Automated extraction and quantification of macular drusen from fundal photographs
ABSTRACT The objective quantification of drusen (and other macular lesions) should have applications epidemi-ologically, in the study of the natural history of drusen, and with such instruments as the scanning laser ophthalmoscope. The automated extraction of drusen from photographs is technically difficult because of uneven macular reflectance, and the confusing pattern of darker vessels. We have developed a method using an IBM personal computer, an image digitising board and specially written software. Once the image is digitised, no further input from the operator is necessary. We present the results of manual counting versus automated counting on a small series of patients with drusen. The automated technique is highly reproducible, and will calculate the retinal area occupied by drusen. The area and numbers of drusen can be compared over time, giving an index of progression. Hard drusen are fairly well detected, but the detection of soft drusen with their lower contrast remains a problem. The technique cannot distinguish between drusen and other pale lesions (e.g., atrophic retinal changes).
- SourceAvailable from: Sunanda Mitra[show abstract] [hide abstract]
ABSTRACT: Age-Related Macular Degeneration (ARMD) is the leading cause of irreversible visual loss among the elderly in the US and Europe. A computer-based system has been developed to provide the ability to track the position and margin of the ARMD associated lesion; drusen. Variations in the subject's retinal pigmentation, size and profusion of the lesions, and differences in image illumination and quality present significant challenges to most segmentation algorithms. An algorithm is presented that first classifies the image to optimize the variables of a mathematical morphology algorithm. A binary image is found by applying Otsu's method to the reconstructed image. Lesion size and area distribution statistics are then calculated. For training and validation, the University of Wisconsin provided longitudinal images of 22 subjects from their 10 year Beaver Dam Study. Using the Wisconsin Age-Related Maculopathy Grading System, three graders classified the retinal images according to drusen size and area of involvement. The percentages within the acceptable error between the three graders and the computer are as follows: Grader-A: Area: 84% Size: 81%; Grader-B: Area: 63% Size: 76%; Grader-C: Area: 81% Size: 88%. To validate the segmented position and boundary one grader was asked to digitally outline the drusen boundary. The average accuracy based on sensitivity and specificity was 0.87 for thirty four marked regions.