Sayadia Sofien Ben, Elloumi Yaroub, Kachouri Rostom, Akil Mohamed, Abdallah Asma Ben, Bedoui Mohamed Hedi
Medical Technology and Image Processing Laboratory, Faculty of Medicine, University of Monastir, Monastir, Tunisia.
LIGM, University Gustave Eiffel, CNRS, ESIEE Paris, 77454 Marne-la-Vallée, Paris, France.
Med Biol Eng Comput. 2022 May;60(5):1449-1479. doi: 10.1007/s11517-022-02546-8. Epub 2022 Mar 18.
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%, and 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.
年龄相关性黄斑变性（AMD）会导致视力逐渐下降直至失明。它被认为是一种不可逆的病症，早期诊断至关重要。然而，眼科医生短缺、老年人数量持续增加以及他们行动不便，导致AMD诊断延迟。在本文中，我们提出了一种用于AMD筛查的自动化方法。所提出的处理流程包括对黄斑区域应用著名的Radon变换，以便即使在智能手机拍摄的眼底图像质量一般的情况下也能对AMD病变进行建模。由此，仔细选择与玻璃膜疣的主要特征相关的相关特征，然后将其提供给支持向量机（SVM）分类器。将该方法集成到与眼底图像采集设备相关联的智能手机中，可形成一个移动计算机辅助检测（CAD）系统，该系统能实现更高性能的AMD筛查。在此框架内，为实现实时实施，提出了一种优化方法以减少处理工作量。我们的方法通过三个公开的STARE、REFUGE和RFMiD数据库进行评估。采用4折交叉验证方法评估该方法的性能，在STARE、REFUGE和RFMiD数据库上分别获得了100%、95.2%和94.3%的准确率。与文献中的现有方法进行了比较。此后，对所提出方法的鲁棒性进行了评估并得到证实。我们注意到，尽管使用了质量退化的眼底图像，如噪声图像和模糊图像，仍保持了100%的准确率。此外，所提出的方法在三星S7 Edge和S9智能手机设备上实现，分别实现了19毫秒和15毫秒的执行时间，这证明了AMD的实时检测。利用其移动性、成本效益、检测性能和减少的执行时间，我们提出的方法似乎是移动设备上实时AMD筛查的一个很好的解决方案。
