432 results about "Retinal image" patented technology

A combination of a scanning laser ophthalmoscope and external laser sources (52) is used for microphotocoagulation and photodynamic therapy, two examples of selective therapeutic laser. A linkage device incorporating a beamsplitter (56) and collimator-telescope (60) is adjusted to align the pivot point (16) of the scanning lasers (38, 40) and external laser source (52). A similar pivot point minimizes wavefront aberrations, enables precise focusing and registration of the therapeutic laser beam (52) on the retina without the risk of vignetting. One confocal detection pathway of the scanning laser ophthalmoscope images the retina. A second and synchronized detection pathway with a different barrier filter (48) is needed to draw the position and extent of the therapeutic laser spot on the retinal image, as an overlay (64). Advanced spatial modulation increases the selectivity of the therapeutic laser. In microphotocoagulation, an adaptive optics lens (318) is attached to the scanning laser ophthalmoscope, in proximity of the eye. It corrects the higher order optical aberrations of the eye optics, resulting in smaller and better focused applications. In photodynamic therapy, a spatial modulator (420) is placed within the collimator-telescope (60) of the therapeutic laser beam (52), customizing its shape as needed. A similar effect can be obtained by modulating a scanning laser source (38) of appropriate wavelength for photodynamic therapy.

A multimodal biometric identification system captures and processes images of both the iris and the retina for biometric identification. Another multimodal ocular system captures and processes images of the iris and/or the from both eyes of a subject. Biometrics based on data provided by these systems are more accurate and robust than using biometrics that include data from only the iris or only the retina from a single eye. An exemplary embodiment emits photons to the iris and the retina of both eyes, an iris image sensor that captures an image of the iris when the iris reflects the emitted light, a retina image sensor that captures an image of the retina when the retina reflects the emitted light, and a controller that controls the iris and the retina illumination sources, where the captured image of the iris and the captured image of the retina contain biometric data.

The present application provides methods and devices for diagnosing and/or predicting the presence, progression and/or treatment effect of a disease characterized by retinal pathological changes in a subject.

Provide are systems methods and devices for diagnosing disease in medical images. In certain aspects, disclosed is a method for training a neural network to detect features in a retinal image including the steps of: a) extracting one or more features images from a Train_0 set, a Test_0 set, a Train_1 set and a Test_1 set; b) combining and randomizing the feature images from Train_0 and Train_1 into a Training data set; c) combining and randomizing the feature images from Test_0 and Test_1 into a testing dataset; d) training a plurality of neural networks having different architectures using a subset of the training dataset while testing on a subset of the testing dataset; e) identifying the best neural network based on each of the plurality of neural networks performance on the testing data set; f) inputting images from Test_0, Train_1, Train_0 and Test_1 to the best neural network and identifying a limited number of false positives and false negative and adding the false positives and false negatives to the training dataset and testing dataset; and g) repeating steps d)-g) until an objective performance threshold is reached.

Obtaining spectral images of an eye includes taking an optical system that images eye tissue onto a digital sensor array and optically fitting a multi-spectral filter array and the digital sensor array, wherein the multi-spectral filter array is disposed between the digital sensor array and an optics portion of the optical system. The resulting system facilitates acquisition of a snap-shot image of the eye tissue with the digital sensor array. The snap shot images support estimation of blood oxygen saturation in a retinal tissue. The resulting system can be based on a non-mydriatic fundus camera designed to obtain the retinal images without administration of pupil dilation drops.

Systems and methods of obtaining and recording fundus images by minimally trained persons, which includes a camera for obtaining images of a fundus of a subject's eye, in combination with mathematical methods to assign real time image quality classification to the images obtained based upon a set of criteria. The classified images will be further processed if the classified images are of sufficient image quality for clinical interpretation by machine-coded and/or human-based methods. Such systems and methods can thus automatically determine whether the quality of a retinal image is sufficient for computer-based eye disease screening. The system integrates global histogram features, textural features, and vessel density, as well as a local non-reference perceptual sharpness metric. A partial least square (PLS) classifier is trained to distinguish low quality images from normal quality images.

An apparatus for obtaining a retinal image of an eye has a control logic processor (214) for executing a sequence of operations for obtaining the image. A visual target (162) orients the eye of a patient when viewed. An indicator element (410) provides a signal that indicates that the patient is in position. A cornea focus detection section (450) indicates cornea focus, in cooperation with the control logic processor (214). An alignment actuator aligns the optical path according to a signal obtained from the cornea focus detection section (450). A retina focus detection section (452) detects retina focus in cooperation with the control logic processor (214). A focusing actuator (406) is controlled by instructions from the control logic processor (214) according to a signal obtained from the retina focus detection section (452). An image capture light source is energized by the control logic processor (214) for illuminating the retina.

