IMAGE QUALITY IMPROVEMENT METHODS FOR OPTICAL COHERENCE TOMOGRAPHY

    公开(公告)号:US20220130021A1

    公开(公告)日:2022-04-28

    申请号:US17647527

    申请日:2022-01-10

    Abstract: Ophthalmological images generated by coherent imaging modalities have multiple types of noise, including random noise caused by the imaging system and speckle noise caused by turbid objects such as living tissues. These noises can occur at different levels in different locations. A noise-reduction method and system of the present disclosure thus relates to applying different filters for different types of noise and/or different locations of images, sequentially or in parallel and combined, to produce a final noise-reduced image.

    3D quantitative analysis with deep learning

    公开(公告)号:US11302006B2

    公开(公告)日:2022-04-12

    申请号:US17103058

    申请日:2020-11-24

    Abstract: A machine learning model is trained to identify the texture difference between the different layers of a multilayer object. By training with data in full 3D space, the resulting model is capable of predicting the probability that each pixel in a 3D image belongs to a certain layer. With the resulting probability map, comparing probabilities allows one to determine boundaries between layers, and/or other properties and useful information such as volume data.

    Image quality improvement methods for optical coherence tomography

    公开(公告)号:US11257190B2

    公开(公告)日:2022-02-22

    申请号:US16797848

    申请日:2020-02-21

    Abstract: Ophthalmological images generated by coherent imaging modalities have multiple types of noise, including random noise caused by the imaging system and speckle noise caused by turbid objects such as living tissues. These noises can occur at different levels in different locations. A noise-reduction method and system of the present disclosure thus relates to applying different filters for different types of noise and/or different locations of images, sequentially or in parallel and combined, to produce a final noise-reduced image.

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