Automatic detection of unstained viable cells in bright field images using a support vector machine with an improved training procedure
Detection of unstained viable cells in bright field images is an inherently difficult task due to the immense variability of cell appearance. Traditionally, it has required human observers. However, in high-throughput robotic systems, an automatic procedure is essential. In this paper, we formulate viable cell detection as a supervised, binary pattern recognition problem and show that a support vector machine (SVM) with an improved training algorithm provides highly effective cell identification. In the case of cell detection, the binary classification problem generates two classes, one of which is much larger than the other. In addition, the total number of samples is extremely large. This combination represents a difficult problem for SVMs. We solved this problem with an iterative training procedure (Compensatory Iterative Sample Selection, CISS). This procedure, which was systematically studied under various class size ratios and overlap conditions, was found to outperform several commonly used methods, primarily owing to its ability to choose the most representative samples for the decision boundary. Its speed and accuracy are sufficient for use in a practical system.
support vector machines cell detection structural risk minimization kernel functions