Automated and Precise Event Detection Method for Big Data in Biomedical Imaging with Support Vector Machine

This paper proposes a machine learning based method which can detect certain events automatically and precisely in biomedical imaging. We detect one important and not well-defined event, which is called flash, in fluorescence images of Escherichia coli. Given a time series of images, first we propose a scheme to transform the event detection on region of interest (ROI) in images to a classification problem. Then with supervised human labeling data, we develop a feature selection technique to utilize support vector machine (SVM) to solve this classification problem. To reduce the time in training SVM model, a parallel version of SVM training is implemented. On ten stacks of fluorescence images labeled by experts, each of which owns one hundred 512 ·512 images with in total 4906 ROIs and 72056 labeled events, event detection with proposed method takes 19 seconds, while human labeling roughly costs 60 hours. With human labeling as the standard, the accuracy of our method achieves an F-value of about 0.81. This method is much faster than human detection and expects to be more precise with bigger data. It also can be expanded to a series of event detection with similar properties and improve efficiency of detection greatly.