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.Using blind parameters, survival curves generated by both methods showed some differences: P-values were 0.0897 for SK-BR-3 cells and 0.0024 for MCF-7/HER2-18 cells.Ĭonclusions: The colony counting using ImageJ and customized macros with optimized parameters was a reliable method for quantifying the number of colonies.My approach to this problem is as follows: Survival curves generated from digital and manual counts were not significantly different P-values were 0.3646 for SK-BR-3 cells and 0.1818 for MCF-7/HER2-18 cells. Both reproducibility and repeatability of digital counts were better than the manual method. Survival curves derived from digital and manual counts were compared by F-test ( P < 0.05). The correlation of digital and manual counting and inter- and intra-experimenter variability were examined by linear regression. Key parameters, intensity threshold and minimum colony size, were optimized based on three preliminary manual counts or blindly chosen. Colonies were counted manually or digitally using ImageJ software with customized macros. Materials and methods: Breast cancer cells were treated with trastuzumab-conjugated gold nanoparticles in combination with X-ray irradiation, 111In labeled trastuzumab, or γ-radiation, followed by clonogenic assays. Purpose: To develop a digital method for counting colonies that highly replicates manual counting.
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