Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses

Automated segmentation of blood vessels in retinal images can help ophthalmologists screen larger populations for vessel abnormalities. However, automated vessel extraction is difficult due to the fact that the width of retinal vessels can vary from very large to very small, and that the local contrast of vessels is unstable. Further, the small vessels are overwhelmed by Gaussian-like noises. Therefore the accurate segmentation and width estimation of small vessels are very challenging. In this paper, we propose a simple and efficient multiscale vessel extraction scheme by multiplying the responses of matched filters at three scales. Since the vessel structures will have relatively strong responses to the matched filters at different scales but the background noises will not, scale production could further enhance vessels while suppressing noise. After appropriate selection of scale parameters and appropriate normalization of filter responses, the filter responses are then extracted and fused in the scale production domain. The experimental results demonstrate that the proposed method works well for accurately segmenting vessels with good width estimation.