Rapid classification of micron-sized particles of sphere, cylinders and ellipsoids by diffraction image parameters combined with scattered light intensity

Abstract

Abstract Spatial distributions of light scattered by single particles correlate closely with their morphologies in terms of refractive index (RI) distribution. Diffraction imaging of scattered light under coherent excitation presents a unique approach to acquire and extract feature parameters for particle classification. A validated method has been applied in this study to accurately simulate diffraction imaging of light scattered by homogeneous particles and obtain calculated diffraction image (DI) data. The feature parameters of DI data have been extracted by the gray-level co-occurrence matrix (GLCM) algorithm. We have developed an unsupervised machine learning algorithm based on Gaussian mixture model (GMM) to classify 1965 particles made of single and double spheres, cylinders and ellipsoids with varied RI values in parameter space. It has been shown that selected GLCM parameters combined with integrated forward scatter intensity can provide effective markers for accurate and morphology based classification. For 1791 particles of the same RI, the mean accuracy values of classifying particles into 3 particle types range from 82.6% to 97.2%. These results demonstrate the strong potential of diffraction imaging method for rapid analysis and classification of nonspherical and homogeneous particles by the GMM classifiers that is very challenging in comparison to distinguishing biological cell types.