Elizabeth M. Massey, James A. Lowell, Andrew Hunter, David Steel


This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object. We implement the level set using a fast upwind scheme and compare the proposed method against five other segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician marked-up boundaries as ground truth.


  1. Chen, H.-C. (2002). Vascular Complications of Diabetes; current issues in pathogenesis and treatment, chapter 10, pages 97-108. Blackwell Publishing.
  2. Deschamps, T., Schwartz, P., Trebotich, D., Colella, P., Saloner, D., and Malladi, R. (2004). Vessel segmentation and blood flow simulation using level-sets and embedded boundary methods. Computer Assisted Radiology and Surgery. Proceedings of the 18th International Congress and Exhibition, 1268:75-80.
  3. Goldbaum, M., Katz, N., Nelson, M., and Haff, L. (1990). The discrimination of similarly colored objects in computer images of the ocular fundus. Investigative Ophthalmology & Visual Science, 31:617-623.
  4. Gonzalez, R. C. and Woods, R. E. (2001). Digital Image Processing. Prentice Hall, Upper Saddle River, NJ.
  5. Lim, Y. W. and Lee, S. U. (1990). On the color image segmentation algorithm based on the thresholding and the fuzzy c-means technique. Pattern Recognition, 23:935-952.
  6. Lowell, J. (2005). Automated Retinal Analysis. PhD thesis, University of Durham.
  7. Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., and Kennedy, L. (2004). Optic nerve head segmentation. IEEE Transactions on Medical Imaging, 23(2):256-264.
  8. Osareh, A., Mirmehdi, M., Thomas, B., and Markham, R. (2001). Automatic recognition of exudative maculopathy using fuzzy c-means clustering and neural networks. In Claridge, E. and Bamber, J., editors, Medical Image Understanding and Analysis, pages 49-52. BMVA Press.
  9. Osher, S. and Sethian, J. A. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79:12-49.
  10. Perona, P. and Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629-639.
  11. Porta, M. and Bandello, F. (2002). Diabetic retinopathy a clinical update. Diabetologia, 45(12):1617-1634.
  12. Sapiro, G. (2001). Geometric Partial Differential Equations and Image Analysis. Cambridge University Press.
  13. Sinthanayothin, C., Boyce, J., Williamson, T., Cook, H., Mensah, E., and Lal, S. andUsher, D. (2002). Automated detection of diabetic retinopathy on digital fundus images. Diabetic Medicine, 19:105-112.
  14. Wang, H., Hsu, W., Goh, K., and Lee, M. (2000). An effective approach to detect lesions in color retinal images. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 181-186.
  15. Wang, L., Bhalerao, A., and Wilson, R. (2004). Robust modelling of local image structures and its application to medical imagery. In ICPR04, pages III: 534-537.
  16. Ward, N., Tomlinson, S., and Taylor, C. J. (1989). Image analysis of fundus photographs: the detection and measurement of exudates associated with diabetic retinopathy. Ophthalmology, 96(1):80-86.

Paper Citation

in Harvard Style

M. Massey E., A. Lowell J., Hunter A. and Steel D. (2009). LESION BOUNDARY SEGMENTATION USING LEVEL SET METHODS . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 245-249. DOI: 10.5220/0001781402450249

in Bibtex Style

author={Elizabeth M. Massey and James A. Lowell and Andrew Hunter and David Steel},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},

in EndNote Style

JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
SN - 978-989-8111-69-2
AU - M. Massey E.
AU - A. Lowell J.
AU - Hunter A.
AU - Steel D.
PY - 2009
SP - 245
EP - 249
DO - 10.5220/0001781402450249