An image processing method performed by a processor and including: a step of acquiring a choroidal vascular image; a step of detecting a vortex vein position from the choroidal vascular image; a step of identifying a choroidal vessel related to the vortex vein position; and a step of finding a size of the choroidal vessel.

Provided herein are methods of detecting a ophthalmologic genetic disease in a subject that include matching properties of captured images and/or videos with properties of an ocular pathology model that is trained on a plurality of reference images and/or videos of ocular cells of reference subjects, which properties of the ocular pathology model are indicative of the pathology. Related systems and computer program products are also provided.

A slit lamp microscope of an aspect example includes a scanner and a data processor. The scanner is configured to scan an anterior segment of a subject's eye with slit light to collect a plurality of cross sectional images. The data processor is configured to generate opacity distribution information that represents a distribution of an opaque area in a crystalline lens, based on the plurality of cross sectional images collected by the scanner.

Methods, systems, and storage media for projecting a viewer-specific 3D object perspectives from a single 3D display are disclosed. Implementations may: acquire face and eye region image data of a plurality of viewers within a field of view of at least one camera associated with a 3D-enabled digital display; analyze the eye region image data to determine at least one 3D eye position, at least one eye state, at least one gaze angle, and at least one point-of-regard for a viewer relative to at least one camera associated with the 3D-enabled digital display; and calculate a plurality of processed image projections for display by the single 3D display. The digital-processing of input image projection enables a separate optical input to the user's eyes, and by the use of visual-acuity pre-processing of the image—via visual-field kernel, enables the treatment of eye abbreviations, including an Amblyopic-eye without the need for any additional eye-ware, or head-up-displays (HMD's).

A method, system, and computer-readable medium, for detecting whether an eye blink or non-blink is captured in the image. The method includes filtering, from the image, one or more objects that are predicted to be unsuitable for determining whether an eye blink or no-blink is captured in the image, to provide a filtered image. The method also includes correlating the filtered image with a reference image, and determining, based on the correlating, whether the eye blink or non-blink is captured in the image. The eye blink is a full eye blink or a partial eye blink, and the images may be sequentially captured IR SLO images, in one example embodiment herein. Images determined to include an eye blink can be omitted from inclusion in a final (e.g., OCT) image.

In order to reduce a radiation dose delivered to an object or an observer, a facility for monitoring handling of the object has an optical unit configured to direct ionizing radiation onto the object and also a filter element in order to attenuate a part of the ionizing radiation. An imaging unit may detect portions of the ionizing radiation passing through the object in order to create an image of the object. A view acquisition system may acquire a viewing movement, and a control unit is configured, during a first operating mode, to control a position of the filter element as a function of the viewing movement. The control unit is configured to identify a predefined sequence of viewing movements and, as a function thereof, to switch into a second operating mode. The position of the filter element is controlled during the second operating mode as a function of an image analysis.

Systems and methods relate to processing optical tomography coherence (OCT) images to predict characteristics of a treatment to be administered to effectively treat age-related macular degeneration. The processing can include pre-processing the image by flattening and/or cropping the image and processing the pre-processed image using a neural network. The neural network can include a deep convolutional neural network. An output of the neural network can indicate a predicted frequency and/or interval at which a treatment (e.g., anti-vascular endothelial growth factor therapy) is to be administered so as to prevent leakage of vasculature in the eye.

A system for and a method of analyzing a corneal lesion using an anterior segment image according to the present invention. The system includes: an image acquisition unit configured to acquire an anterior segment image from the eyeball of a subject, a feature extractor configured to extract feature information on a position and a cause of a lesion in the cornea from the anterior segment image by applying a convolution layer to the anterior segment image through machine learning on the basis of a database in which clinical information pre-acquired by analyzing positions and causes of lesions in the corneas of subjects is stored; and a result determination unit configured to identify a position of the cornea from the anterior segment image using the feature information and to analyze and determine the position and the cause of the lesion in the cornea from the position of the cornea.

An ophthalmic image of an evaluation target is acquired, a plurality of subsection images is extracted from the ophthalmic image, a state of a subject's eye is predicted for each of the subsection images based on a learned model in which learning has been performed in advance regarding extracting a plurality of subsection images from an ophthalmic image for learning, and predicting a state of a subject's eye for the each of subsection image by machine learning using correct answer data related to a state of each subsection image, and the subsection image is extracted from the ophthalmic image so as to have an image size corresponding to a state of a subject's eye of an evaluation target.

A diagnosis method includes: (a) checking tear film break-up time point and location by photographing cornea of the subject's eye and checking in time series at least one or more times of the tear film break-up time point; (b) checking corneal surface temperature by measuring the surface temperature of the cornea of the subject to be evaluated using a thermal imaging camera performed simultaneously with the photographing of the tear film of the eye; (c) mapping the tear film break-up time point and the change in the surface temperature of the cornea based on time; and (d) diagnosing type of dry eye syndrome based on any one of the tear film break-up time point and a location of surface temperature change time point of the corneal corresponding thereto, in mapping result in step (c).