Image processing algorithms for waterborne pathogen classification - Luo, Jiawen (Nanyang Technological University, 2022)
Waterborne pathogens are microorganisms in the water that can cause infections, diseases and even deaths. Classification of such microorganisms is essential for water quality monitoring which is important for human health and safety. The conventional biochemical approaches are standard, accurate but have high requirements on monetary, time, and human resources, and usually leave chemical pollutions. With the development of machine learning techniques, algorithms were established for cheaper and simpler microorganism image classification. This PhD study has investigated and developed portable and accurate classification algorithms that are more feasible for portable, real-time applications based on images captured by an optofluidic system, which is smaller and cheaper than microscopes. Most existing microorganism classification algorithms are designed for microscopic images so do not work well on our optofluidic images. Besides, the existing algorithms are mainly focused on classification accuracy but not on the computational cost. Thus, this PhD study aims to develop image processing algorithms for waterborne pathogen classification that have the potential for portable, real-time water quality monitoring applications. This PhD study has made three contributions. The first contribution is the proposed EHHOG (entropy of histogram of HOG) (HOG: histogram of oriented gradients) method, combining some existing morphological features extracted from binarized images and the proposed feature EHHOG extracted from greyscale images, for waterborne pathogen classification. The second contribution is proposing the Multi-Region Binarization method to capture the information missed by the existing binarization methods and developing the Binarized- Greyscale-Hybrid Algorithm with Multi-Region Binarization (BiGHAM) method that improved the classification accuracy from the EHHOG method. The third contribution is the proposed improved-BiGHAM (iBiGHAM) method with a new circle-based region partitioning for Multi-Region Binarization and a new way to apply the mutual-information-based feature selection. The iBiGHAM method further increased the classification accuracy with simpler trial procedures than the BiGHAM method. It could achieve similar or higher accuracy than the existing algorithms including the deep-learning-based approach, while the computational complexity was much lower. Moreover, these proposed algorithms were adapted and applied to images in an open-sourced dataset, also achieving better classification performance than the existing algorithms.