Illumination Compensated Segmentation of Microscopic Images of Activated Sludge Flocs

Image processing and analysis is a useful tool for monitoring of activated sludge wastewater treatment plant. However its effectiveness is dependent on performance of the segmentation algorithms. The activated sludge plant is monitored by image processing and analysis of images acquired through trinocular microscope. The sample observed under microscope is collected from aeration tank of the plant. In this paper, a segmentation technique with integrated illumination compensation is proposed for the microscopic images of the activated sludge samples. The illumination noise was modeled and estimated as Gaussian distribution symmetric about a threshold value determined by global Otsu thresholding algorithm. The performance of the algorithm was evaluated using time required for segmentation, Rand index, accuracy and quantification of flocs. In order to compare with the state-of-the-art algorithms, gold approximations of ground truth images were manually prepared. The performance was assessed by combining the evaluation metrics in an integrated perspective. The proposed algorithm exhibits better performance in terms of both integrated and non-integrated perspectives.