Morphological analysis of activated sludge flocs and filaments

Lee, Xue Yong; Khan, Muhammad Burhan; Nisar, Humaira; Yeap, Kim Ho; Ng, Choon Aun; Malik, Aamir Saeed

doi:10.1109/i2mtc.2014.6860985

Cited by 15 publications

(6 citation statements)

References 5 publications

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“…2B) using the equations below that were used to characterize the floc and activated sludge. 31,32,37 Spherical index:…”

Section: †)mentioning

confidence: 99%

“…Methods such as non-intrusive image analysis (NIA) and particle image velocimetry (PIV) have not been applied to investigate the nZVI aggregates, although they are proven to be valuable for the in situ characterization of the solids (e.g., coagulation flocs and activated sludge) in water. [31][32][33] In this work, in situ characterization was conducted to study nZVI aggregates in aqueous solution. Specifically, the aggregates were studied in aqueous solution under hydrodynamic conditions commonly encountered in three typical unit operations in practical nZVI applications, including mechanical mixing, gravitational settling, and compression, using six techniques including non-intrusive image analysis and particle image velocimetry.…”

Section: Introductionmentioning

confidence: 99%

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In situ characterization of aggregates of nanoscale zero-valent iron (nZVI) in water: an engineering aspect

Wang

et al. 2022

Environ. Sci.: Nano

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“…2B) using the equations below that were used to characterize the floc and activated sludge. 31,32,37 Spherical index:…”

Section: †)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In situ characterization of aggregates of nanoscale zero-valent iron (nZVI) in water: an engineering aspect

Wang

et al. 2022

Environ. Sci.: Nano

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“…In [68], a system to monitor morphological features of floc and filaments in activated sludge which uses segmentation approach is introduced. It uses H-minima transform (region based method), followed by median filter, finally conventional binary conversion and area opening operation are applied.…”

Section: B Region Based Segmentation (Rbs)mentioning

confidence: 99%

A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential

Kulwa

Zhao

et al. 2019

IEEE Access

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Microorganisms play a great role in ecosystem, wastewater treatment, monitoring of environmental changes, and decomposition of waste materials. However, some of them are harmful to humans and animals such as tuberculosis bacteria and plasmodium. In such course, it is important to identify, track, analyze, consider the beneficial side and get rid of the negative effects of microorganisms using fast, accurate, and reliable methods. In recent decades, image analysis techniques have been used to address the drawbacks of manual traditional approaches in the identification and analysis of microorganisms. As image segmentation being an important step (technique) in the detection, tracking, monitoring, feature extraction, modeling, and analysis of microorganisms, different methods have been deployed, from classical approaches to current deep neural networks upon different challenges on microorganism images. This survey comprehensively analyses the various studies focused on developing microorganism image segmentation methods in the last 30 years (since 1989). In this survey, segmentation methods are categorized into classical and machine learning methods. Furthermore, these methods are subcategorized into threshold-based, regionbased, and edge-based which belong to classical methods, supervised and unsupervised machine leaningbased methods which belong to machine learning category. A growth trend of different methods and most frequently used methods in each category are meticulously analyzed. A clear explanation of the suitability of these methods for different segmentation challenges encountered on microscopic microorganism images is also enlightened. INDEX TERMSMicroorganism segmentation, content-based microscopic image analysis, feature extraction, microscopic images, classical methods, machine learning.

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“…Final step in image segmentation is the performance evaluation followed by image analysis, in which the automatically segmented image is compared objectively or subjectively with the reference image. Here the main purpose of segmentation is to observe the objects that are invisible to the naked eyes and to recognise the objects in an image (flocs or filaments in the sample) 5,6 . The flocs are bigger in size as compared to the filaments and can be observed at 4 times magnification however filaments need at least 10 or 20 times magnification.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Activated Sludge Filaments using Phase Contrast Images

Nisar

Khan

2018

Orient. J. Comp. Sci. and Technol

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Segmentation algorithms play an important role in image processing and analysis. The identification of objects and process monitoring strongly depends on the accuracy of the segmentation algorithms. Waste water treatment plants are used to treat wastewater from municipal and industrial plants. Activated sludge process is used in wastewater treatment plants to biodegrade the organic constituents present in waste water. This biodegradation is done with the help of microorganisms and bacteria. There are two important types of microscopic organisms present in the activated sludge plants, named as flocs as filaments, which are visible under microscope. In this paper we study the microscopic images of wastewater using phase contrast microscopy. The images are acquired from wastewater sample using a microscope. The samples of wastewater are collected from domestic wastewater treatment plant aeration tank. Our main aim is to segment threadlike organisms knows as filaments. Several segmentation algorithms (such as edge based algorithm, k-means algorithm, texture based algorithm, and watershed algorithm) will be explored and their performance will be compared using gold approximations of the images. The performance of the algorithms are evaluated using different performance metrics, such as Rand Index, specificity, variation of information, and accuracy. We have found that edge based segmentation works well for phase contrast microscopic images of activated sludge wastewater.

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Morphological analysis of activated sludge flocs and filaments

Cited by 15 publications

References 5 publications

In situ characterization of aggregates of nanoscale zero-valent iron (nZVI) in water: an engineering aspect

In situ characterization of aggregates of nanoscale zero-valent iron (nZVI) in water: an engineering aspect

A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential

Segmentation of Activated Sludge Filaments using Phase Contrast Images

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