GPU accelerated image processing for everyone

This reference contains all methods currently available in CLIJ, CLIJ2 and CLIJx for processing binary 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)

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

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

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.

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

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.

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 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 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 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.

Generates a distance map from a binary image.

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 if two images A and B equal pixel wise.

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

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.

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

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.

Determines a threshold according to a given method and saves it to the threshold_value variable.

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

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

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

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

Apply ImageJs Watershed algorithm to a binary image.

Apply ImageJs Watershed algorithm to a binary image.

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

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

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

Masks a single label in a label map.

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

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 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.

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

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

Determines the mean intensity in a masked image.

Determines the minimum intensity in a masked 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 Keep Largest Region to a binary image.

Apply MorpholibJs Marker-controlled Watershed to an image.

Determines if two images A and B equal pixel wise.

Determines if two images A and B equal pixel wise.

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.

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.

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.

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.

Apply SimpleITKs Binary Fill hole to an image.

Apply SimpleITKs Binary Pruning to an image.

Apply SimpleITKs Binary Thinning to an image.

Generate SimpleITKs Danielsson distance map from a binary image.

Apply SimpleITKs Otsu Thresholding to an image.

Apply SimpleITKs ZeroCrossing to an image.

Apply SimpleITKs ZeroCrossingBasedEdgeDetection to an image.

Erodes a binary image until just its skeleton is left.

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

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

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.

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.

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.

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

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.

118 methods listed.