State of the art software methods (such as fixed value approaches or statistical approaches) to create a binary image of fluorescent bacterial cells are not as accurate and precise as they should be for counting bacteria and measuring their area. To overcome these bottlenecks, we introduce biological significance to obtain a binary image from a greyscale microscopic image. Using our biological significance approach we are able to automatically count about the same number of cells as an individual researcher would do by manual/visual counting. Using the fixed value or statistical approach to obtain a binary image leads to about 20% less cells in automatic counting. In our procedure we included the area measurements of the bacterial cells to determine the right parameters for background subtraction and threshold values. In an iterative process the threshold and background subtraction values were incremented until the number of particles smaller than a typical bacterial cell is less than the number of bacterial cells with a certain area. This research also shows that every image has a specific threshold with respect to the optical system, magnification and staining procedure as well as the exposure time. The biological significance approach shows that automatic counting can be performed with the same accuracy, precision and reproducibility as manual counting. The same approach can be used to count bacterial cells using different optical systems (Leica, Olympus and Navitar), magnification factors (200× and 400×), staining procedures (DNA (Propidium Iodide) and RNA (FISH)) and substrates (polycarbonate filter or glass).
Keywords: Algorithm; Automated-cell-count; Binarization; Fluorescence; Microscopy.
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