A general framework for vessel segmentation in retinal images

We present a general framework for vessel segmentation in retinal images with a particular focus on small vessels. The retinal images are first processed by a nonlinear diffusion filter to smooth vessels along their principal direction. The vessels are then enhanced using a compound vessel enhancement filter that combines the eigenvalues of the Hessian matrix, the response of matched filters, and edge constraints on multiple scales. The eigenvectors of the Hessian matrix provide the orientation of vessels and so only one matched filter is necessary at each pixel on a given scale. This makes the enhancement filter is more efficient compared with existing multiscale matched filters. Edge constraints are used to suppress the response of spurious boundary edges. Finally, the center lines of vessels are tracked from seeds obtained using multiple thresholds of the enhanced image. Evaluation of the enhancement filter and the segmentation is performed on the publicly available DRIVE database.