label map and excludes all labels which are not on the surface.

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.

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

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

Applies a local minimum and maximum filter.

local id

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 binary image with pixel values 0 and 1 depending on if a pixel value x in image X was above of equal to the pixel value m in mask image M.

Computes the local threshold based on Auto Local Threshold (Bernsen method) see: https://imagej.net/Auto_Local_Threshold see code in: https://github.com/fiji/Auto_Local_Threshold/blob/master/src/main/java/fiji/threshold/Auto_Local_Threshold.java Formular: <pre>if (tcontrast > max - min){ if ((max + min)/2.0 >= 128) res = 0} else if (val > (max + min)/2.0) res =0</pre>

Computes the local threshold based on Auto Local Threshold (Contrast method) see: https://imagej.net/Auto_Local_Threshold see code in: https://github.com/fiji/Auto_Local_Threshold/blob/master/src/main/java/fiji/threshold/Auto_Local_Threshold.java Formular: <pre>if (abs(value - min) >= abs(max - value) && (value != 0)) value = 0 </pre>

Computes the local threshold based on Auto Local Threshold (Mean method) see: https://imagej.net/Auto_Local_Threshold see code in: https://github.com/fiji/Auto_Local_Threshold/blob/master/src/main/java/fiji/threshold/Auto_Local_Threshold.java Formular: <pre>if(value > (mean - c_value)) value = 0 </pre>

Computes the local threshold based on Auto Local Threshold (Median method) see: https://imagej.net/Auto_Local_Threshold see code in: https://github.com/fiji/Auto_Local_Threshold/blob/master/src/main/java/fiji/threshold/Auto_Local_Threshold.java Formular: <pre>if(value > (median - c_value)) value = 0 </pre>

Computes the local threshold based on Auto Local Threshold (MidGrey method) see: https://imagej.net/Auto_Local_Threshold see code in: https://github.com/fiji/Auto_Local_Threshold/blob/master/src/main/java/fiji/threshold/Auto_Local_Threshold.java Formular: <pre>if (value > ( (max + min)/2.0 - c_value) ) value = 0 </pre>

Computes the local threshold based on Auto Local Threshold (Niblack method) see: https://imagej.net/Auto_Local_Threshold see code in: https://github.com/fiji/Auto_Local_Threshold/blob/master/src/main/java/fiji/threshold/Auto_Local_Threshold.java Formular: <pre>t = mean + k_value * sqrt(var - c_value) </pre>

Computes the local threshold (Fast version) based on Auto Local Threshold (Phansalkar method) see: https://imagej.net/Auto_Local_Threshold see code in: https://github.com/fiji/Auto_Local_Threshold/blob/c955dc18cff58ac61df82f3f001799f7ffaec5cb/src/main/java/fiji/threshold/Auto_Local_Threshold.java#L636 Formulary: <pre>t = mean * (1 + p * exp(-q * mean) + k * ((stdev / r) - 1))</pre>

Computes the local threshold based on Auto Local Threshold (Sauvola method) see: https://imagej.net/Auto_Local_Threshold see code in: https://github.com/fiji/Auto_Local_Threshold/blob/master/src/main/java/fiji/threshold/Auto_Local_Threshold.java Formular: <pre>t = mean * (1.0 + k_value * (stddev / r_value - 1.0)) </pre>

Computes base e logarithm of all pixels values.

Applies a scaling operation using linear interpolation to generate an image stack with a given isotropic voxel size.

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

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

Computes a masked image by applying a binary 2D mask to an image stack.

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

Checks if all elements of a matrix are different by less than or equal to a given tolerance.

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.

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

Computes the maximum of a constant scalar s and each pixel value x in a given image X.

Computes the maximum of a pair of pixel values x, y from two given images X and Y.

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

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

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 label image and a parametric intensity image and will replace each labels value in the parametric image by the maximum value of neighboring labels. The distance number of nearest neighbors can be configured. Note: Values of all pixels in a label each must be identical.

Takes a label image and a parametric intensity image and will replace each labels value in the parametric image by the maximum value of neighboring labels.

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

Takes a label image and a parametric intensity image and will replace each labels value in the parametric image by the maximum value of neighboring labels. The radius of the neighborhood can be configured: * radius 0: Nothing is replaced * radius 1: direct neighbors are taken into account * radius 2: neighbors and neighbors or neighbors are taken into account * radius n: …

Determines the maximum intensity projection of an image along X.

