Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening

The Role of Retinal Imaging and Portable Screening Devices in Tele-ophthalmology Applications for Diabetic Retinopathy Management

In the years since its introduction, retinal imaging has transformed our capability to visualize the posterior pole of the eye. Increasing practical advances in mobile technology, regular monitoring, and population screening for diabetic retinopathy management offer the opportunity for further development of cost-effective applications through remote assessment of the diabetic eye using portable retinal cameras, smart-phone-based devices and telemedicine networks. Numerous retinal imaging methods and mobile technologies in tele-ophthalmology applications have been reported for diabetic retinopathy screening and management. They provide several advantages of automation, sensitivity, specificity, portability, and miniaturization for the development of point-of-care diagnostics for eye complications in diabetes. The aim of this paper is to review the role of retinal imaging and mobile technologies in tele-ophthalmology applications for diabetic retinopathy screening and management. At large, although improvements in current technology and telemedicine services are still needed, telemedicine has demonstrated to be a worthy tool to support health caregivers in the effective management and prevention of diabetes and its complications.

Automatic detection of microaneurysms in retinal fundus images

Diabetic retinopathy (DR) is one of the leading causes of new cases of blindness. Early and accurate detection of microaneurysms (MAs) is important for diagnosis and grading of diabetic retinopathy. In this paper, a new method for the automatic detection of MAs in eye fundus images is proposed. The proposed method consists of four main steps: preprocessing, candidate extraction, feature extraction and classification. A total of 27 characteristic features which contain local features and profile features are extracted for KNN classifier to distinguish true MAs from spurious candidates. The proposed method has been evaluated on two public database: ROC and e-optha. The experimental result demonstrates the efficiency and effectiveness of the proposed method, and it has the potential to be used to diagnose DR clinically.

Automatic Screening and Grading of Age-Related Macular Degeneration from Texture Analysis of Fundus Images

Age-related macular degeneration (AMD) is a disease which causes visual deficiency and irreversible blindness to the elderly. In this paper, an automatic classification method for AMD is proposed to perform robust and reproducible assessments in a telemedicine context. First, a study was carried out to highlight the most relevant features for AMD characterization based on texture, color, and visual context in fundus images. A support vector machine and a random forest were used to classify images according to the different AMD stages following the AREDS protocol and to evaluate the features' relevance. Experiments were conducted on a database of 279 fundus images coming from a telemedicine platform. The results demonstrate that local binary patterns in multiresolution are the most relevant for AMD classification, regardless of the classifier used. Depending on the classification task, our method achieves promising performances with areas under the ROC curve between 0.739 and 0.874 for screening and between 0.469 and 0.685 for grading. Moreover, the proposed automatic AMD classification system is robust with respect to image quality.