Biomedical subjects

Sam Philip

Publications and source records attributed to Sam Philip.

3 recordsLinked to original sources

PubMed2006

Automatic detection of retinal anatomy to assist diabetic retinopathy screening.

Screening programmes for diabetic retinopathy are being introduced in the United Kingdom and elsewhere. These require large numbers of retinal images to be manually graded for the presence of disease. Automation of image grading would have a number of benefits. However, an important prerequisite for automation is the accurate location of the main anatomical features in the image, notably the optic disc and the fovea. The locations of these features are necessary so that lesion significance, image field of view and image clarity can be assessed. This paper describes methods for the robust location of the optic disc and fovea. The elliptical form of the major retinal blood vessels is used to obtain approximate locations, which are refined based on the circular edge of the optic disc and the local darkening at the fovea. The methods have been tested on 1056 sequential images from a retinal screening programme. Positional accuracy was better than 0.5 of a disc diameter in 98.4% of cases for optic disc location, and in 96.5% of cases for fovea location. The methods are sufficiently accurate to form an important and effective component of an automated image grading system for diabetic retinopathy screening.

Automation↗

PubMed2006

Automated microaneurysm detection using local contrast normalization and local vessel detection.

Screening programs using retinal photography for the detection of diabetic eye disease are being introduced in the UK and elsewhere. Automatic grading of the images is being considered by health boards so that the human grading task is reduced. Microaneurysms (MAs) are the earliest sign of this disease and so are very important for classifying whether images show signs of retinopathy. This paper describes automatic methods for MA detection and shows how image contrast normalization can improve the ability to distinguish between MAs and other dots that occur on the retina. Various methods for contrast normalization are compared. Best results were obtained with a method that uses the watershed transform to derive a region that contains no vessels or other lesions. Dots within vessels are handled successfully using a local vessel detection technique. Results are presented for detection of individual MAs and for detection of images containing MAs. Images containing MAs are detected with sensitivity 85.4% and specificity 83.1%.

Algorithms↗

PubMed2006

Automated assessment of diabetic retinal image quality based on clarity and field definition.

PURPOSE: To evaluate the performance of an automated retinal image quality assessment system for use in automated diabetic retinopathy grading. METHODS: Algorithmic methods have been developed for assessing the quality of 45 degrees single field retinal images for use in diabetic retinopathy screening. For this purpose, image quality was defined by two aspects: image clarity and field definition. An image with adequate clarity was defined as one that shows sufficient detail for automated retinopathy grading. The visibility of the macular vessels was used as an indicator of image clarity, since these vessels are known to be narrow and become less visible with any image degradation. An image with adequate field definition was defined as one that shows the desired field of view for retinopathy grading, including the full 45 degrees field of view, the optic disc, and at least two optic disc diameters of visible retina around the fovea. From 489 patients attending a diabetic retinopathy screening program, 1039 retinal images were obtained. The images were graded by a clinician for image clarity and field definition, with a comprehensive image-quality grading scheme. RESULTS: The sensitivity and specificity were, respectively, 100% and 90.9% for inadequate clarity detection, 95.3% and 96.4% for inadequate field definition detection, and 99.1% and 89.4% for inadequate overall quality detection. CONCLUSIONS: The automated system performs with sufficient accuracy to form part of an automated diabetic retinopathy grading system.

Adolescent↗