Aged Macular Degeneration (AMD) leads to a progressive decline in visual acuity until reaching blindness. It is considered as an irreversible pathology where an early diagnosis remains crucial. However, the lack of ophthalmologists, the permanent increase in elderly people and their limited mobility involves a delay in AMD diagnosis.In this paper, we propose an automated method for AMD screening. The proposed processing pipeline consists in applying the well-known radon transform to the macula region in order to model the AMD lesions even with a moderate quality of smartphone captured fundus images. Thereby, the relevant features are carefully selected, related to the main proprieties of drusens, and then provided to an SVM classifier. The implementation of the method into a smartphone associated to a fundus image capturing device leads to a mobile CAD system that performs higher performance AMD screening. Within this framework and, to achieve a real time implementation, an optimization approach is suggested in order to reduce the processing workload.The evaluation of our method is carried out through the three public STARE, REFUGE and RFMID databases. A 4-fold cross validation approach is used to evaluate the method performance where accuracies of 100%, 95.2%, 94.3% are respectively obtained with STARE, REFUGE and RFMID databases. Comparisons with the state-of-the-art methods in the literature are done. Thereafter, the robustness of the proposed method was evaluated and proved. We note that 100% accuracy was preserved despite the use of degraded quality fundus images as noisy and blurred. Moreover, the propounded method was implemented in S7-Edge and S9 Smartphone devices, where the execution times of 19 and 15 milliseconds were respectively achieved, which proves the AMD real time detection.Taking advantage of its mobility, cost-effective, detection performance and reduced execution time, our proposed method seems a good solution for real time AMD screening on mobile devices.
Fundus image processing is getting widely used in retinopathy detection. Detection approaches always proceed to identify the retinal components, where optic disk is one of the principal ones. It is characterized by: a higher brightness compared to the eye fundus, a circular shape and convergence of blood vessels on it. As a consequence, different approaches for optic disk detection have been proposed. To ensure a higher performing detection, those approaches varied in terms of characteristics set chosen to detect the optic disk. Even the performances are slightly different, we distinguish a significant gap on the computational complexity and hence on the execution time. This paper focuses on the survey of the approaches for optic disk detection. To identify an efficient approach, it is relevant to explore the chosen characteristics and the proposed processing to locate the optic disk. For this purpose, we analyze the computational complexity of each detection approach. Then, we propose a classification approach in terms of computational efficiency. In this comparison study, we distinguish a relation between computational complexity and the characteristic set for OD detection.
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