Advertisement

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)

Abstract

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.

Keywords

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.

References

  1. 1.
    H. Shen, C.V. Stewart, B. Roysam, G. Lin, and H.L. Tanenbaum, “Frame-rate spatial referencing based on invariant indexing and alignment with application to online retinal image registration,” IEEE Trans. on PAMI, vol.25, pp.379–384, Mar. 2003.Google Scholar
  2. 2.
    A. Pinz, S. Bernogger, P. Datlinger, and A. Kruger, “Mapping the human retina,” IEEE Transactions on Medical Imaging, vol.17, no.4, pp.606–619, 1998.CrossRefGoogle Scholar
  3. 3.
    A. Can, H. Shen, J.N. Turner, J.L. Tanenbaum, and B. Roysam, “Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms,” IEEE Transactions on Information Technology in Biomedicine, vol.3, no.2, pp.125–137, June 1999.CrossRefGoogle Scholar
  4. 4.
    S. Chardhuri, S. Chatterjee, N. katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Transcations on Medical Imaging, vol.8, no.3, pp.263–269, 1989.CrossRefGoogle Scholar
  5. 5.
    A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Transactions on Medical Imaging, vol.19, no.3, pp.203–210, 2000.CrossRefGoogle Scholar
  6. 6.
    A. R. Davies and R. Wilson, “Curve and corner extraction using the multiresolution fourier transform,” in Image Processing and its Applications. 4h IEE Conf., 1992.Google Scholar
  7. 7.
    L. Wang and A. Bhalerao, “Detecting branching structures using local gaussian models,” in International Symposium on Biomedical Imaging (ISBI). IEEE, July 2002.Google Scholar
  8. 8.
    K.P. Burnham and D.R. Anderson, Model Selection and Inference, Springer-Verlag, 1998.Google Scholar
  9. 9.
    A. Bhalerao, E. Thönnes, W. Kendall, and R. Wilson, “Inferring vascular structure from 2d and 3d imagery,” in Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

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

Personalised recommendations