SURF Descriptor and Pattern Recognition Techniques in Automatic Identification of Pathological Retinas.

Automatic systems for eye disease identification are important in the ophthalmology field. Medical image processing and analysis have enabled prevention, diagnosis and treatment of eye diseases which were assumed to incurable, in the past. Despite the advances in technology, automated systems for retina component detection are affected by the large diversity of images and degradation caused by artifacts originated by diseases. These diseases can potentially degrade images and cause inaccurate diagnosis for automated systems as well as specialists. Therefore, several researches have been employed to overcome these drawbacks. In this study, we introduce a computer-aided diagnosis system that can identify patients who present risks of vision loss and require greater assistance of a specialist. Our approach applies the Speed-Up Robust Feature algorithm to find points of interest to form visual dictionaries. These dictionaries describe an image as a vector of characteristics which is input to the step that classifies retina image as healthy or pathological. We evaluated the proposed system in 1674 images of six public databases and obtained 96.70% accuracy and Kappa index of 0.9 (excellent).