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# Testing vaccine spray/aerosol droplets

### From Mathematics Is A Science

This image analysis example came from a manager of an animal health company: "What I am basically interested is the total number of spots on the yellow paper, their size and the proportion of various size spots compared to the total. For example there are 500 spots on the paper and 10% of them are < 1-5 micron, 30 % of them are 1-5 micron, 30 % are 5-10 micron etc. I am using these yellow papers for testing vaccine spray/aerosol droplets and would like to assess various sprayers for vaccination purposes."

I've experimented with the images a bit. These are the results.

First image, P3260017.JPG. I cropped it first. The analysis with settings 0-10 captured all of the spots but also some noise. The image would have to have a higher resolution for Pixcavator to capture the spots more reliably. Just bringing the camera closer to the sheet would significantly improve the quality of the analysis. Pixcavator's output table is also below.

Second image, P3260024.JPG. I cropped it and then applied AutoContrast (underTools). The analysis settings I used were 10-10. The results are much better. I saved the image with all objects colored so that you can see that all spots are captured and what appears to be noise isn't. (I had to make a call on what is noise, but as it turns out the user wants to count those as well.) Pixcavator's output table is also below.

About the output. First, you'd need to calibrate the image because the data in the spreadsheet is presented in terms of pixels not microns. Next, assuming that the image analysis is satisfactory, what's left is data analysis with Excel. Excel allows you to count the number of rows with entries within parameters that you set.

Calibration is the number of microns per pixel. The easiest way to find it is to take a picture of a ruler and then measure its length in pixels. You can use that number with other pictures but only if you take them at exactly same distance, depending on how much accuracy you need.

My conclusion is that Pixcavator would be able help with this problem.

**Run this analysis** with Pixcavator SI.

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