Segmentation and Quantification of Activated Sludge Flocs for Wastewater Treatment

Activated sludge process is commonly used in wastewater treatment plants to process domestic or industrial effluent. The main objects of interest in the activated sludge systems are flocs and filamentous organisms. The proper settling of the sludge flocs in the activated sludge wastewater treatment process is crucial to the normal functioning of the system. In this paper image processing techniques are used to segment and detect activated sludge flocs in microscopic images of activated sludge. This can be helpful in the study of the morphology of flocs and their quantification. In this paper, Otsu thresholding, k-means and fuzzy c-means segmentation techniques are used to segment and detect flocs in microscopic images of activated sludge. The performance of the segmentation techniques is assessed for activated sludge images at different microscopic magnifications using global consistency error (GCE. Ground truth images are used to benchmark the accuracy of segmentation algorithms. Otsu thresholding method performed better segmentation in terms GCE. But the performance of segmentation deteriorates at higher magnifications. The quantification of flocs also provides a means for assessing the segmentation performance of different algorithms. Otsu thresholding has better quantification performance as compared to fuzzy c-means and k-means, with some apparent exceptions caused by imperfect segmentation of images at 40 times magnification.