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Image segmentation/condensate_Segmentation_V4.m

+function [DataOut, maskDroplets] = condensate_Segmentation_V4(imageCrop, emulsionMask, pixelSize)
+% in version 4 the watersheding was improved an switched on again
+
+% calibrated for Olympus 40xUPLXAPO air objective  at Olympus IX83 microscope 
+seSize = 5;
+se = strel('disk', seSize);
+se2 = strel('disk', 20);
+
+% start image analysis
+if std2(imageCrop) <= 5 / 2^16
+    % no droplets in image
+    maskDroplets = 0;
+else
+    % there are droplets start segmentation
+    % gradient based particle detection
+    imCropFilterd = roifilt2(imageCrop, emulsionMask, 'imgaussfilt', 2);
+    imCropFilterd = roifilt2(imCropFilterd, emulsionMask, 'imgradient');
+    imCropFilterd = imCropFilterd ./ (mean2(imCropFilterd) + 2 * std2(imCropFilterd));
+    imCropFilterd(imCropFilterd <= 1) = 0;
+    
+    % bw treshholding step
+    thresh = adaptthresh(imCropFilterd, 0.1) ;
+    maskDroplets = imbinarize(imCropFilterd, thresh);
+    
+    % BW filtering
+    maskDroplets = imfill(maskDroplets, 'holes');
+    maskDroplets = imerode(maskDroplets, se); % removes small objects (careful with se)
+    maskDroplets = bwareaopen(maskDroplets, seSize);
+    
+    % watershed to separate connected particles
+    % inspired by https://blogs.mathworks.com/steve/2013/11/19/watershed-transform-question-from-tech-support/
+    maskDist = - bwdist(~maskDroplets);
+    maskDist2 = imimposemin(maskDist, imextendedmin(maskDist, 2));
+    maskWatershed = watershed(maskDist2);
+    maskDroplets(maskWatershed == 0) = 0;
+    maskDroplets = logical(maskDroplets);
+    
+    % derive solute mask and mean intensity
+    maskSolute = emulsionMask - imdilate(maskDroplets, se2);
+    maskSolute(maskSolute < 0) = 0;
+    maskSolute = logical(imerode(maskSolute, se2));
+end
+
+if ~sum(sum(maskDroplets)) == 0
+    dropletDiameters = regionprops(maskDroplets, 'EquivDiameter');
+    dropletRadii = cat(1, dropletDiameters.EquivDiameter) ./2 .* pixelSize;
+    dropletVolumes = 4/3 .* pi .* dropletRadii.^3;
+    dropletRegProps = regionprops(maskDroplets, imageCrop, 'MeanIntensity', 'MaxIntensity', 'Eccentricity', 'Circularity', 'Centroid');
+    
+    % Data output in case of condensates present
+    DataOut.dropletRadii = dropletRadii;
+    DataOut.dropletVolumes = dropletVolumes;
+    DataOut.dropletTotalVolume = sum(dropletVolumes);
+    DataOut.dropletIntensities = [dropletRegProps.MeanIntensity]';
+    DataOut.dropletIntensities_mean = mean([dropletRegProps.MeanIntensity]);
+    DataOut.dropletIntensities_std = std([dropletRegProps.MeanIntensity]);
+    DataOut.dropletIntensitiesMax = [dropletRegProps.MaxIntensity]';
+    DataOut.dropletEccentricity = [dropletRegProps.Eccentricity]';
+    DataOut.dropletCircularity = [dropletRegProps.Circularity]';
+    DataOut.dropletCentroid = [dropletRegProps.Circularity]';
+    DataOut.soluteIntensity_mean = mean(imageCrop(maskSolute));
+    DataOut.soluteIntensity_std = std(imageCrop(maskSolute));
+else
+    % Data output in case of no condensates present
+    DataOut.dropletRadii = NaN;
+    DataOut.dropletVolumes = NaN;
+    DataOut.dropletTotalVolume =  NaN;
+    DataOut.dropletIntensities =  NaN;
+    DataOut.dropletIntensities_mean = NaN;
+    DataOut.dropletIntensities_std = NaN;
+    DataOut.dropletIntensitiesMax = NaN;
+    DataOut.dropletEccentricity = NaN;
+    DataOut.dropletCircularity = NaN;
+    DataOut.dropletCentroid = NaN;
+    DataOut.soluteIntensity_mean = mean(imageCrop(emulsionMask));
+    DataOut.soluteIntensity_std = std(imageCrop(emulsionMask));
+end
+
+end
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