Discriminating Ramos and Jurkat Cells with Image Textures from Diffraction Imaging Flow Cytometry Based on a Support Vector Machine

Background: The flow cytometry (FCM) has been widely used in both basic and clinical research applications. However, the conventional noncoherent fluorescence and the bright or dark field images acquired spatially integrated and can only yield limited information. Few 3D morphological features of cells can be unveiled. Objective: Diffraction imaging techniques can be used to improve the flow cytometry system and to reflect some 3D morphological features of cells. Method: The newly developed diffraction imaging flow cytometry system (DIFC) in our previous studies could be used to compensate conventional flow cytometries to reflect a cell's 3D morphological features. In this study, we developed a method based on a Support Vector Machine to classify the diffraction images acquired from human acute leukaemia T (Jurkat) cells and Burkitt lymphoma B (Ramos) cells with the diffraction imaging flow cytometry system technique. Results: As a result, an accuracy of 99.38% with MCC value of 0.9875 was achieved in an independent testing dataset, which indicated that the DIFC system could differentiate the cells. Conclusion: It is indicated by the results that strong correlation exists between the characteristic parameters of the images and the 3D morphological features of cells. Since diffraction images correlate strongly to the 3D morphology of cells, this system could be used for studies concerning cellular morphology.