The extraction of retinal vessels plays an important role in the diagnosis and study of retinal diseases, such as Age-related Macular Degeneration (AMD), Diabetic Retinopathy, Retinopathy of Prematurity. Vessel diameters, tortuosity, branch lengths, angles, and bifurcations are essential to diagnosing these diseases. However, this is a challenging task due to high noise levels, the low contrast of thin vessels to the background, non-uniform illumination, and the central light reflex. Our goal here is to develop a framework to accurately segment the retinal vessels as a preprocessing step for the feature extraction of the vessels towards the future disease diagnosis. In this paper, we present a principal curve based retinal vessel segmentation approach to achieve this goal. We first use the isotropic Gaussian kernel Frangi filter to enhance the retinal vessels and measure the diameters of them. A multiscale principal curve projection and tracing algorithm is then proposed to identify the centerlines of the vessels in the output image of the Franfi filter using the underlying kernel smoothing interpolation of the intensities. The estimated vessel radius from the Frangi filter are used as the bandwidth of the kernel interpolation in the principal curve projection and tracing step. The vessel features towards diagnosing and analyzing the diseases can be extracted from our segmentation results. The presented approach is implemented on a publicly available DRIVE database.