3 September 2015 Local adaptive approach toward segmentation of microscopic images of activated sludge flocs
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Abstract
Activated sludge process is a widely used method to treat domestic and industrial effluents. The conditions of activated sludge wastewater treatment plant (AS-WWTP) are related to the morphological properties of flocs (microbial aggregates) and filaments, and are required to be monitored for normal operation of the plant. Image processing and analysis is a potential time-efficient monitoring tool for AS-WWTPs. Local adaptive segmentation algorithms are proposed for bright-field microscopic images of activated sludge flocs. Two basic modules are suggested for Otsu thresholding-based local adaptive algorithms with irregular illumination compensation. The performance of the algorithms has been compared with state-of-the-art local adaptive algorithms of Sauvola, Bradley, Feng, and c-mean. The comparisons are done using a number of region- and nonregion-based metrics at different microscopic magnifications and quantification of flocs. The performance metrics show that the proposed algorithms performed better and, in some cases, were comparable to the state-of the-art algorithms. The performance metrics were also assessed subjectively for their suitability for segmentations of activated sludge images. The region-based metrics such as false negative ratio, sensitivity, and negative predictive value gave inconsistent results as compared to other segmentation assessment metrics.
© 2015 SPIE and IS&T
Muhammad Burhan Khan, Muhammad Burhan Khan, Humaira Nisar, Humaira Nisar, Choon Aun Ng, Choon Aun Ng, Po Kim Lo, Po Kim Lo, Vooi Voon Yap, Vooi Voon Yap, } "Local adaptive approach toward segmentation of microscopic images of activated sludge flocs," Journal of Electronic Imaging 24(6), 061102 (3 September 2015). https://doi.org/10.1117/1.JEI.24.6.061102 . Submission:
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