Optos Plc

David Clifton

Peter Robert Wakeford

Ralph Allen Pinnock

Craig Robertson

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.

System, method, and computer-readable medium for rejecting full and partial blinks for retinal tracking

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.

Retinal position tracking

A method, apparatus, and computer-readable medium, for assessing image quality of an image produced by a scanning imaging system. The method comprises acquiring (S) image data of an image produced by the scanning imaging system and calculating (Sto S), for each section of the image: a respective first value measuring at least one of sharpness or contrast of at least a part of the section, the measuring depending on noise, a respective second value measuring noise in at least a part of the section, and a respective third value indicating image quality, by combining the first and second values. The combining is such that calculated third values have a weaker dependency on the noise than the first values. The method further comprises determining (S) a quality score that is indicative of image quality of the image based on a variation of the calculated third values among the sections.

Method, apparatus, and computer-readable medium for assessing quality of images produced by scanning system for eye examination

An image processing apparatus uses first and second digital vascular image data to register two images. The two images may be from different imaging modes. The first and second images are processed with a two-dimensional, directional filter () that has the effect of producing clusters of orthogonally adjacent image data points in which the magnitude of an intensity gradient between each orthogonally adjacent image data point is less than a predetermined value. Subsequently, common clusters are identified between the first and second image data using a corner detecting algorithm (). The directional filter produces 'stepping' features, where vascular features would otherwise appear with smooth edges. These numerous features are identified by the corner detecting algorithm and can be used () for registering common clusters between the first and second image data. The filter may be a rotating Gabor filter matched to vascular features in the images.

Relating to image processing

A laser scanning ophthalmoscope obtains images of a retina. An image is processed by (i) mapping an image along a one dimensional slice; (ii) computing a wavelet scalogram of the slice; (iii) mapping ridge features from the wavelet scalogram; repeating steps (i), (ii) and (iii) for one or more mapped image slices. The mapped ridge features from the slices are superimposed. Textural information is derived from the superimposed mapped ridge features. The analysis can be tuned to detect various textural features, for example to detect image artifacts, or for retinal pathology classification.

Methods and apparatus for image processing, and laser scanning ophthalmoscope having an image processing apparatus

An embodiment of the invention provides an apparatus and a method. An intensity gradient between first and second optical image data of an optical image produced by an optical scanning device is determined. The first optical image data is a first optical image pixel and the second optical image data is a second optical image pixel. An intensity gradient image comprising a plurality of intensity gradient image pixels is determined, each intensity gradient image pixel representing the magnitude of an intensity gradient. One or more anomalies in the optical image in dependence on the magnitude of the intensity gradient being greater than or equal to a threshold value is identified.

Identifying and correcting anomalies in an optical image

A method of identifying anomalies in images produced using an imaging device (). The method comprises receiving (), () first and second pluralities of candidate anomalies, the candidate anomalies being identified in subsequent images produced with an imaging device. A constellation match (), (), (), () is carried out between the first and second pluralities of candidate anomalies to identify a correlation therebetween, represented by a constellation match value. A plurality of constellation match values is determined with a different relative x-y shift between the first and second pluralities of candidate images. If a good correlation is found at a particular x-y shift then the candidate anomalies are common between the first and second images, which is indicative of the first and second images including anomalies.

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Dunfermline, United Kingdom

David Clifton