DOI: 10.17863/cam.113063
title: Research data supporting "Computer Vision for High-Throughput Analysis of Pickering Emulsions"

description: Pickering emulsions were characterised by optical microscopy by pipetting a droplet of the emulsion layer onto a glass slide. Images were recorded using a Canon 400D camera mounted to a Swift 350T microscope using either a ×4, ×10 or ×40 objective at a resolution of 5184 × 3456 pixels (pixels). The images used in analysis were chosen to capture the widest range of morphologies possible. Manual image analysis All images were manually analysed by sampling random coordinates for a given image and marking three points along the inner edge of the droplets at that coordinate. The radii and position of the droplets were then estimated from the three identified points. Random coordinates were sampled until fifty droplets per image were reached. While we created a custom user interface for displaying images and defining/generating coordinates to streamline these time-consuming tasks, they can be easily completed with standard imaging software (e.g. ImageJ). Region Growing analysis (RG) Image analysis by thresholding and RG was carried out using ImageJ. Images were globally thresholded manually so that the image was binarized into droplets and the continuous phase. The ‘analyze particles’ tool was then used and the “size” and “circularity” parameters manually set to best fit the droplets of a given image. The RG technique works by scanning a thresholded image (with all pixels set to either 1 or 0) for an “island” (a pixel set to 1, for example) where a droplet is located. The neighbouring pixels are then tested to see if they are also part of that island (1 or 0). The radius of the droplet is calculated from the area, assuming that the droplet is circular. Circle Hough Transform (CHT) Original open-source software called “Hough-Scan” was developed to perform the semi-automated detection of droplets using the circle Hough transform and is available on GitHub (Hough-scan, https://github.com/KRichardsF/Hough-Scan). It combines tools found in the OpenCV library with a graphical user interface (GUI) that makes it easier to select the appropriate parameters for image analysis. The software also tiles the image and analyses each tile independently to reduce the computational burden of analysing images with many circular objects. The user selectable parameters are as follows: Tile Size, Tile Overlap, Blur, Minimum Distance, Canny Upper Limit, Hough-Threshold, Min Radius and Max Radius. Further discussion of these parameters can be found in the ESI, Section 3. Comparative evaluation of image analysis techniques For each image, true positives (TP) and false positives (FP) were determined manually. The outputs from the image analysis for a given method, i.e. radii and coordinates, were randomly sampled and marked as true or false by comparing them against the original image. Random sampling was achieved using the Python random module (which uses pseudo-random number generators based on the Mersenne Twister algorithm) to choose a random index of a list of droplets that had not been chosen before. Fifty samples were taken for each image and fifty images were used in total, spanning a wide range of emulsion morphologies. A false negative for a given image was determined by comparing a random sample of fifty manually defined droplets to the output from one of the image analysis methods and marking anything with a difference in coordinate greater than 1.5 times the radius as false (i.e., coordinate ± 1.5 × radius = false). This limit was found to provide the best balance between sensitivity and specificity. The accuracy of each method (recall and precision) was determined by comparing the radii of fifty manually determined circles, to the equivalent circle determined by either the RG or CHT methods. For these comparisons, fifty droplets were randomly chosen in the manual image and compared to the same fifty droplets in each of the CHT/RG images. This was repeated for fifty images. It should be noted that all images were benchmarked against manual droplet identification, which as previously stated, carries its own biases due to the subjective nature of image interpretation as well as human errors