An mRMR-SVM Approach for Opto-Fluidic Microorganism Classification

The detection of microorganisms is important in numerous applications such as water quality monitoring, blood analysis, and food testing. The conventional detection methods are tedious and labour-intensive. Establish methods involve culturing, counting and identification of the pathogen by an experienced technician which typically can take several days. The use of opto-fluidic technology to capture microorganism images offers 0 route to reduce the overall assay time. However, the detection still requires a trained technician. This paper proposes an image processing method that can be used to classify microorganism images captured by an opto-fluidic set up in an automatic manner. The proposed algorithm incorporates some of the features used in other microorganism image detection methods and proposes two new features-Entropy of Histogram of Oriented Gradients (HOG) and the filtered intensities. In addition, we propose to apply the minimal-Redundancy-Maximal-Relevance (mRMR) criterion to select and rank these features. The probability and joint probability distribution functions of the mRMR are estimated using a Gaussian model and the Kernel Density Estimation model. The performance of the proposed method was validated using SVM and data collected from an experimental setup. The results show that our proposed method outperforms existing methods and is capable of achieving a classification accuracy up to 95.8%.