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Related Concept Videos

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Full-Field Optical Coherence Microscopy for Histology-Like Analysis of Stromal Features in Corneal Grafts
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Restoration of Optical Coherence Images of Living Tissue Using the CLEAN Algorithm.

J M Schmitt

    Journal of Biomedical Optics
    |September 28, 2012
    PubMed
    Summary

    This study adapted the CLEAN algorithm from radio astronomy for optical coherence tomography (OCT) imaging. The enhanced CLEAN algorithm improves resolution and reveals hidden tissue morphology in OCT images, even with speckle noise.

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    Published on: August 21, 2019

    Area of Science:

    • Biomedical Optics
    • Image Processing
    • Medical Imaging

    Background:

    • Optical coherence tomography (OCT) is a key imaging modality for biological tissues.
    • Image resolution and artifact reduction are critical challenges in OCT.
    • Existing deconvolution algorithms may not fully address the complexities of scattering media.

    Purpose of the Study:

    • To adapt and evaluate the CLEAN algorithm for restoring optical coherence tomography (OCT) images of biological tissue.
    • To assess the CLEAN algorithm's effectiveness in improving image resolution and revealing subsurface tissue morphology.
    • To investigate the algorithm's performance in the presence of speckle noise and densely packed scatterers.

    Main Methods:

    • Adapted the CLEAN iterative point-deconvolution algorithm for OCT imaging.
    • Derived the CLEAN deconvolution kernel from the OCT scanner's point-spread function, incorporating an inverse Wiener filter.
    • Evaluated the algorithm on images from a prototype OCT scanner with speckle-reduction hardware, using scattering phantoms and living tissue.

    Main Results:

    • The CLEAN algorithm significantly improved feature resolution in OCT images of scattering phantoms and biological tissues.
    • Restored images revealed detailed tissue morphology previously not evident in unprocessed images.
    • The algorithm demonstrated robustness against speckle noise and graceful performance degradation with increasing scatterer density.

    Conclusions:

    • The adapted CLEAN algorithm is effective for enhancing OCT image quality, improving resolution, and visualizing tissue structures.
    • CLEAN offers a promising approach for overcoming limitations in OCT imaging of complex biological samples.
    • Further improvements could leverage amplitude and phase information for even more advanced OCT image restoration.