Automatic detection of exudates in retinal images based on threshold moving average models

Abstract

Since exudate diagnostic procedures require the attention of an expert ophthalmologist as well as regular monitoring of the disease, the workload of expert ophthalmologists will eventually exceed the current screening capabilities. Retinal imaging technology is a current practice screening capability providing a great potential solution. In this paper, a fast and robust automatic detection of exudates based on moving average histogram models of the fuzzy image was applied, and then the better histogram was derived. After segmentation of the exudate candidates, the true exudates were pruned based on Sobel edge detector and automatic Otsu's thresholding algorithm that resulted in the accurate location of the exudates in digital retinal images. To compare the performance of exudate detection methods we have constructed a large database of digital retinal images. The method was trained on a set of 200 retinal images, and tested on a completely independent set of 1220 retinal images. Results show that the exudate detection method performs overall best sensitivity, specificity, and accuracy of 90.42%, 94.60%, and 93.69%, respectively.