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Image processing and analysis is useful to monitor the activated sludge (AS) wastewater treatment plants based on the morphology of microbial aggregates (flocs) and filamentous bacteria. Phasecontrast microscopy is used to observe filamentous bacteria in the AS samples at lower objective magnification with improved visibility of details. However, segmentation of the phase-contrast images faces inherent difficulties caused by the artifacts associated with the microscopy, such as halos and shade-off. This paper is comprised mainly of three tasks: robust segmentation of phase-contrast images for filamentous bacteria, identification of novel image analysis parameters for morphology of the bacteria, and the use of the proposed parameters to model sludge volume index (SVI). SVI is the most important physical measurement employed to monitor the operation of an AS plant. In this paper, a robust phase-congruency-based method, augmented by top-hat and bottom-hat filtering, is proposed for segmentation of filamentous bacteria. Different metrics, such as accuracy, recall, variation of information, F-measure, and Rand index are used for the segmentation assessment. We propose an exact procedure to determine the total length of branched and unbranched filamentous bacteria. Moreover, a novel rotation invariant feature is proposed to determine the extent of the curvature of a filament. Finally, we investigated regression models for SVI of multiple AS wastewater treatment plants, based on the proposed image analysis parameters of the filaments. The modeling of SVI proves the significance of the proposed image analysis parameters for monitoring AS plants.

Image Processing and Analysis of Phase-Contrast Microscopic Images of Activated Sludge to Monitor the Wastewater Treatment Plants