Determines the maximum intensity projection of an image along X.

Determines the maximum intensity projection of an image along Z.

Determines the maximum intensity projection of an image along Z within a given z range.

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.

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.

Takes a label image and a parametric intensity image and will replace each labels value in the parametric image by the minimum value of neighboring labels. The distance number of nearest neighbors can be configured. Note: Values of all pixels in a label each must be identical.

Determines the mean intensity in a threshleded image.

Takes a label image and a parametric intensity image and will replace each labels value in the parametric image by the mean average value of neighboring labels.

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

Takes a label image and a parametric intensity image and will replace each labels value in the parametric image by the mean average value of neighboring labels. The radius of the neighborhood can be configured: * radius 0: Nothing is replaced * radius 1: direct neighbors are taken into account * radius 2: neighbors and neighbors or neighbors are taken into account * radius n: …

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

Determines the mean squared error (MSE) between two images.

Determines the mean average intensity projection of an image along X.

Determines the mean average intensity projection of an image along Y.

Determines the mean average intensity projection of an image along Z.

Determines the mean average intensity projection of an image along Z but only for pixels above a given threshold.

Determines the mean average intensity projection of an image along Z but only for pixels below a given threshold.

Determines the mean average intensity projection of an image along Z within a given z range.

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.

Starts from a label map, determines median touch portion to neighbors (between 0 and 1) and draws a map.

Determines the median intensity projection of an image stack along Z.

Determines the median intensity projection of an image stack along Z where pixels in a corresponding mask image are unequal to zero.

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.

Computes the minimum of a constant scalar s and each pixel value x in a given image X.

Computes the minimum of a pair of pixel values x, y from two given images X and Y.

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

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

Determines the minimum of all pixels in a given image.

Determines the minimum intensity in a masked image.

Takes a label image and a parametric intensity image and will replace each labels value in the parametric image by the minimum value of neighboring labels. The distance number of nearest neighbors can be configured. Note: Values of all pixels in a label each must be identical.

Takes a label image and a parametric intensity image and will replace each labels value in the parametric image by the minimum value of neighboring labels.

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

Takes a label image and a parametric intensity image and will replace each labels value in the parametric image by the minimum value of neighboring labels. The radius of the neighborhood can be configured: * radius 0: Nothing is replaced * radius 1: direct neighbors are taken into account * radius 2: neighbors and neighbors or neighbors are taken into account * radius n: …

Determines the minimum intensity projection of an image along Y.

Determines the minimum intensity projection of an image along Y.

Determines the minimum intensity projection of an image along Z.

Determines the minimum intensity projection of an image along Z within a given z range.

Determines the minimum intensity projection of all pixels in an image above a given threshold along Z within a given z range.

Takes a label image and a parametric intensity image and will replace each labels value in the parametric image by the mode value of neighboring labels. The distance number of nearest neighbors can be configured. Note: Values of all pixels in a label each must be identical.

Takes a label image and a parametric intensity image and will replace each labels value in the parametric image by the most popular value of neighboring labels.

Takes a touch matrix and a vector of values to determine the most popular integer value among touching neighbors for every object. TODO: This only works for values between 0 and 255 for now.

Takes a label image and a parametric intensity image and will replace each labels value in the parametric image by the most popular value of neighboring labels. The radius of the neighborhood can be configured: * radius 0: Nothing is replaced * radius 1: direct neighbors are taken into account * radius 2: neighbors and neighbors or neighbors are taken into account * radius n: …

Apply MorpholibJ ChamferDistanceMap to an image.

Apply MorpholibJs Classic Watershed to an image.

Apply MorpholibJ’s Extended Maxima to an image to produce an image where maxima regions are set to 255 and background to 0.

Apply MorpholibJ’s Extended Minima to an image to produce an image where minima regions are set to 255 and background to 0.

Apply MorpholibJ Fill Holes (Binary/Gray) to an image.

Apply MorpholibJ FloodFillComponentsLabeling3D to an image.

Apply MorpholibJ Keep Largest Region to a binary image.

Apply MorpholibJs Marker-controlled Watershed to an image.

Apply MorpholibJ Morphological Segmentation to an object image to produce a label image.

Apply MorpholibJ Morphological Segmentation to an object image to produce a label image.

Apply MorpholibJ Remove Border Labels to a label image.

Multiplies all pixel intensities with the x, y or z coordinate, depending on specified dimension.

