Detection of blood vessels in retinal fundus images is an important initial step in the development of systems for computeraided diagnosis of pathologies of the eye. In this study, we perform multifeature analysis for the detection of blood vessels in retinal fundus images. The vessel detection techniques implemented include multiscale vesselness measures, Gabor filters, line operators, and matched filters. The selection of an appropriate threshold is crucial for accurate detection of retinal blood vessels. We evaluate an adaptive threshold selection method along with several others for this purpose. We also propose a postprocessing technique for removal of false-positive pixels around the optic nerve head. Values of the area under the receiver operating characteristic curve of up to 0:961 were obtained using the 20 test images of the DRIVE database.