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.

In situ microscopy as a tool for the monitoring of filamentous bacteria: a case study in an industrial activated sludge system dominated by M. parvicella.

The present study demonstrates the application of in situ microscopy for monitoring the growth of filamentous bacteria which can induce disturbances in an industrial activated sludge process. An in situ microscope (ISM) is immersed directly into samples of activated sludge with Microthrix parvicella as dominating species. Without needing further preparatory steps, the automatic evaluation of the ISM-images generates two signals: the number of individual filaments per image (ISM-filament counting) and the total extended filament length (TEFL) per image (ISM-online TEFL). In this first version of the image-processing algorithm, closely spaced crossing filament-segments or filaments within bulk material are not detected. The signals show highly linear correlation both with the standard filament index and the TEFL. Correlations were further substantiated by comparison with real-time polymerase chain reaction (real-time PCR) measurements of M. parvicella and of the diluted sludge volume index. In this case study, in situ microscopy proved to be a suitable tool for straightforward online-monitoring of filamentous bacteria in activated sludge systems. With future adaptation of the system to different filament morphologies, including cross-linking filaments, bundles, and attached growth, the system will be applicable to other wastewater treatment plants.

Evaluating the influence of wastewater composition on the growth of Microthrix parvicella by GCxGC/qMS and real-time PCR.

This study underlines the significance of long chain fatty acid (LCFA) content in wastewater influents as an influencing factor promoting the growth of Candidatus 'Microthrix parvicella' (M. parvicella), the most common filamentous bacteria causing foam in activated sludge systems worldwide. Quantification of M. parvicella by real-time polymerase chain reaction (real-time PCR) and analysis of LCFAs by means of two-dimensional gas chromatography coupled with mass spectrometry (GCxGC/qMS), involving solid phase micro-extraction (SPME) to enhance sensitivity, were combined for the first time as a monitoring tool. The results indicate a highly significant correlation between the abundance of M. parvicella and the total LCFA loading (r = 0.96) and linolenic acid C18:3 (r = 0.98) in particular. Additionally, comparison of slope values for the direct correlations of all significant LCFAs found in the analyses showed that the influence of LCFAs on M. parvicella growth increases with an increasing degree of unsaturation of carbon chains. These findings suggest that by removing lipid compounds from the incoming waters, substrate availability would be limited for M. parvicella.