Multiplies all pixels value x in a given image X with a constant scalar s.

Multiplies all pixels value x in a given image X with a constant scalar s from a list of scalars.

Multiplies all pairs of pixel values x and y from two images X and Y.

Multiplies two matrices with each other.

Multiplies all pairs of pixel values x and y from an image stack X and a 2D image Y.

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.

Starts from a label map, determines touch portion to neighbors, counts those above a given value (between 0 and 1) and draws a map.

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

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.

Apply a maximum filter (box shape) to the input image.

Apply a maximum filter (diamond shape) to the input image.

Apply a minimum filter (box shape) to the input image.

Apply a minimum filter (diamond shape) to the input image.

Determines if two images A and B equal pixel wise.

Determines if two images A and B equal pixel wise.

Apply a local maximum filter to an image which only overwrites pixels with value 0.

Apply a local maximum filter to an image which only overwrites pixels with value 0.

Apply a binary opening to the input image by calling n erosions and n dilations subsequenntly.

Apply a binary opening to the input image by calling n erosions and n dilations subsequenntly.

Organises windows on screen.

Sets all pixels to 1 if their intensity lies out of a given range, and 0 otherwise.

Apply a binary watershed to a binary image and introduce black pixels between objects.

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.

Run particle image velocimetry on a 2D+t timelapse.

Pastes an image into another image at a given position.

Pastes an image into another image at a given position.

Meshes all points in a given point list which are indiced in a corresponding index list.

Takes a list of point indices to generate a touch matrix (a.k.a. adjacency graph matrix) out of it.

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

Takes meta data from a stack and assigns it to the current image. The stack implements the Last-In-First-Out (LIFO) principle.

Computes all pixels value x to the power of a given exponent a.

Calculates x to the power of y pixel wise of two images X and Y.

This plugin takes two image filenames and loads them into RAM. The first image is returned immediately, the second image is loaded in the background and will be returned when the plugin is called again.

Determines the extrema of pixel values:

Visualises an image on standard out (console).

Copies an image specified by its name from GPU memory back to ImageJ and shows it.

Pulls a binary image from the GPU memory and puts it on the currently active ImageJ window as region of interest.

Copies a binary image specified by its name from GPU memory back to ImageJ and shows it. This binary image will have 0 and 255 pixel intensities as needed for ImageJ to interpret it as binary.

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.

Writes an image into a string.

Pushes a tile in an image specified by its name, position and size from GPU memory.

Converts an image into a table.

Copies the content of a vector image to a column in the results table. You can configure if new rows should be appended or if existing values should be overwritten.

Copies an image specified by its name to GPU memory in order to process it there later.

Converts an array to an image.

Copies an image specified by its name to GPU memory in order to process it there later.

Copies an image specified by its name to GPU memory in order to process it there later.

Copies an image specified by its name to GPU memory in order to process it there later.

Copies an image specified by its name to GPU memory in order to process it there later.

Stores meta data in a stack. The stack implements the Last-In-First-Out (LIFO) principle.

Converts a table to an image.

Converts a table column to an image.

Converts an string to an image.

Push a tile in an image specified by its name, position and size to GPU memory in order to process it there later.

Read an image from disc.

Takes a label image and an parametric image and reads parametric values from the labels positions.

Reads a raw file from disc and pushes it immediately to the GPU.

Takes a label image and an parametric image and reads parametric values from the labels positions.

Takes a pointlist and a parametric image and reads parametric values from the positions.

Takes a label map and reduces all labels to their edges. Label IDs stay the same and background will be zero.

Takes a label map and reduces all labels to their center spots. Label IDs stay and background will be zero.

Reduces the number of slices in a stack by a given factor. With the offset you have control which slices stay: * With factor 3 and offset 0, slices 0, 3, 6,… are kept. * With factor 4 and offset 1, slices 1, 5, 9,… are kept.

Frees memory of a specified image in GPU memory.

Replaces integer intensities specified in a vector image.

Replaces a specific intensity in an image with a given new value.

Replaces pixel values x with y in case x is zero.

Prints a list of all images cached in the GPU to ImageJs log window together with a sum of memory consumption.

Resamples an image with given size factors using an affine transform.

Resets the meta data stack.

Flippes Y and Z axis of an image stack. This operation is similar to ImageJs ‘Reslice [/]’ method but offers less flexibility such as interpolation.

