Model based segmentation for retinal fundus images

Authors:
Li Wang;Abhir Bhalerao
Affiliations:
Department of Computer Science, University of Warwick, UK;Department of Computer Science, University of Warwick, UK
Venue:
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Year:
2003

Citing 3
Cited 1

Frame-Rate Spatial Referencing Based on Invariant Indexing and Alignment with Application to Online Retinal Image Registration

IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring Vascular Structure from 2D and 3D Imagery

MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms

IEEE Transactions on Information Technology in Biomedicine

A fast, efficient and automated method to extract vessels from fundus images

Journal of Visualization

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Abstract

This paper presents a method for detecting and measuring the vascular structures of retinal images.F eatures are modelled as a superposition of Gaussian functions in a local region.The parameters i.e. centroid, orientation, width of the feature are derived by a minimum mean square error (MMSE) type of spatial regression.W e employ a penalised likelihood test, the Akakie Information Criteria (AIC), to select the best model and scale for vessel segments.A maximum-cost spanning tree (MST) algorithm is then used to perform the neighbourhood linking and infer the global vascular structure.W e present results of evaluations on a set of twenty digital fundus retinal images.