Image processing for identification and quantification of filamentous bacteria in in situ acquired images.
BACKGROUND
In the activated sludge process, problems of filamentous bulking and foaming can occur due to overgrowth of certain filamentous bacteria. Nowadays, these microorganisms are typically monitored by means of light microscopy, commonly combined with staining techniques. As drawbacks, these methods are susceptible to human errors, subjectivity and limited by the use of discontinuous microscopy. The in situ microscope appears as a suitable tool for continuous monitoring of filamentous bacteria, providing real-time examination, automated analysis and eliminating sampling, preparation and transport of samples. In this context, a proper image processing algorithm is proposed for automated recognition and measurement of filamentous objects.


METHODS
This work introduces a method for real-time evaluation of images without any staining, phase-contrast or dilution techniques, differently from studies present in the literature. Moreover, we introduce an algorithm which estimates the total extended filament length based on geodesic distance calculation. For a period of twelve months, samples from an industrial activated sludge plant were weekly collected and imaged without any prior conditioning, replicating real environment conditions.


RESULTS
Trends of filament growth rate-the most important parameter for decision making-are correctly identified. For reference images whose filaments were marked by specialists, the algorithm correctly recognized 72 % of the filaments pixels, with a false positive rate of at most 14 %. An average execution time of 0.7 s per image was achieved.


CONCLUSIONS
Experiments have shown that the designed algorithm provided a suitable quantification of filaments when compared with human perception and standard methods. The algorithm's average execution time proved its suitability for being optimally mapped into a computational architecture to provide real-time monitoring.
Graduate Program in Electrical and Computer Engineering, Federal University of Technology Paraná, Av. Sete de Setembro 3165, Curitiba, 80230-901, Brazil. phil.adias@gmail.com.|Department of Information Technology, Mannheim University of Applied Sciences, Paul-Wittsack-Str. 10, 68163, Mannheim, Germany. phil.adias@gmail.com.
Philipe A
PA
Dias
Institute for Urban Water and Waste Management, University of Duisburg-Essen, Universitätsstr. 15, 45141, Essen, Germany.
Thiemo
T
Dunkel
Department of Information Technology, Mannheim University of Applied Sciences, Paul-Wittsack-Str. 10, 68163, Mannheim, Germany.
Diego A S
DA
Fajado
Institute for Urban Water and Waste Management, University of Duisburg-Essen, Universitätsstr. 15, 45141, Essen, Germany.
Erika de León
Ede L
Gallegos
Institute for Urban Water and Waste Management, University of Duisburg-Essen, Universitätsstr. 15, 45141, Essen, Germany.
Martin
M
Denecke
Department of Biotechnology, Mannheim University of Applied Sciences, Paul-Wittsack-Str. 10, 68163, Mannheim, Germany.
Philipp
P
Wiedemann
Graduate Program in Electrical and Computer Engineering, Federal University of Technology Paraná, Av. Sete de Setembro 3165, Curitiba, 80230-901, Brazil.
Fabio K
FK
Schneider
Department of Information Technology, Mannheim University of Applied Sciences, Paul-Wittsack-Str. 10, 68163, Mannheim, Germany.
Hajo
H
Suhr
D000465:Algorithms; D001419:Bacteria / Q000166:cytology* / Q000302:isolation & purification*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D012372:ROC Curve; D012722:Sewage / Q000382:microbiology
D012722:Sewage
Digital image processing; Filamentous bacteria recognition; Filamentous bulking and foaming; Filamentous microorganism; In situ microscopy; Wastewater treatment