Flippes X, Y and Z axis of an image stack. This operation is similar to ImageJs ‘Reslice [/]’ method but offers less flexibility such as interpolation.

Computes a radial projection of an image stack.

Computes a radial projection of an image stack and reslices it from top.

Flippes X, Y and Z axis of an image stack. This operation is similar to ImageJs ‘Reslice [/]’ method but offers less flexibility such as interpolation.

Flippes Y and Z axis of an image stack. This operation is similar to ImageJs ‘Reslice [/]’ method but offers less flexibility such as interpolation.

Converts a table column to an image.

Converts a table to an image.

Applies a rigid transform using linear interpolation to an image stack.

Rotates an image in plane.

Rotates an image stack in 3D.

Rotates a given input image by 90 degrees clockwise.

Rotates a given input image by 90 degrees counter-clockwise.

Rotates a given input image by 90 degrees counter-clockwise.

Rotates a given input image by 90 degrees counter-clockwise.

Pulls an image from the GPU memory and saves it as TIF to disc.

Scales an image with a given factor.

Scales an image with a given factor.

Scales an image with a given factor.

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.

Sets all pixel values x of a given image X to a constant value v.

Sets all pixel values x of a given column in X to a constant value v.

Sets all pixel values at the image border to a given value.

Sets all pixels in an image which are not zero to the index of the pixel.

Sets all pixel values x of a given plane in X to a constant value v.

Sets all pixel values to their X coordinate

Sets all pixel values to their Y coordinate

Sets all pixel values to their Z coordinate

Fills an image or image stack with uniformly distributed random numbers between given minimum and maximum values.

Sets all pixel values x of a given row in X to a constant value v.

Sets all pixel values a of a given image A to a constant value v in case its coordinates x == y.

Sets all pixel values a of a given image A to a constant value v in case its coordinates x > y.

Sets all pixel values a of a given image A to a constant value v in case its coordinates x < y.

Determine the shortest distance from a distance matrix.

Visualises two 2D images as one RGB image.

Visualises a single 2D image.

Visualises three 2D images as one RGB image

Extend labels with a given radius.

Determines the absolute difference between two images and sets all pixels to 1 where it is below or equal a given tolerance, and 0 otherwise.

Apply SimpleITKs Bilateral filter to an image.

Apply SimpleITKs Binary Fill hole to an image.

Apply SimpleITKs Binary Pruning to an image.

Apply SimpleITKs Binary Thinning to an image.

Apply SimpleITKs Binomial Blur filter to an image.

Apply SimpleITKs BoundedReciprocal to an image.

Apply SimpleITKs Canny edge detection filter to an image.

Apply SimpleITKs ConnectedComponent to an image.

Generate SimpleITKs Danielsson distance map from a binary image.

Apply SimpleITKs Gaussian Blur to an image.

Convolve an image with a kernel image using SimpleITK and a fast Fourier transform (FFT).

Apply SimpleITKs H-Maxima filter to an image.

Apply SimpleITKs Inverse Deconvolution to an image.

Apply SimpleITKs Landweber Deconvolution to an image.

Apply SimpleITKs Median filter to an image.

Apply SimpleITKs Median projection to an image.

Apply SimpleITKs Morphological Watershed filter to an image.

Apply SimpleITKs Otsu Multiple Thresholds to an image.

Apply SimpleITKs Otsu Thresholding to an image.

Apply SimpleITKs Tikhonov Deconvolution to an image.

Apply SimpleITKs Wiener Deconvolution to an image.

Apply SimpleITKs ZeroCrossing to an image.

Apply SimpleITKs ZeroCrossingBasedEdgeDetection to an image.

Computes the sinus of all pixels value x.

Erodes a binary image until just its skeleton is left.

Determines if two images A and B smaller pixel wise.

Determines if two images A and B smaller pixel wise.

Determines if two images A and B smaller or equal pixel wise.

Determines if two images A and B smaller or equal pixel wise.

Convolve the image with the Sobel kernel.

Convolve the image with the Sobel kernel slice by slice.

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

Turns an image stack in XYZ cartesian coordinate system to an AID polar coordinate system.

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 squared difference pixel by pixel between two images.

Stack to tiles.

Computes the local standard deviation of a pixels box neighborhood.

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 label image and a parametric intensity image and will replace each labels value in the parametric image by the standard deviation value of neighboring labels. The distance number of nearest neighbors can be configured. Note: Values of all pixels in a label each must be identical.

