Abstract: Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

Abstract: System and method for fully automated end-to-end eye screening with automated medical acquisition and analysis. The system includes an eye imaging device, a mechanism that moves the imaging device, a computing platform that guides the movement mechanism, a user interface, and an electronic display device and/or printer to provide the screening, monitoring, and/or diagnosis report.

Abstract: Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

Abstract: Disclosed are example embodiments of a system of retinal three-dimensional (3D) imaging. The system of retinal 3D imaging includes an image sensor within a light path and a reimaging corrective optics module within the light path. The system of retinal 3D imaging also includes an objective lens in the light path and a baffle-and-illumination module in the light path. In an aspect, the reimaging corrective optics module is in front of the image sensor, the objective lens is in front of the reimaging corrective optics module, and the baffle-and-illumination module is between the objective lens and the reimaging corrective optics module.

Abstract: System and method for fully automated end-to-end eye screening with automated medical acquisition and analysis. The system includes an eye imaging device, a mechanism that moves the imaging device, a computing platform that guides the movement mechanism, a user interface, and an electronic display device and/or printer to provide the screening, monitoring, and/or diagnosis report.

Abstract: Various embodiments for eye imaging, screening, diagnosis, and monitoring are provided. Some embodiments comprise systems for retinal imaging capable of capturing multiple high resolution images using multiple quick succession flashes. The images can be combined to provide for a complete image of the retina of a target FOV without corneal reflections. In some embodiments, a system for retinal imaging can utilize an optical design pupil layout that allocates a large area for the path of imaging rays on the eye pupil by using a narrow buffer area. In other embodiments, a system for retinal imaging can comprise a main device, including an imaging and sensing device, and a robotic system, including an eye tracker and/or sensors, wherein the robotic system is capable of automatically aligning the main device and selecting an optimal imaging mode by detecting a pupil size of an eye and is combined with an AI analysis tool.

Abstract: System and method for fully automated end-to-end eye screening with automated medical acquisition and analysis. The system includes an eye imaging device, a mechanism that moves the imaging device, a computing platform that guides the movement mechanism, a user interface, and an electronic display device and/or printer to provide the screening, monitoring, and/or diagnosis report.

Abstract: System and method for fully automated end-to-end eye screening with automated medical acquisition and analysis. The system includes an eye imaging device, a mechanism that moves the imaging device, a computing platform that guides the movement mechanism, a user interface, and an electronic display device and/or printer to provide the screening, monitoring, and/or diagnosis report.

Abstract: Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The systems and methods may allow, for example, for automated identification and localization of lesions and other anatomical structures from medical data obtained from medical imaging devices, computation of image-based biomarkers including quantification of dynamics of lesions, and/or integration with telemedicine services, programs, or software.

Abstract: Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The design includes a system and method for accessing retinal images related to a patient, each retinal image including a plurality of pixels. For each of the retinal images, the system designates a first set of the plurality of pixels as active pixels including interesting retinal image regions and computes a first vector of numbers comprising pixel-level descriptors for each of the active pixels. The system computes a second vector of numbers for each of the retinal images and provides a second classification using supervised learning.

Abstract: Methods and apparatus for video object segmentation are provided, suitable for use in a super-resolution system. The method comprises alignment of frames of a video sequence, pixel alignment to generate initial foreground masks using a similarity metric, consensus filtering to generate an intermediate foreground mask, and refinement of the mask using spatio-temporal information from the video sequence. In various embodiments, the similarity metric is computed using a sum of squared differences approach, a correlation, or a modified normalized correlation metric. Soft thresholding of the similarity metric is also used in one embodiment of the present principles. Weighting factors are also applied to certain critical frames in the consensus filtering stage in one embodiment using the present principles.

Abstract: A method/system of transcoding an MPEG 2/4 bit stream into an H.264 format, handles an input MPEG 2/4 bit stream in a decoder, and identifies certain data in the input bit stream for reuse in the H.264 format; and, reuses the identified data in a re-encoder with assistance from a mapping module in transcoding by converting the input bit stream into an output H.264 format. The identified data includes information at a macrolevel and information at a picture level. The information at the macrolevel might comprise additional stages incorporated in the re-encoder module including a Mapping Process stage, a Sub Pixel Refinement stage, a Mode Selection stage to choose from Intra, Inter or Skip modes, followed by the standard H.264 encoding loop and the Entropy Coding Block. The information at the picture level might include; a) average quantizer of frame, and, b) total bits per frame.

Abstract: Methods and apparatus for video object segmentation are provided, suitable for use in a super-resolution system. The method comprises alignment of frames of a video sequence, pixel alignment to generate initial foreground masks using a similarity metric, consensus filtering to generate an intermediate foreground mask, and refinement of the mask using spatio-temporal information from the video sequence. In various embodiments, the similarity metric is computed using a sum of squared differences approach, a correlation, or a modified normalized correlation metric. Soft thresholding of the similarity metric is also used in one embodiment of the present principles. Weighting factors are also applied to certain critical frames in the consensus filtering stage in one embodiment using the present principles.

Abstract: A method/system of transcoding an MPEG 2/4 bit stream into an H.264 format, handles an input MPEG 2/4 bit stream in a decoder, and identifies certain data in the input bit stream for reuse in the H.264 format; and, reuses the identified data in a re-encoder with assistance from a mapping module in transcoding by converting the input bit stream into an output H.264 format. The identified data includes information at a macrolevel and information at a picture level. The information at the macrolevel might comprise additional stages incorporated in the re-encoder module including a Mapping Process stage, a Sub Pixel Refinement stage, a Mode Selection stage to choose from Intra, Inter or Skip modes, followed by the standard H.264 encoding loop and the Entropy Coding Block. The information at the picture level might include; a) average quantizer of frame, and, b) total bits per frame.