Journal of Computer Sciences and Applications, 2016, Vol. 4, No. 3, 59-66 Available online at http://pubs.sciepub.com/jcsa/4/3/2 ©Science and Education Publishing DOI:10.12691/jcsa-4-3-2 Detection of Hard Exudates in Retinal Fundus Images based on Important Features Obtained from Local Image Descriptors Kemal AKYOL 1,* , Şafak BAYIR 1 , Baha ŞEN 2 , Hasan B. ÇAKMAK 3 1 Department of Computer Engineering, Faculty of Engineering, Karabuk University, Turkey 2 Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Yıldırım Beyazıt University, Turkey 3 Department of Ophthalmology, Faculty of Medicine, Hacettepe University, Turkey *Corresponding author: kemalakyol48@gmail.com Abstract Diabetic retinopathy is one of the main complications of diabetes mellitus and it is a progressive ocular disease, the most significant factor contributing to blindness in the later stages of the disease. It has been a subject of many studies in the medical image processing field for a long time. Hard exudates are one of the primary signs of early stage diabetic retinopathy diagnosis. Immediately identifying hard exudates is of great importance for the blindness and coexistent retinal edema. There are various ways of achieving meaningful information from an image and one of them is key point extraction method. In this study, we presented a technique based on the acquisition of important information by utilizing the description information about the image within the framework of the learning approach in order to identify hard exudates. This technique includes the learning and testing processes of the system in order to make the right decisions in the analysis of new retinal fundus images. We performed experimental validation on DIARETDB1 dataset. The obtained results showed us the positive effects of machine learning technique suggested by us for the detection of hard exudates. Keywords: biomedical image processing, feature extraction, image classification, image recognition, machine learning Cite This Article: Kemal AKYOL, Şafak BAYIR, Baha ŞEN, and Hasan B. ÇAKMAK, “Detection of Hard Exudates in Retinal Fundus Images based on Important Features Obtained from Local Image Descriptors.” Journal of Computer Sciences and Applications, vol. 4, no. 3 (2016): 59-66. doi: 10.12691/jcsa-4-3-2. 1. Introduction A serious complication of diabetes mellitus results in diabetic retinopathy eye disease and vision loss. The development of the disease and prevention of blindness can be averted by early recognition and timely management [1,2,3,4]. A specialist eye doctor examines the diagnosis of diabetic retinopathy (DR), which is carried out by means of visual analysis of retinal fundus images being hard exudate (HE), hemorrhage and microaneurysm. A visible sign of diabetic retinopathy and a marker for the existence of coexistent retinal edema are hard exudates which are one of the most common anomalies found in the eye fundus of patients suffering from diabetic retinopathy. These exudates lead to loss of eyesight or blindness for people with diabetic retinopathy [5,6]. In this study, we presented a new approach that consists of image processing, key point extraction, feature extraction, important features and classifier stages in order to detect hard exudates from retinal fundus images. On the other hand, we benefited from digital image processing techniques during this study. We performed the experimental validation on a public image database, DIARETDB1 [7]. Key point detectors are mathematical methods that allow us to achieve necessary information to distinguish images. Many studies which are practiced with key point detectors and their descriptors have already been proposed in the literature (e.g. [8,9,10,11,12]). We built our study by making use of key point detector and descriptor algorithms called SURF (Speeded-Up Robust Features) and ORB (Oriented FAST and Rotated BRIEF) were introduced in [13,14] respectively with the insights gained from these previous work. Feature vector, obtained by descriptor algorithms, does not always indicate exact information and this leads to noisy, over fitting and slowing down training/testing data in negative situations. We planned to develop an effective and consistent method to overcome these negative situations emerged as data space grows. For this purpose, we obtained important features with Recursive Feature Elimination (RFE) method which reveals the best compatible features from features space as inputs dynamically. We tested Random Forest (RF) and Back Propagation Neural Network (BPNN) classifier algorithms performance by using these important features. In this process, we modeled the system by choosing classifier algorithm that provides better performance within the framework of speed and accuracy criteria.