Maximum likelihood estimation of vessel parameters from scale space analysis

Authors:
J. Ng;S. T. Clay;S. A. Barman;A. R. Fielder;M. J. Moseley;K. H. Parker;C. Paterson
Affiliations:
Blackett Laboratory, Department of Physics, Imperial College London, London SW7 2BW, United Kingdom;Blackett Laboratory, Department of Physics, Imperial College London, London SW7 2BW, United Kingdom;Digital Imaging Research Centre, School of Computing, Information Systems and Mathematics, Kingston University, Penrhyn Road, Kingston upon Thames, Surrey KT1 2EE, United Kingdom;Department of Optometry and Visual Science, City University, Northampton Square, London EC1V OHB, United Kingdom;Department of Optometry and Visual Science, City University, Northampton Square, London EC1V OHB, United Kingdom;Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom;Blackett Laboratory, Department of Physics, Imperial College London, London SW7 2BW, United Kingdom
Venue:
Image and Vision Computing
Year:
2010

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Abstract

We describe a method of detecting features in retinal images using a model-based approach. The image is processed using a bank of filters in a scale space. A parametric model of the target feature is then proposed and the filter responses to the model calculated. A noise model is proposed, and incorporated into a maximum likelihood estimator to estimate model parameters. The estimator uses the generative parametric model to explore smoothly the scale space. This method is applied to the detection of retinal blood vessels, using a Gaussian-profiled valley as a model. A simple thresholding method is proposed as an example of using the rich estimated parameter maps to detect vessels and the results are compared against two existing vessel detectors. Our system is compared against ground truth and the output of existing systems. It is found to be comparable and, in addition, produces direct estimates of vessel calibres and contrasts. It does not use any form of region growing or vessel tracking, but thresholds a function of the estimated vessel parameters to determine vessel regions.