Pattern recognition and classification of two cancer cell lines by diffraction imaging at multiple pixel distances

• Cross-polarized diffraction images allow label-free cell classification.
• GLCM yields accurate and effective features for automated classification by SVM.
• Consistent accuracies are up to 99.8% on training and up to 99.5% on 3 test sets.
• Effects of image blur on classification have been quantitatively analyzed.
• Results indicate diffraction imaging flow cytometry as a powerful cell assay tool.

Rapid and label-free imaging methods for accurate cell classification are highly desired for biology and clinical research. To improve consistency of classification performance, we have developed an approach of pattern analysis by gray level co-occurrence matrix (GLCM) algorithm to extract textural features at multiple pixel distances from cross-polarized diffraction image (p-DI) pairs, which were acquired with a method of polarization diffraction imaging flow cytometry using one time-delay-integration camera for significantly reduced blurring. Support vector machine (SVM) based classification was performed to discriminate HL-60 from MCF-7 cells using the GLCM features and consistency of optimized SVM classifiers was evaluated on three test data sets. It has been shown that the classification accuracy of the best performing SVM classifiers at or above 98.0% can be achieved among all four data sets for each of the three incident beam polarizations. These results suggest that the p-DI pair data provide a new platform for rapid and label-free classification of single cells with high and consistent accuracy.

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