Automatic Detection of Pathological Retinal Images Using Color and Shape Features

Abstract

Objective: We propose an algorithm for exudate detection and pathological retinal images identification. Method: We improved an existing algorithm that detects exudates in a retinal image replacing the k-means clustering by fuzzy k-means and applied an additional step to detect optical disc (OD). Furthermore, our approach added a classification process to eliminate remaining false exudates regions. Finally, we classify the retinal image as pathological or non-pathological by measuring the ratio of candidate exudate regions before classification and the number of regions removed by the classification step. Results: Tests were performed on DIARETDB1 database, and the results obtained were; Fmeasure – 90%, area under the ROC curve – 88% and the Kappa coefficient – 77% (very good). Conclusion: The success of the algorithm is due mostly to the OD detection approach and the classification step. The obtained results confirmed that the proposed algorithm outperformed the others.