Automatic optic cup segmentation algorithm for retinal fundus images based on random forest classifier

Glaucoma is an eye disease that constitutes the second cause of blindness over the world. Although it cannot be cured, its progression may be prevented if it is early detected. Expert ophthalmologists use as a sign of suffering from the disease, the evaluation of the relationship between optic disc and cup areas in retinal fundus images and, therefore, image processing techniques applied to glaucoma has become an emerging research line. This paper presents a novel technique for the detection of the optic cup in retinal fundus images, which may be included in a glaucoma computer aided diagnosis tool. The method, based on a color space related to human perception and adapted to surrounding conditions, JCh from CIECAM 02 (International Commission on Illumination Color Appearance Model), utilizes a random forest classifier to obtain cup edge pixels. As vessels tend to bend in the edge of the cup, the classifier does not consider all the pixels in the image. In fact, only those belonging to vessels and possessing the highest curvature among their neighbors are taken into account. Another prior knowledge used in the proposed method is the fact that cup area usually posses a bright yellow color. Therefore the feature vector serving as an input for the classifier is made with the curvature, the color of the candidate pixel and its location relative to the OD center. Finally, a basic post processing is performed to join the selected pixels with a smooth curve. The method has been tested in a publicly available database, GlaucomaRepo, from where we used 35 images for training and 55 for test. Five numerical parameters were calculated and a comparison against three color spaces was performed. The results obtained indicate the effectiveness of the approach.