Segmentation of blood vessels in retinal images using 2D entropies of gray level-gradient cooccurrence matrix

A novel automated method for the segmentation of blood vessels in retinal images based upon enhancement and maximum entropy thresholding is proposed. Blood vessels usually have poor local contrast. Before thresholding fundus images, several matched filters are employed to enhance the contrast of blood vessels. The matched-filter-response (MFR) image is processed by a thresholding scheme in order to extract blood vessels from the background. Then, the proposed thresholding approach evaluates two-dimensional entropies based on the gray level-gradient cooccurrence matrix. The 2D threshold vector that maximizes the edge class entropies is selected. This thresholding method utilizes the information of gray level and gradient in the MFR image. It is found that the proposed algorithm works well in normal or abnormal retinal images.