Abstract-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 20µ per pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly-selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of highly variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent, Good or Fair performance in 83% of cases, and performs well even on blurred images.

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 sub-pixel accuracy. The diameter measurement is based on a two-dimensional difference of Gaussian model, which is optimized to fit a two-dimensional intensity vessel segment. The performance of the method is evaluated against Brinchmann-Hansen's half height, Gregson's rectangular profile and Zhou's Gaussian model. Results from 100 sample profiles show that the presented algorithm is over 30% more precise than the compared techniques and is accurate to a third of a pixel.

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. The algorithm is based on standard recommendations for quality analysis by human screeners, examining the clarity of retinal vessels within the macula region. An evaluation against a reference standard data-set is given; it is shown that the algorithm's performance correlates closely with that of clinicians manually grading image quality.

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. The optic nerve head and fovea are detected, so that the macula region can be identified and scanned. The algorithm is assessed against a reference standard database drawn from the Birmingham City Hospital (UK) diabetic retinopathy screening programme, against two possible modes of use: independent screening, and pre-filtering to reduce human screener workload.

Interpretation of geological features in seismic data is a subjective process, relying on one's visual perception and experience built up over several years. Based on these human factors, financial decisions are made that may have serious consequences to a petroleum company. The aim of this study is to review the role of one key visual cue in this interpretative process: colour. Colour is a powerful cue that can have a significant impact on the interpretation of seismic data. However, compensation mechanisms within the human perceptual system can sometimes lead to unexpected visual effects, such as luminance sensitivity and simultaneous contrast, which have the potential to bias the interpretation of geoscientific information and therefore increase interpretation uncertainty and risk. Here we examine these visual effects, and present the findings of an experiment aiming to illustrate bias dependent on the use of colour. Both inter-and intra-operator differences were found in the manual delineation of a sedimentary geobody from seismic data. The results clearly suggest that measurements from seismic data based on manual delineation of imaged object boundaries can be associated with uncertainties that are usually unquantified.