Special Section on Quality Control by Artificial Vision: Nonconventional Imaging Systems

Local adaptive approach toward segmentation of microscopic images of activated sludge flocs

[+] Author Affiliations
Muhammad Burhan Khan, Humaira Nisar, Choon Aun Ng, Po Kim Lo, Vooi Voon Yap

Universiti Tunku Abdul Rahman, Faculty of Engineering and Green Technology, Jalan Universiti, Bandar Barat, Kampar 31900, Malaysia

J. Electron. Imaging. 24(6), 061102 (Sep 03, 2015). doi:10.1117/1.JEI.24.6.061102
History: Received May 19, 2015; Accepted July 21, 2015
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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.

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Citation

Muhammad Burhan Khan ; Humaira Nisar ; Choon Aun Ng ; Po Kim Lo and Vooi Voon Yap
"Local adaptive approach toward segmentation of microscopic images of activated sludge flocs", J. Electron. Imaging. 24(6), 061102 (Sep 03, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.6.061102


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