Diagnosing Diabetic Retinopathy from Colored Fundus Images

In this study, the problem of automatic detection of Diabetic Retinopathy (DR) has been addressed. As the technology advances, researchers are becoming more interested in intelligent medical diagnosis systems to assist screening for specific diseases such as diabetes and its complications. DR is a serious condition which can result in blindness if it is not diagnosed and controlled at early stage. Medical experts diagnose DR from specific lesions in colored fundus images. There are different segments possibly appearing in fundus images including Optical Disk (OD), Blood Vessels (BV), Dark Lesions (i.e. Microaneurysm (MA) or briefly Aneurysm, Hemorrhage (H) and Neuvascularization (NV)), and Light Lesions (i.e. Hard Exudates (HE) and Cotton Wool Spots (CVS)). In this thesis, an automated system is proposed for automatic detection of lesions and accordingly grading DR. The proposed system is implemented as follows: After removing noisy area, optical disk is discovered in images based on a histogram template method. Then, using thresholding a black and white mask is produced to remove optical disk from fundus images. The network of blood vessels should also be removed. Based on Kirsch edge enhancement technique, blood vessels are masked. The next step of segmentation is searching for dark and light lesions. In the next phase, six features related to anatomical characteristics of anomalies in retinal images are extracted. These features are related to size, shape, color and brightness of the regions. Support Vector Machine (SVM) classifier is the last stage of the system. Light lesions and dark lesions are separately classified into their corresponding anomalies using linear SVM classifier. 5-fold cross validation is used to avoid bias in selection of train and test sets. Experimental results conducted on four data sets including DIARETDB0, DIARETDB1, STARE and HRF have proved that accuracy, sensitivity and specificity of the proposed system are comparable or superior to state-of-the-art methods. In the last step, based on the detected abnormal lesions, the degree of severity of DR is automatically defined. Keywords: Diabetic Retinopathy, Automatic Detection, Fundus Image, Abnormal Lesions, Image Segmentation, Feature Extraction, Classification.