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Automatic multicell identification using a compact lensless single and double random phase encoding system.

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    Compact lensless systems using random phase encoding enable automatic cell identification. This method generates unique opto-biological signatures for accurate classification of single or mixed cell types.

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    Area of Science:

    • Optics and Photonics
    • Biotechnology
    • Computational Biology

    Background:

    • Automated cell identification is crucial in biological research and diagnostics.
    • Traditional microscopy methods can be limited by lens aberrations and numerical aperture constraints.
    • Lensless imaging offers potential for compact and cost-effective optical systems.

    Purpose of the Study:

    • To investigate the efficacy of single random phase encoding (SRPE) and double random phase encoding (DRPE) systems for automatic cell identification.
    • To evaluate the performance of these lensless systems in identifying multiple cells, including mixed populations.
    • To demonstrate the feasibility of using opto-biological signatures (OBSs) for cell classification.

    Main Methods:

    • Inputting microscope slides into SRPE and DRPE systems illuminated by coherent or partially coherent light.
    • Generating unique opto-biological signatures (OBSs) captured by an image sensor.
    • Extracting statistical features (mean, standard deviation, skewness, kurtosis, entropy, Pearson's correlation) from OBSs in spatial and frequency domains.
    • Utilizing a random forest classifier for cell identification based on extracted features.

    Main Results:

    • Both SRPE and DRPE systems successfully demonstrated cell identification capabilities.
    • The lensless nature of the systems eliminates numerical aperture limitations, leading to compact designs.
    • Statistical features extracted from OBSs enabled accurate classification of cell types.
    • This study represents the first reported use of DRPE for automated cell identification, distinct from its typical application in image encryption.

    Conclusions:

    • Compact, lensless SRPE and DRPE systems are effective for automated cell identification.
    • The opto-biological signature (OBS) approach combined with random forest classification offers a robust method for analyzing cell populations.
    • Random phase encoding presents a novel and promising technique for advanced biological imaging and analysis.