Diffraction imaging flow cytometry is a new biological cell research method developed recently, which can get abundant information of 3D morphology inside the cell without staining. However, the pattern of diffraction image is non-intuitive, and cannot be directly classified by the observer. On the contrary, the bright field images obtained by microscope are clear enough to be directly perceived through the senses by researchers. This paper will introduce a new flow cell imaging design incorporating the merits of these two kinds of methods that can obtain diffraction image and bright field image of the same cell simultaneously, which is based on the infinite microscopic architecture with two optical paths. The first path gets the diffraction image by defocusing the shared object lens, meanwhile, the second path gets the bright-field microscopic image by adjustable lens compensating for the defocus. In the new system diffraction and microscopic images of yeasts were captured with illumination of 532nm laser and 450nm LED respectively. Two classification models were set up for recognition of yeast-budding-state with diffraction images and microscopic images independently by using GLCM (Grayscale Co-occurrence Matrix) feature extraction method, which got the highest 97% accuracy of classification with diffraction images compared to the 94% with microscopic images.
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