Utility Plugins

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The GDSC plugins provide various utility tools for image analysis. The following tools are available:. The following tools are available: Binary Image Processing Threshold Provides methods to threshold an image into foreground and background pixels. This is a modified version of the Auto Threshold plugin developed by Gabriel Landini. An additional method had been added that sets the threshold as the image average plus a factor of the standard deviation.

This is a fast method that can be used within the Find Peaks algorithm Multi Threshold Thresholds an image into levels using multiple thresholds. Based on the Multi Threshold plugin developed by Yasunari Tosa; the modifications allow processing bit images and imagej threshold binary options auto threshold imagejcom output options Mask Creater Creates a mask from an image with the following options: This respects the display value for each channel Use ROI: Edge Mask Create an edge mask from an image.

Edges are defined where the gradient is steepest. Provides methods to threshold an image into foreground and background pixels. This is a fast imagej threshold binary options auto threshold imagejcom that can be used within the Find Peaks algorithm. Thresholds an image into levels using multiple thresholds.

Based on the Multi Threshold plugin developed by Yasunari Tosa; the modifications allow processing bit images and further output options. Creates a mask from an image with the following options: Create an edge mask from an image. Create a mask from a source image using the Mask Creater and apply it to a target image. All pixels outside the mask in the target image will be set to zero.

Skeletonise reduce to single pixel lines a mask image. Then produce a set of lines connecting node points imagej threshold binary options auto threshold imagejcom the skeleton. Analyses an image using a given mask. Skeletonises the mask image and extracts a set of lines connecting node points on the skeleton. This allows a one-click adjustment of all the open images for optimal viewing. Performs a Difference of Gaussians filter for local contrast enhancement. The filter performs subtraction of one blurred version of an image from another, less blurred version of the original.

The result is an image containing only the information contained within the spatial frequency imagej threshold binary options auto threshold imagejcom the two blurred images. Aligns an image stack to a reference image using XY translation to maximise the normalised correlation. Outputs a new stack with the best alignment with optional sub-pixel accuracy performed using a Gaussian fit or Cubic spline fit. The correlation is computed in the Frequency domain using Fast Fourier Transforms. Edge artifacts of the Frequency domain are reduced by using a window function on the image gradually reducing the edge imagej threshold binary options auto threshold imagejcom to zero.

Contains options to restrict the translation space. Note that the results will differ slightly due to the use of different correlation methods. Aligns open image stacks to a reference image using XY translation to maximise the normalised correlation. The self-align mode aligns all timepoints within a stack to the current frame. The projection is thresholded to extract the signal, normalised to and the resulting channel images are tiled imagej threshold binary options auto threshold imagejcom a composite image.

The composite is then aligned using the maximum correlation between the images. Using a composite ensures each channel contributes equally to the aligment. The translation is applied to the entire stack for that timepoint.

Scales all images in a stack so that the maximum of the image is set to the specified value. For example this can be used to scale a floating point image so that the maximum is for correct display. Can accept a list file of images allowing a set of images to be scaled together. The maximum of the entire set of images is used to calculate the scale factor applied to each image.

Find cell edges using marked seed points. Best suited to identify round cells with edge pixels either lighter or darker than the cell. For each point ROI the plugin uses a curved edge filter to find edges within a range of an expected radius. Edge detection is performed using an approximation to the Laplacian-of-Gaussian LoG and the kernel size and smoothing can be controlled.

The edge detection is weighted using the distance to the current outline of the cell initially a circle of the specified radius around the point.

A polygon outline is constructed using the highest edge value in each 10 degree segment around the centre. This is fitted using an oval shape of two ellipses back-to-back and imagej threshold binary options auto threshold imagejcom process iterated using the new cell outline. The final outline can be output as the polygon shape or the elliptical fit. This can be dilated to expand the outline to encompass the entire cell. Supports a preview allowing the parameters to be interactively set.

The average intensity of each channel inside and outside the spot are then compared for the top N spots to identify increases in signal within spot regions. Pixels are sampled using a radius around the clicked position. The average HSB values and the standard deviations from all sampled pixels are shown in a frame. Provides the ability to call the HSB Filter. The filter values are set using the averages and the widths using the scaled standard deviations e. All pixels not within the specified width of the given values are set to zero.

Note that Hue is a continuous property where values are wrapped around from 1 to 0. Finds spots in an image. Imagej threshold binary options auto threshold imagejcom the closest neighbour spot within a radius and produces a line profile through the spots. If no neighbour is present produces a line profile through the principle axis. Outputs the separation between the spot maxima to a table.

Allows analysis of regions using the ROI selection tools and respects manual thresholding to identify spots. The mode can be calculated with or without ignoring zero values. This plugin is useful for projecting 3D masks, for example those created by the FindFoci plugin. Analyses the mean value within an ROI across a stack of exposures. Exposures must be set within the slice labels.

Saturated pixels within an ROI remove that imagej threshold binary options auto threshold imagejcom from analysis. A linear fit is made of the mean verses exposure using a rolling window and the gradient reported for the best fit. The gradient is a measure of the pixel intensity within the ROI. This plugin assumes images are taken of the same object at different exposure times on a camera with a linear response curve.

Thus doubling the exposure should double the pixel value. The plugin allows analysis of images with a large dynamic range by capturing each object with a suitable exposure.

Drift should be minimal for the best results. Installs a tool into the menu that allows distances to be measured in 3D. Distance is measured between imagej threshold binary options auto threshold imagejcom successive mouse clicks on the same channel and time frame of an image.

Results are recorded to a table.

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Thresholding is an image operation that separates an image into two classes: When thresholding, you have the option of choosing a threshold value or you can use an algorithm that calculates the threshold for you.

But before applying a threshold to an image lets look at an example. Figure 1 shows an example of an 8bit image grayscale image , where the pixel intensity values ranges from A histogram will give you the number of pixels that have a specific intensity value. For instance, in this example, there are 46 pixels that have the intensity value of Figure 2A.

If you want to save the histogram or look at the values of each intensity press List Figure 2B. Now, lets threshold an image. A new window will open and your image will have an overlay of red that represents all the thresholded pixels Figure 3.

In this example all the pixels that have an intensity value greater than will be thresholded. Now, if you scroll and choose another number like 13, then more pixels are going to be selected Figure 4. If you click apply Figure 3A , then you will convert the image into a binary image, with values 0 and There are different algorithms that can automatically determine the threshold of an image or selection. A description of each individual algorithm can be found here.

The most commonly used algorithms are the Otsu and the Dafault, which is a variation of the IsoData algorithm. The Otsu algorithm simply chooses a threshold where the combined spread or intra class variance of the foreground and background is minimal.

A window like this will open Figure 5. Histogram If you want to save the histogram or look at the values of each intensity press List Figure 2B.

Manual Threshold Now, lets threshold an image. Otsu The Otsu algorithm simply chooses a threshold where the combined spread or intra class variance of the foreground and background is minimal. After choosing the algorithm just click apply. Particle Analyzer options There are different options: Choose the size of the particles Choose the circularity, a size of 1 means a perfect circle Select if you want to calculate area, mean, etc.

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