![]() Steps implemented and parameters (set in the macro code, not from a dialog box) The contours can also be saved from the ROI manager window to keep them as reference with the original data. If the macro is launched with exactly 3 selections in the ROI manager it will use these contours automatically without asking the user to draw them (same transformation applied) it is hence easy to process multi-channel 3D stacks. The contours are saved to the ROI manager automatically. The tube axis and contour are user drawn from three different views. Process only a single channel 3D stack but it is easy to process multiple channels by exporting and importing ROI manager selections (so as to perform the same operation). The final image is a radial mapping of the intensity (radial angle along X, axial length along Y). Technically, compute the radial average intensity projection inside a ring centered on the radial symmetry axis of the object. Unfold a tubular structure and flatten its surface (like peeling of and flattening the skin of a banana). The dilation is used after comet detection to ensure an overlap between the same comet from frame to frame and avoid multiple counting of the same comet.ġ) A reference image showing the detected soma after border retraction and comets (Fig 10.2).Ģ) A log showing the image name, the soma area and the comet (spot) count. ![]() Spot dilation: Dilation of the detected comets (in pixel). Min spot area: Minimum area of a comet (in pixel). Used to ignore bright edge signal (autofluorescence). This mask is used to 1) restrict the comet detection and 2) report cell area.Īpproximate radius: Comet approximate radius in pixels.ĭetection threshold: Intensity sensitivity level for comet detection.īorder retraction: Cell boundary retraction (pix). If this fails it is possible to use a user drawn segmentation mask (then fixed across time). Manual ROI drawing (tick box): By default the macro segments the cell for all time points. Process spinning disk time-lapses to 1) segment the soma and reports its area (first frame only), then count the number of micro-tubule comets inside the soma across the time-lapse. To find out the version of Weka trainable segmentation that you run, call the plugin and copy the name of the plugin window to this string.Ĭ lassifier: The classifier is expected to have the default name ( classifier.model ), unless the string classifier is updated in the macro preamble.ġ) A label mask per image with one gray level per class (Figure 9.2).Ģ) A results table (figure 9.3) reporting the image names and fractional class areas (one image per row). The version should be updated in the first line of the macro, (string variable WekaVersion). WekaVersion: The macro has only be tested with Weka trainable segmentation v3.3.2 even though it should also work with a different version. ![]() The classifier to be applied should be previously trained on a representative image and exported to file (Save classifier) to an empty folder named Results inside the folder with the images to be processed.Īfter launching the macro, pick any image from the folder to process. Macro Test Data (input image folder and trained classifier) (410 KB)īatch processes all the images (TIFF and JPEG files) located in a user defined folder by calling Fiji Weka trainable segmentation to classify each pixel, and reports the areas of each class in a human readable results table. ![]() Please, do not tackle this problem by deblurring - it simply does not help.Trainable WEKA Segmentation batch processing Maybe because of the problem nature I cannot find anything via Google. So I have original image, I have to pass the image through Gaussian Blur (,], / 16) and prepare original image before blurring, so that it will be equal to original image (whenever possible, and closest to the original image when not possible). Making it short - Filter should make inverse of original image that will give original image after Gaussian blur. This operation has nothing to do with sharpening image (I assume it will disort image not make it sharper) and will never be used to restore original image from blurred one (it differs in assumptions and available data from that problem). Let us call it invB, and operation of Gaussian blur as function GBlur and original image O.įilter I would like to have gives invB as output, so that GBlur(invB) = O. I have original bitmap and I want to make something like Gaussian Blur Inverse. I am looking for method to obtain reverse blur filter.
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