Segmentation of optic disc in retina images using texture

The paper describes our work on the segmentation of the optic disc in retinal images. Our approach comprises of two main steps; a pixel classification method to identify pixels that may belong to the optic disc boundary and a circular template matching method to estimate the circular approximation of the optic disc boundary. The features used are based on texture, calculated using the intensity differences of local image patches. This was adapted from Binary Robust Independent Elementary Features (BRIEF). BRIEF is inherently invariant to image illumination and has a lower degree of computational complexity compared to other existing texture measurement methods. Fuzzy C-Means (FCM) and Naive Bayes are the clustering and classifier used to cluster/classify the image pixels. The method was tested on a set of 196 images composed of 110 healthy retina images and 86 glaucomatous images. The average mean overlap ratio between the true optic disc region and segmented region is 0.81 for both FCM and Naive Bayes. Comparison with a method based on the Hough Transform is also provided.