Efficient automated method for image-based classification of microbial cells

Monitoring of bacterial populations requires automated analysis tools that provide accurate cell type quantification results. Here, methods for automated image analysis and bacteria type classification are presented. The classification method employs several discriminative features, calculated from automatically segmented images, for class determination. The performance of the algorithm is evaluated with a case study where three different bacterial types are present. Moreover, the accuracy of the method is demonstrated by generating experiments of synthetic bacterial population images.