Automated classification of biological cells according to their 3D morphology is highly desired in a flow cytometer setting. We have investigated this possibility experimentally and numerically using a diffraction imaging approach. A fast image analysis software based on the gray level co-occurrence matrix (GLCM) algorithm has been developed to extract feature parameters from measured diffraction images. The results of GLCM analysis and subsequent classification demonstrate the potential for rapid classification among six types of cultured cells. Combined with numerical results we show that the method of diffraction imaging flow cytometry has the capacity as a platform for high-throughput and label-free classification of biological cells
