![]() ![]() Add colony: Adds the currently selected area as a colony allowing correcting the automatic count.Count folder: Counts and/or loads the *-RoiSet.zip for all images in the current folder, and replaces the current "Results" table with the new results.Open next: Replaces the current image with the next one in the same folder and do the same as "Open image".The number of colonies is added in a new row to the "Results" table. Open image: Opens an image and automatically counts it's colonies, or simply loads and display the saved *-RoiSet.zip from a previous count.The plugin's interface is simple, on the left are the functions buttons, on the right the input fields for the settings.Įxcept for the "Delete mode" and the "About" buttons, all the others have a keyboard shortcut (visible inside square brackets on the button's name) that can be used when the mouse cursor is placed on the image. When the image is closed, a file named *-RoiSet.zip is automatically saved in the current folder, so that the next time the same image is opened, the plugin won't have to recount that image and can simply load the results from the *-RoiSet.zip. The obtained values are displayed in a "Results" table and can be saved in *.xls file format. This plugin was created to count bacteria colonies from agar plates images. Or subfolder, restart ImageJ, and there will be a new "Colony Counter" command in the Plugins menu or submenu. Included in Colony_Counter.jar, and is released under the GNU General Public License.ĭownload Colony_Counter.jar to the plugins folder, When you have scanned the whole image, merge all labels that you have noted (they are connected to the same object) and then count the number of distinct labels and you will have your count.University of Lisbon, Faculty of Sciences Label exists and the pixel to the right of p has a different label, make a note of this. If p is a background pixel or p has a label, do nothing. If p is a background pixel or p has a label, do nothing.įor each pixel p: If p is an object pixel, copy the label from the right if there is one, pixels that have value 1.įor each pixel p: If p is an object pixel, copy the label from above or left. The labeling is the done by scanning each pixel row twice, once left-to-right and once right-to-left. You could use a technique called connected component labeling which can be used do distinguish between different objects in an image.įirst of all you need to make the image binary by some mean of thresholding. %# Then you can see why we are getting some false colonies. You can inspect the segmentation ( mask) and colony markers ( colonies) to see where mistakes are made: %# Leave out the changes to mask to just see the colony markers. The count is 359, which I'd say is pretty close. If you have an image which doesn't work but you think should, leave a comment there. ![]() I have also uploaded the colony_count function on the file exchange. We use this code like this: I = imread('') %# Component labeling with 4-connectivity to avoid merging adjacent colonies. %# maxima (the brightest points in the image) which occur in the segmentation. %# quite bright, we can find a single point per colony by as the regional %# Colonies are merged in the segmented image. The erosion is to make sure no dish pixels Roi = roi & poly2mask(x,y,m,n) %# Cumulative union of all circles. X = x + peak(1) %# Translate the circle appropriately. = circlepoints(peak(3)) %# Points on the circle of this radius. Peaks = circle_houghpeaks(h, radii, 'npeaks', 10) %# Pick top 10 circles. H = circle_hough(bw,radii,'same','normalise') %# Circular HT. %#if graythresh(I) graythresh(I) %# Otsu's method. %# Uncomment this if you have might have some images with light background I = rgb2gray(im2double(I)) %# Color-to-gray conversion. Things are pretty straightforward from there: function = colony_count(I) This file exchange toolbox provides us with a working circular Hough transform. Count colonies as the regional maxima of the original image, which are represented in the segmented image.Global thresholding with Otsu's method, restricted to the dish.Use Hough transform to identify circles corresponding to the Petri dish.My approach to this problem is as follows: ![]()
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