An automated retinal image quality grading algorithm.

This paper introduces an algorithm for the automated assessment of retinal fundus image quality grade. Retinal image quality grading assesses whether the quality of the image is sufficient to allow diagnostic procedures to be applied. Automated quality analysis is an important preprocessing step in algorithmic diagnosis, as it is necessary to ensure that images are sufficiently clear to allow pathologies to be visible.

Automated diagnosis of referable maculopathy in diabetic retinopathy screening.

This paper introduces an algorithm for the automated diagnosis of referable maculopathy in retinal images for diabetic retinopathy screening. Referable maculopathy is a potentially sight-threatening condition requiring immediate referral to an ophthalmologist from the screening service, and therefore accurate referral is extremely important. The algorithm uses a pipeline of detection and filtering of "peak points" with strong local contrast, segmentation of candidate lesions, extraction of features and classification by a multilayer perceptron.

Assessment of stereoscopic optic disc images using an autostereoscopic screen - experimental study.

Background: Stereoscopic assessment of the optic disc morphology is an important part of the care of patients with glaucoma. The aim of this study was to assess stereoviewing of stereoscopic optic disc images using an example of the new technology of autostereoscopic screens compared to the liquid shutter goggles.

Methods: Independent assessment of glaucomatous disc characteristics and measurement of optic disc and cup parameters whilst using either an autostereoscopic screen or liquid crystal shutter goggles synchronized with a view switching display.

Measurement of retinal vessel widths from fundus images based on 2-D modeling.

Changes in retinal vessel diameter are an important sign of diseases such as hypertension, arteriosclerosis and diabetes mellitus. Obtaining precise measurements of vascular widths is a critical and demanding process in automated retinal image analysis as the typical vessel is only a few pixels wide. This paper presents an algorithm to measure the vessel diameter to subpixel accuracy.

Optic nerve head segmentation.

Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 microns/pixel).