The aim of this work was to develop a method for the automatic identification of exudates, using an unsupervised clustering approach. The ability to classify each pixel as belonging to an eventual exudate, as a warning of disease, allows for the tracking of a patient’s status through a noninvasive approach. In the field of diabetic retinopathy detection, we considered four public domain datasets (DIARETDB0/1, IDRID, and e-optha) as benchmarks. In order to refine the final results, a specialist ophthalmologist manually segmented a random selection of DIARETDB0/1 fundus images that presented exudates. An innovative pipeline of morphological procedures and fuzzy C-means clustering was integrated in order to extract exudates with a pixel-wise approach. Our methodology was optimized, and verified and the parameters were fine-tuned in order to define both suitable values and to produce a more accurate segmentation. The method was used on 100 tested images, resulting in averages of sensitivity, specificity, and accuracy equal to 83.3%, 99.2%, and 99.1%, respectively.

The main contribution of this paper is introducing a method to distinguish between different landmarks of the retina: bifurcations and crossings. The methodology may help in differentiating between arteries and veins and is useful in identifying diseases and other special pathologies, too. The method does not need any special skills, thus it can be assimilated to an automatic way for pinpointing landmarks; moreover it gives good responses for very small vessels. A skeletonized representation, taken out from the segmented binary image (obtained through a preprocessing step), is used to identify pixels with three or more neighbors. Then, the junction points are classified into bifurcations or crossovers depending on their geometrical and topological properties such as width, direction and connectivity of the surrounding segments. The proposed approach is applied to the public-domain DRIVE and STARE datasets and compared with the state-of-the-art methods using proper validation parameters. The method was successful in identifying the majority of the landmarks; the average correctly identified bifurcations in both DRIVE and STARE datasets for the recall and precision values are: 95.4% and 87.1% respectively; also for the crossovers, the recall and precision values are: 87.6% and 90.5% respectively; thus outperforming other studies.

Landmark points in retinal images can be used to create a graph representation to understand and to diagnose not only different pathologies of the eye, but also a variety of more general diseases. Aim of this paper is the description of a non-supervised methodology to distinguish between bifurcations and crossings of the retinal vessels, which can be used in differentiating between arteries and veins. A thinned representation of the binarized image, is used to identify pixels with three or more neighbors. Junction points are classified into bifurcations or crossovers according to their geometrical and topological properties. The proposed approach is successfully compared with the state-of-the-art methods with the benchmarks DRIVE and STARE. The recall, precision and F-score average detection values are 91.5%, 88.8% and 89.8% respectively.

Diabetic Retinopathy (DR) is the main cause of blindness for diabetic patients. As the exudates are the primary sign of DR, therefore early detection and timely treatment can prevent and delay the risk of vision loss. Automatic screening could facilitate the screening process, reduce inspection time, and increase accuracy, which is vital in ophthalmic treatment, this development of exudates detection will help doctors in detecting symptoms faster. In this research, we use an automatic method to detect exudates from retinal digital images with non-dilated pupils of retinopathy patients; starting by detecting both the optic disc (OD) and retinal vessels, then probable exudates are defined through morphological techniques, in the last main phase, four features are implemented as input data for the fuzzy C-means (FCM) clustering to define the existing exudates in the fundus images. The overall detection performance is evaluated through measuring sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy. These measures are done by comparing results to hand-drawn ground truth (GT) done by an expert; which are comparatively analyzed. It is found that the proposed method detects exudates successfully with average values of sensitivity, specificity, PPV, PLR and accuracy of 86.3%, 98.4%, 20.8%, 86.2 and 98.4% respectively on the testing studied database. اعتلال الشبكية بمرض السكري يعتبر من أكثر الأمراض خطورة على العين والذي قد يؤدي الى فقدان البصر وخاصة عند مرضى السكري. تعتبر الافرازات من أولى العلامات الدالة على الاصابة بالمرض، لذلك من المهم اكتشافها واحصائها من أجل العلاج والوقاية من تفاقم المرض وفقدان البصر. في هذا البحث قمنا باقتراح طريقة تلقائية (اوتوماتيكية) للكشف عن الافرازات من صور شبكية العين لمرضى السكري، والتي بدورها تساعد الأطباء في عملية مراقبة التغير في وضعية العين واكتشاف اعراض امراض العين في مراحل مبكرة. وقد تم التطبيق على صور شبكية العين لمرضى السكري، وكانت الخوارزمية تعتمد اولا على استخراج قرص العين والاوعية الدموية وذلك لتحسين عملية اكتشاف الافرازات، ثم قمنا بتعيين وتحديد الافرازات وذلك بالاعتماد على خوارزمية التجميع الضبابي بالإضافة الى مجموعة من تقنيات معالجة الصور باستخدام برنامج الماتلاب. في النهاية تم تقييم النتائج عن طريق حساب مجموعة معايير منها: الحساسية، النوعية، والدقة وذلك بمقارنتها بالصور التي تم رسمها عن طريق الاخصائيين والأطباء، وكانت متوسطات النتائج كما يلي :86.3%، 98.4% و98.4% على التوالي.