Retinal Vessel Detection using Self-Matched Filtering

Automated analysis of retinal images usually requires estimating the positions of blood vessels, which contain important features for image alignment and abnormality detection. Matched filtering can produce the best results but is difficult to implement because the vessel orientations and widths are unknown beforehand. Many researchers use Hessian filtering, which provides an estimate for vessel orientation through the use of three orientation templates. We propose a novel filtering approach, called self-matched filtering, which is based on the 180deg rotated version of the noisy vessel signal in the local neighborhood. We show that even though the proposed filter achieves half the signal-to-noise ratio of a matched filter, it does not require the estimation of the vessel scale and orientation, and can outperform Hessian filtering by up to a factor of two in terms of miss detection error.