It is possible to estimate optical characteristic according to a pupil diameter in daily life of an examinee, correction data near to the optimal prescription value, eyesight, and sensitivity. A calculation section receives measurement data indicating refractive power distribution of an eye to be examined and pupil data on the eye and calculates lower order and higher order aberrations according to the measurement data and the pupil data (S101 to 105). For example, a pupil edge is detected from the anterior ocular segment image and a pupil diameter is calculated. By using this pupil diameter, lower order and higher order aberrations are calculated. According to the lower order and higher order aberrations obtained, the calculation section performs simulation of a retina image by using high contrast or low contrast target and estimates the eyesight by comparing the result to a template and/or obtains sensitivity (S107). Alternatively, according to the lower order and the higher order aberraations obtained, the calculation section calculates an evaluation parameter indicating the quality of visibility by the eye to be examined such as the Strehl ratio, the phase shift (PTF), and the visibility by comparison of the retina image simulation with the template. According to the evaluation parameter calculated, the calculation section changes the lower order aberration amount so as to calculate appropriate correction data for the eye to be examined (S107). The calculation section outputs data such as the eyesight, sensitivity, correction data, and the simulation result to a memory or a display section (S109).

A method of predicting clinical performance of an ophthalmic optical correction using simulation by imaging a series of objects of different sizes by each of a plurality of eye optical systems, each of the eye optical systems including the ophthalmic optical correction, the method providing an output value representing the resolution and contrast performance of the optical design at that vergence for the eye optical systems.

This invention permits retinal images to be acquired at high speed and with unprecedented resolution in three dimensions (4×4×6 μm). The instrument achieves high lateral resolution by using adaptive optics to correct optical aberrations of the human eye in real time. High axial resolution and high speed are made possible by the use of Fourier-domain optical coherence tomography. Using this system, we have demonstrated the ability to image microscopic blood vessels and the cone photoreceptor mosaic.

A vision metric, called the sharpness metric, indicates the subjective sharpness of a patient's vision by taking into account both the wavefront aberration and the retinal response to the image. A retinal image quality function such as the point spread function is convolved by a neural quality function, and the maximum of the convolution over the retinal plane provides the sharpness metric. The sharpness metric can be used to control eye surgery or the fabrication of a lens.

The invention discloses a retinal vessel segmentation method based on combination of deep learning and a traditional method and relates to the fields of computer vision and mode recognition. According to the method, two grayscale images are both used as training samples of a network, corresponding data amplification, including elastic deformation, smooth filtering, etc., is done against the problem of less retinal image data, and wide applicability of the method is improved. According to the method, an FCN-HNED retinal vessel segmentation deep network is constructed, an autonomous learning process is realized to a great extent through the network, convolutional features of a whole image can be shared, feature redundancy can be reduced, the category of multiple pixels can be recovered from the abstract features, a CLAHE graph and a gauss matched filtering graph of the retinal vessel image are input into the network, an obtained vessel segmentation graph is subjected to weighted average, and therefore a better and more intact retinal vessel segmentation probability graph is obtained. Through the processing mode, the robustness and accuracy of vessel segmentation are improved to a great extent.

A method and system capture an image of the interior of the eye, for example the retina and determine whether the captured image is sufficient to provide data for identifying an individual or animal before attempting to generate the identification data. If the captured image is not sufficient, the method and system automatically capture another image of the interior of the eye.

The invention is directed to an optical system for a fundus camera for reflection-free opthalmoscopy having a beam path with refractive and reflective optical elements which are used substantially in common for illumination and observation or recording. An imaging mirror system substantially comprising a plurality of reflecting optical elements in the form of mirrors and is provided for illuminating and imaging the fundus. At least one optical element, for example, mirror, is formed as a freeform mirror with an imaging, reflecting freeform surface. The optical elements are arranged in a housing in a precisely defined position and attitude relative to one another in such a way that an imaging of the reflecting surfaces of the optical elements on the image of the imaged retina is prevented within a wide diopter range of the patient's eye to be examined.

The invention discloses a retinal blood vessel segmentation method based on the deep-learning adaptive weight, comprising: performing sample expansion on retinal blood vessel images and grouping the samples; constructing a full-convolution neural network for blood vessel segmentation; pre-training the network through the training samples; performing the global adaptive weight segmentation on the retinal blood vessel images; obtaining the initial model parameters for retinal blood vessel segmentation; adding a conditional random field layer to the last network layer and optimizing the network; using the rotation test method to input the testing samples to the network; and obtaining the retinal blood vessel segmentation result. The blood vessel segmentation full-convolution neural network structure and the adaptive weight method proposed by the invention can realize the human-eye level image segmentation and can test on two internationally published retinal image databases DRIVE and CHASE_DB1 with the average accuracy of 96.00 % and 95.17% respectively, both higher than the latest algorithm.

A platform is proposed for automated analysis of retinal images, for obtaining from them information characterizing retinal blood vessels which may be useful in forming a diagnosis of a medical condition. A first aspect of the invention proposes that a plurality of characteristics of the retina are extracted, in order to provide data which is useful for enabling an evaluation of cardiovascular risk prediction, or even diagnosis of a cardiovascular condition. A second aspect uses fractal analysis of retinal images to provide vascular disease risk prediction, such as, but not limited to, diabetes and hypertension.

An artificially intelligent entity can emulate human behavior in video games. An AI model can be made by receiving gameplay logs of a video gameplay session, generating, based on the gameplay data, first situational data indicating first states of the video game, generating first control inputs provided by a human, the first control inputs corresponding to the first states of the video game, training a first machine learning system using the first situational data and corresponding first control inputs, and generating, using the first machine learning system, a first artificial intelligence model. The machine learning system can include a convolutional neural network. Inputs to the machine learning system can include a retina image and/or a matrix image.