Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses |
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| Authors: | Qin Li Jane You David Zhang |
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| Affiliation: | 1. Medical Devices and Electronics Testing Center, Shenzhen Academy of Metrology and Quality Inspection, China;2. Biometrics Research Centre, Department of Computing, The Hong Kong Polytechnic University, KLN, Hong Kong;1. Department of Electrical Engineering, K.N. Toosi University of Technology, Seyed Khandan, P.O. Box 16315-1355, Tehran, Iran;2. Inserm U1105 GRAMFC, Groupe de Recherches sur l''Analyse Multimodale de la Fonction Cérébrale, 80036 Amiens Cedex, France;1. Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, The Netherlands;2. Dept. of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Italy;1. College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China;2. Department of Electrical & Electronic Engineering, Eastern Mediterranean University, Gazimagusa, Mersin, Turkey;3. Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, Japan;1. CSI Institute of Technology, Thovalai, Tamil Nadu, India;2. Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India;1. School of Geosciences and Info-physics, Central South University, Changsha, Hunan 410083, China;2. Ecole Centrale de Lyon, ICJ, UMR5205, F-69134, France;3. College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA;1. VAMPIRE Project, Dipartimento di Matematica e Informatica, Università degli Studi di Palermo, Via Archirafi 34, 90123 Palermo, Italy;2. VAMPIRE Project, School of Computing, University of Dundee, Dundee, Scotland, United Kingdom |
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| Abstract: | 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. |
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