Automated fluorescence microscopy image analysis of Pseudomonas aeruginosa bacteria in alive and dead stadium

Fluorescent microscopy techniques take advantage of observing even single cells in live and dead stadium, and make it possible to selectively recognize specific components of biomolecular structures. This methodology is based on Green Fluorescent Protein (GFP) recognition of biochemical activities of individual microbial cells visible in screening. Unfortunately, recognition perception of human professional for fluorescent signals can be affected by various environmental factors what can lead to false interpretation of the results. Therefore intelligent computer method for fluorescent signal counting can be a great assistance at work. In this article we present experimental research results on the development of new automated fluorescence microscopy image analysis, implemented for Pseudomonas aeruginosa bacteria. Proposed method is composed of two stages of image processing. In the first, we enhance the image and extract only important bacteria shapes into simplified image. In the second, this simplification is used for detection of rod shape and spherical shape bacteria. At the end of processing statistical analysis is performed to evaluate number of bacteria in dead and alive stadium.