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
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.