Microsc. Microanal. 23, 1130–1142, 2017 doi:10.1017/S1431927617012673
© MICROSCOPY SOCIETY OF AMERICA 2017
Segmentation Approach Towards Phase-Contrast Microscopic Images of Activated Sludge to Monitor theWastewater Treatment
Muhammad Burhan Khan, Humaira Nisar,* Choon Aun Ng, Kim Ho Yeap, and Koon Chun Lai Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar, Perak 31900, Malaysia
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.
Key words: phase-contrast microscopy, activated sludge, image segmentation, segmentation assessment, filamentous bacteria
INTRODUCTION
The activated sludge (AS) process is commonly used to treat domestic and industrial effluent in the wastewater treatment plants. The performance of the process is conventionally monitored by physico-chemical procedures that are time- consuming, laborious, and have associated environmental hazards (Boztoprak et al., 2015; Mesquita et al., 2016). Image processing and analysis is a clean, time-efficient, and semi- automated alternative to monitor AS in a wastewater treat- ment plant (Mesquita et al., 2013; Khan et al., 2015a,2015b). The performance of the AS process depends on the settling ability of flocs, and the presence of filamentous bacteria, in the secondary clarifier of the wastewater treatment plant. The flocs are microbial aggregates that compose of live and dead microorganisms, and their metabolism products (Jenkins et al., 2003). The filamentous bacteria are also refer- red to as filaments in this paper. The visual representation of the flocs and filaments is shown in Figure 1 using a phase- contrast microscopic (PCM) image of AS. The settling ability of a floc depends on its morphological structure. The fila- ments form the backbone of the floc structure (Bitton, 2005). The presence of filaments at a certain concentration improves the compactness of a floc, and consequently it settling ability. However, if the filaments are present in a large amount, they
*Corresponding Author.
humaira@utar.edu.my Received May 16, 2017; accepted October 25, 2017
increase the porosity of the flocs and cause themto float. As a result, the flocs do not settle properly, affecting the quality of the effluent of the AS wastewater treatment plant. The mor- phology and quantification of the flocs and filaments can be monitored and parametrized by using image processing and analysis. For example, the total length of filament is an important parameter determined by image processing. It is used to model the sludge volume index that is one of the most important parameters used to monitor the AS process (Amaral et al., 2013). Therefore, image segmentation should be able to calculate the length of the filaments accurately. Image processing and analysis has been used in the con-
field and fluorescence microscopy along with staining proce- dures (Bitton, 2005; Mesquita et al., 2013). A number of
text of AS from two perspectives. First, it is used to model physico-chemical measurement using image analysis para- meters (Amaral et al., 2013; Boztoprak et al., 2015; Khan et al., 2016). Second, it is employed to identify the state of the AS plant using classification models without predicting the physico-chemical measurements (Khan et al., 2017). The first approach results into models that are plant-specificand show deviation if the state of the plant changes. The second approach can be generalized to multiple plants irrespective of their states. TheAS images to be processed are acquired using bright-
segmentation techniques have been suggested to segment flocs and filaments in the bright-field AS images corrupted by irregular illumination (Khan et al., 2014; Lee et al., 2014;Khan et al., 2015a,2015b). Fluorescence microscopy and staining
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148 |
Page 149 |
Page 150 |
Page 151 |
Page 152 |
Page 153 |
Page 154 |
Page 155 |
Page 156 |
Page 157 |
Page 158 |
Page 159 |
Page 160 |
Page 161 |
Page 162 |
Page 163 |
Page 164 |
Page 165 |
Page 166 |
Page 167 |
Page 168 |
Page 169 |
Page 170 |
Page 171 |
Page 172 |
Page 173 |
Page 174 |
Page 175 |
Page 176 |
Page 177 |
Page 178 |
Page 179 |
Page 180
