In this paper, we proposed a novel algorithm to detect blood vessels on retinal images. By using directional local contrast as its detection feature, our algorithm is highly sensitive, fast and accurate. The algorithm only needs integral computing with very simple parameter adjustments and highly suitable for parallelization. It is much more robust to illumination conditions than intensity based counterparts and equally effective for large and small blood vessel detections. Traditional blood vessel mapping solutions focused on detecting the most number of blood vessel pixels at the cost of least number of falsely identified background pixels. This performance criterion works for well illuminated images with sharp boundary, but it does not address two major concerns. The first is that it favors detection of large blood vessels, and the second is that for darker images (due to poor illumination or pigment colors) it can be very difficult to generate hand traced maps. To overcome these problems, we propose using central lines of the blood vessels as a new performance measure for blood vessel mapping. The new performance measure is easy to evaluate, and it complements the existing performance measure. Experiment results on two public retinal image databases show that our algorithm outperforms two well known existing algorithms in terms of speeds and accuracy.