- #HOW TO CALCULATE PARTICLE DISTRIBUTION IN IMAGEJ SOFTWARE HOW TO#
- #HOW TO CALCULATE PARTICLE DISTRIBUTION IN IMAGEJ SOFTWARE MANUAL#
- #HOW TO CALCULATE PARTICLE DISTRIBUTION IN IMAGEJ SOFTWARE SERIES#
#HOW TO CALCULATE PARTICLE DISTRIBUTION IN IMAGEJ SOFTWARE MANUAL#
One of the sample sets could be from the original cited research, ideally - but we could also include "difficult" image sets to ensure the method developed robustly replicates the manual method. Output: a results.csv and area.csv based on a manual processing of the input image Input: an image set that's been manually processed I was thinking that a good test/spec might include at least one sample set with: Is there any sample input/output sets from going through this process, manually or automatically? Thanks to & for their input and guidance! When using a pseudo flat-field, I'm running the "Mean." command with various radius values until I find one that sufficiently blurs the image, and then use that value to run the macro.
It might be good for the macro to save a pseudo flat-field when it's generated for future reference. Flat-field correction will handle the stable dust, but not the dust that "moves" across images. Here is an image of what I think are potential false positives (dust?). Flatfield correction is a start, but only affects false positives that are consistent across images. Dust in the microscope or on the slide can cause dark blotches, or uneven illumination can affect the results of image processing.
#HOW TO CALCULATE PARTICLE DISTRIBUTION IN IMAGEJ SOFTWARE HOW TO#
This could help reduce issues with uneven illumination.Ī big question is how to remove potential false-positives from the image. (The latter option may be useful in ensuring consistency across runs of the macro, especially as the needed values are exported to the "area.csv" file at the end of each run.) Questions and next stepsĪn approach that might be worth incorporating is Rolling-Ball background correction.
You can either measure a scale in an image or enter a known value of pixels per inch. There are two options for calibrating the image scale. This radius needs to be large enough to generate a blurred image where the individual particles are no longer found that extracting the blue channel before processing images helped, so that is an option as well. The macro asks for a "radius value" to generate a gaussian blurred version of the image. If there is not an existing flat-field image, the macro can generate a pseudo flat-field. If the sample includes an image taken with an empty slide (a "flat-field), that image can be used to remove uneven illumination from the photos. Vignetting is handled by selecting a region of interest in one image that crop area is then applied to all of the images in the sample.įlatfield image correction is available. I included some image correction to handle image calibration. Save the area measured and image count as a separate csv ("area.csv).Save the results table as a csv ("results.csv").(each of these steps saves a separate image as an intermediate step).Analyze particles (and superimpose a particle count number on each particle in the saved image).For each file in directory with a valid image extension ('tif', 'tiff', 'jpg', 'jpeg', 'bmp', 'fits', 'pgm', 'ppm', 'pbm','gif', 'png', 'jp2','psd’):.Create new directory to save for analysis results and images.store crop setting to be applied to all images.Open first image to manually apply cropping to cut out vignetting.store scale to be applied to each image.draw a line between two points of known distance,.prompt for image with calibration ruler,.Prompt for flatfield image for correction, if available.The macro will attempt to ignore files without image-type file suffixes. all images from one sample need to be in a directory without other images.
This roughly follows process of using ImageJ to process passive particle monitor samples The macro (version 0.1) can be downloaded here. These results then need to be entered into the spreadsheet developed by from these calculations. I wrote a script to automate the process of analyzing images. ImageJ has a macro language somewhat akin to a simplified version of Java that includes a built-in recorder to automate actions. A single slide can result in dozens of photographs to cover the entire sample area, so we want to automate this process as much as possible.
#HOW TO CALCULATE PARTICLE DISTRIBUTION IN IMAGEJ SOFTWARE SERIES#
ImageJ (or Fiji) has tools that speed up this analysis, but a series of steps must be applied to each image for analysis. Measuring air quality with passive particle collectors requires an accurate count of the size and number of particles collected.