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Segmentation Approach Towards Phase-Contrast Microscopic Images of Activated Sludge to Monitor the Wastewater Treatment

Published online by Cambridge University Press:  07 December 2017


Muhammad Burhan Khan
Affiliation:
Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar, Perak 31900, Malaysia
Humaira Nisar
Affiliation:
Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar, Perak 31900, Malaysia
Choon Aun Ng
Affiliation:
Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar, Perak 31900, Malaysia
Kim Ho Yeap
Affiliation:
Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar, Perak 31900, Malaysia
Koon Chun Lai
Affiliation:
Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar, Perak 31900, Malaysia
Corresponding
E-mail address:

Abstract

Image processing and analysis is an effective tool for monitoring and fault diagnosis of activated sludge (AS) wastewater treatment plants. The AS image comprise of flocs (microbial aggregates) and filamentous bacteria. In this paper, nine different approaches are proposed for image segmentation of phase-contrast microscopic (PCM) images of AS samples. The proposed strategies are assessed for their effectiveness from the perspective of microscopic artifacts associated with PCM. The first approach uses an algorithm that is based on the idea that different color space representation of images other than red-green-blue may have better contrast. The second uses an edge detection approach. The third strategy, employs a clustering algorithm for the segmentation and the fourth applies local adaptive thresholding. The fifth technique is based on texture-based segmentation and the sixth uses watershed algorithm. The seventh adopts a split-and-merge approach. The eighth employs Kittler’s thresholding. Finally, the ninth uses a top-hat and bottom-hat filtering-based technique. The approaches are assessed, and analyzed critically with reference to the artifacts of PCM. Gold approximations of ground truth images are prepared to assess the segmentations. Overall, the edge detection-based approach exhibits the best results in terms of accuracy, and the texture-based algorithm in terms of false negative ratio. The respective scenarios are explained for suitability of edge detection and texture-based algorithms.


Type
Instrumentation and Software
Copyright
© Microscopy Society of America 2017 

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