Retinal Spot Lesion Detection Using Adaptive Multiscale Morphological Processing

  • Xin Zhang
  • Guoliang Fan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4292)


We present a new spot lesion detection algorithm for retinal images with background diabetic retinopathy (DR) pathologies. The highlight of this algorithm is its capability to deal with all DR-related spot lesions of various sizes and shapes that is accomplished by a unique adaptive multiscale morphological processing technique. A scale map is generated to delineate lesion areas based an edge model, and it is used to fuse multiscale morphological processing results for lesion enhancements. The local/releative entropy thresholding techniques are employed to segment lesion regions, and a scale-guided validation process is used to remove over-detections based on the scale map. The proposed algorithm is tested on 30 retinal images where all spot lesions are hand-labelled for performance evaluation. Compared with two existing algorithms, the proposed one significantly improves the overall performance of spot lesion detection producing higher sensitivity and/or predictive values.


Diabetic Retinopathy Retinal Image Lesion Detection Optimal Scale Early Treatment Diabetic Retinopathy Study 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    ETDRS Research Group: Grading dabetic retinopathy from seteoscopic color fundus photographos – an extension of the modified Airelie House Classification (ETDRS report number 10). Ophthalmology 98, 786–806 (1991)Google Scholar
  2. 2.
    Fransen, S.R., Leonard-Martin, T.C., Feuer, W.J., Hildebrand, P.L.: Clinical evaluation of patients with diabetic retinopathy: Accuracy of the Inoveon diabetic retinopathy-3DT system. The American Academy of Ophthalmology 109, 595–601 (2002)CrossRefGoogle Scholar
  3. 3.
    Spencer, T., Olson, J.A., McHardy, K.C., Sharp, P.F., Forrester, J.V.: An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. Computers and Biomedical Research 29, 284–302 (1996)CrossRefGoogle Scholar
  4. 4.
    Niemeijer, M., Ginneken, B.V., Staal, J., Suttorp-Schulten, M.S.A., Abramoff, M.D.: Automatic detection of red lesions in digital color fundus photographs. IEEE Trans. Medical Imaging 24, 584–592 (2005)CrossRefGoogle Scholar
  5. 5.
    Frame, A.J., Undrill, P.E., Cree, M.J., Olson, J.A., McHardy, K.C., Sharp, P.F., Forrester, J.V.: A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms. Computers in Biology and Medicine, 225–238 (1998)Google Scholar
  6. 6.
    Walter, T., Klein, J.C., Massin, P., Erginary, A.: A contribution of image processing to the diagnosis of diabetic retinopathy–detection of exudates in color fundus images of the human retina. IEEE Trans. Medical Imaging 21, 1236–1243 (2001)CrossRefGoogle Scholar
  7. 7.
    Sbeh, A.B., Cohen, L.D., Mimoum, G., Coscas, G.: A new approach of geodesic reconstruction for drusen segmentation in eye fundus images. IEEE Tran. Medical Imaging 20, 1321–1333 (2001)CrossRefGoogle Scholar
  8. 8.
    Zhang, X., Chutatape, O.: Top-down and bottom-up strategies in lesion detection of background diabetic retinopathy. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 422–428 (2005)Google Scholar
  9. 9.
    Maragos, P.: Pattern spectrum and multiscale shape representation. IEEE Trans. Pattern Analysis and Machine Intelligence 11, 701–716 (1989)MATHCrossRefGoogle Scholar
  10. 10.
    Vincent, L.: Morphological grayscale reconstruction: Definition, efficient algorithms and applications in image analysis. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 633–635 (1992)Google Scholar
  11. 11.
    Mukhopadhyay, S., Chanda, B.: An edge preserving noise smoothing technique using multiscale morphology. Signal Processing, 527–544 (2002)Google Scholar
  12. 12.
    vanBeck, P.J.L.: Edge-Based Image Representation and Coding. PhD thesis, Delft University of Technology, the Netherlands (1995)Google Scholar
  13. 13.
    Fan, G., Cham, W.K.: Model-based edge reconstruction for low bit-rate wavelet-compressed images. IEEE Trans. Circuits and Systems for Video Technology 10, 120–132 (2000)CrossRefGoogle Scholar
  14. 14.
    Pal, N.R., Pal, S.K.: Entropic thresholding. Signal Processing 16, 97–108 (1989)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Chang, C.I., Chen, K., Wang, J., Althouse, M.L.G.: A relative entropy-based approach to image thresholding. Pattern Recognition 27, 1275–1289 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xin Zhang
    • 1
  • Guoliang Fan
    • 1
  1. 1.School of Electrical and Computer EngineeringOklahoma State UniversityStillwaterUSA

Personalised recommendations