Microorganism image classification with circle-based Multi-Region Binarization and mutual-information-based feature selection

Abstract

BackgroundAn important part of water quality control is waterborne pathogen detection. Conventional microorganism detections are done by costly and complicated biochemical approaches, which leave chemical pollutions and require professional laboratories, technicians, and much time. Recently, machine-learning-based image classification algorithms are developed for faster, cheaper, simpler, and more environmentally friendly microorganism detection. Most of them are designed for conventional microscopic images. On the other hand, the optofluidic system is a smaller and cheaper alternative to conventional microscopes, so we aim to propose a microorganism classification algorithm for optofluidic images. MethodIn our previous study, the Binarized-Greyscale-Hybrid Algorithm with Multi-Region Binarization (BiGHAM) algorithm was developed for our optofluidic image dataset. In this paper, a new circle-based region partition method for the Multi-Region Binarization and a new mutual-information-based feature selection method were proposed to improve the BiGHAM method. Additionally, an implementation example of how to adapt and apply our method on an open dataset was presented. Results and conclusionWith the circle-based region partition method and the new feature combination selected based on mutual information, the improved-BiGHAM algorithm achieved higher classification accuracies, and the algorithm is simpler than the previous BiGHAM method during training. For our optofluidic image dataset, the accuracy improvement for the 1000-image-per-class case was from 94.8 to 96.3%, and for the 30-image-per-class case was from 77.8 to 85.6% with the support vector machine as the classifier. The adapted algorithm also works well on the open dataset, showing the potential to be extended to applications on different microorganism images.