Model Based Segmentation for Retinal Fundus Images

  • Li Wang
  • Abhir Bhalerao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2749)


This paper presents a method for detecting and measuring the vascular structures of retinal images. Features 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. We 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. We present results of evaluations on a set of twenty digital fundus retinal images.


Retinal Image Minimum Mean Square Error Ground Truth Image Spatial Frequency Domain Minimum Mean Square Error Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Li Wang
    • 1
  • Abhir Bhalerao
    • 1
  1. 1.Department of Computer ScienceUniversity of WarwickUK

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