Takes a label image and a parametric intensity image and will replace each labels value in the parametric image by the standard deviation value of neighboring labels.

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

Takes a label image and a parametric intensity image and will replace each labels value in the parametric image by the standard deviation value of neighboring labels. The radius of the neighborhood can be configured: * radius 0: Nothing is replaced * radius 1: direct neighbors are taken into account * radius 2: neighbors and neighbors or neighbors are taken into account * radius n: …

Computes the local standard deviation of a pixels spherical neighborhood.

Determines the standard deviation intensity projection of an image stack along Z.

Starts acquistion of images from a webcam.

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.

Stops continous acquistion from a webcam.

Measures time and outputs delay to last call.

Applies Gaussian blur to the input image and subtracts the result from the original image.

Applies Gaussian blur to the input image and subtracts the result from the original image.

Applies Gaussian blur to the input image and subtracts the result from the original image.

Subtracts one image X from a scalar s pixel wise.

Subtracts one image X from another image Y pixel wise.

Sums all pixels slice by slice and returns the sums in a vector.

Determines the sum of all pixels in a given image.

Determines the sum intensity projection of an image along Z.

Determines the sum intensity projection of an image along Z.

Determines the sum intensity projection of an image along Z.

Convolve the image with the Tenengrad kernel slice by slice.

Fuses #n# image stacks using Tenengrads algorithm.

Convolve the image with the Tenengrad kernel slice by slice.

Computes a binary image with pixel values 0 and 1.

The automatic thresholder utilizes the Default threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

Applies a Difference-of-Gaussian filter to an image and thresholds it with given sigma and threshold values.

The automatic thresholder utilizes the Huang threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the IJ_IsoData threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the Intermodes threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the IsoData threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the Li threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the MaxEntropy threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the Mean threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the MinError threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the Minimum threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the Moments threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the Otsu threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the Percentile threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the RenyiEntropy threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the Shanbhag threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the Triangle threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

The automatic thresholder utilizes the Yen threshold method implemented in ImageJ using a histogram determined on the GPU to create binary images as similar as possible to ImageJ ‘Apply Threshold’ method.

Applies a top-hat filter for background subtraction to the input image.

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.

Applies a top-hat filter for background subtraction to the input image.

Converts a touch matrix in an adjacency matrix

Takes a pointlist with dimensions n*d with n point coordinates in d dimensions and a touch matrix of size n*n to draw lines from all points to points if the corresponding pixel in the touch matrix is 1.

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.

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

Translate an image stack in X and Y.

Translate an image stack in X, Y and Z.

Measures center of mass of thresholded objects in the two input images and translates the second image so that it better fits to the first image.

Applies 2D translation registration to every pair of t, t+1 slices of a 2D+t image stack.

Transpose X and Y axes of an image.

Transpose X and Z axes of an image.

Transpose Y and Z axes of an image.

Copies all pixels instead those which are not a number (NaN) or infinity (inf), which are replaced by 0.

Computes the local variance of a pixels box neighborhood.

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.

Computes the local variance of a pixels spherical neighborhood.

Combines an intensity image and a label (or binary) image so that you can see segmentation outlines on the intensity image.

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

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

Applies two Gaussian blurs, spot detection, Otsu-thresholding and Voronoi-labeling. The thresholded binary image is flooded using the Voronoi approach starting from the found local maxima. Noise-removal sigma for spot detection and thresholding can be configured separately.

Apply a binary watershed to a binary image and introduces black pixels between objects.

Applies a pre-trained CLIJx-Weka model to an image and a corresponding label map to classify labeled objects.

Applies a pre-trained CLIJx-Weka model to an image and a corresponding label map to classify labeled objects.

Takes a point list image representing n points (n*2 for 2D points, n*3 for 3D points) and a corresponding touch matrix , sized (n+1)*(n+1), and exports them in VTK format.

Takes an image with three/four rows (2D: height = 3; 3D: height = 4): x, y [, z] and v and target image.

Takes a point list image representing n points (n*2 for 2D points, n*3 for 3D points) and exports them in XYZ format.

Determines a Z-position of the maximum intensity along Z and writes it into the resulting image.

Determines a Z-position of the minimum intensity along Z and writes it into the resulting image.

Project a defined Z-slice of a 3D stack into a 2D image.

Project multiple Z-slices of a 3D stack into a new 3D stack.

622 methods listed.