An ophthalmic information processing apparatus includes a specifying unit and an image deforming unit. The specifying unit is configured to specify a three-dimensional position of each pixel in a two-dimensional front image depicting a predetermined site of a subject's eye, based on OCT data obtained by performing optical coherence tomography on the predetermined site. The image deforming unit is configured to deform the two-dimensional front image, by changing position of at least one pixel in the two-dimensional front image based on the three-dimensional position, to generate a three-dimensional front image.

Systems and methods are provided for measuring the mechanical properties of ocular tissue, such as the lens or corneal tissue, for diagnosis as well as treatment monitoring purposes. A laser locking feedback system is provided to achieve frequency accuracy and sensitivity that facilitates operations and diagnosis with great sensitivity and accuracy. Differential comparisons between eye tissue regions of a patient, either on the same eye or a fellow eye, can further facilitate early diagnosis and monitoring.

The invention relates to a computing system and a computer-implemented method for classifying images of retina of eyes of subjects. A captured image of a retina is processed to obtain a plurality of different segmented images each having different selected portions of the captured image using different selection rules. The multiple segmented images are provided to respective dedicated machine learning models to output an image classification based on the respective segmented images provided as input. An ensemble classification is determined based on the multiple classifications obtained by means of the multiple trained machine learning models.

A computer-implemented method is described. The method is be implemented by processors of an aircraft system. The method includes receiving images of an eye and a lighting configuration associated with a cockpit of an aircraft. The method further includes detecting a position of the eye within each of the images. The method further includes compensating for a pupillary light response of the eye based on the position of the eye within the image and the lighting configuration. By compensating for the pupillary light response, a fatigue level of the operator is estimated with reduced noise.

An eye test administered to a remote subject over a computer network, where sensors provide feedback through the computer network so that light source position, light source brightness and/or a display configuration and/or parameter setting related to a visual display that is used to implement the remote eye test.

In a laser cataract procedure that also corrects for astigmatism, an iris registration method compares an iris image of a patient's eye taken when the eye is not docked to a patient interface device with an iris image of the same eye that is docked to the patient interface, to calculate a rotation angle between the two images. The astigmatism axis of the eye is measured when the eye is not docked, and the measured axis is rotated by the calculated rotation angle to obtain a rotated astigmatism axis relative to the iris image of the docked eye. The laser cataract procedure is performed based on the rotated astigmatism axis. The rotation angle is calculated by optimizing a transformation that transforms the undocked iris image to match the docked iris image, where the transformation includes a dilation factor that accounts for different pupil dilation of the two iris images.

A method of processing a sequence of images of a retina acquired by an ophthalmic device to generate retinal position tracking information indicative of retina movement during acquisition. The method includes (i) receiving one or more images of the retina; (ii) calculating a cross-correlation between a reference image and an image based on the received image(s) to acquire an offset between the image and reference image; and repeating processes (i) and (ii) to acquire, as the tracking information, respective offsets for images that are based on the respective received image(s). Another step includes modifying the reference image during the repeating, by determining a measure of similarity between correspondingly located regions of pixels in two or more received images and accentuating features in the reference image representing structures of the imaged retina in relation to other features in the reference image based on the determined measure of similarity.

An ophthalmic system may comprise an imaging device having a field of view oriented toward the eye of the patient; a patient interface housing defining a passage therethrough, having a distal end coupled to one or more seals configured to be directly engaged with one or more surfaces of the eye of the patient, and wherein the proximal end is configured to be coupled to the patient workstation such that at least a portion of the field of view of the imaging device passes through the passage; and two or more registration fiducials coupled to the patient interface housing in a predetermined geometric configuration relative to the patient interface housing within the field of view of the imaging device such that they may be imaged by the imaging device in reference to predetermined geometric markers on the eye of the patient which may also be imaged by the imaging device.

A alteration caused in a nerve fiber layer could not accurately be displayed. There is provided a tomographic imaging apparatus including a generation means for generating a nerve fiber bundle map, a designation means for designating an arbitrary nerve fiber bundle in the nerve fiber bundle map, and a display control means for causing display means to display a parameter of the designated nerve fiber bundle.

Provided is an image processing apparatus configured to process an image of a fundus of an eye to accurately measure thicknesses of membranes that form a blood vessel wall of an eye. The image processing apparatus includes: an image acquiring unit configured to acquire an image of an eye; a vessel feature acquiring unit configured to acquire membrane candidate points that form an arbitrary wall of a blood vessel based on the acquired image; a cell identifying unit configured to identify a cell that forms the wall of the blood vessel based on the membrane candidate points; and a measuring position acquiring unit configured to identify a measuring position regarding the wall of the blood vessel based on a position of the identified cell.