A Gaussian Mixture Model Based System for Detection of Macula in Fundus Images

  • Anam Tariq
  • Arslan Shaukat
  • Shoab A. Khan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)


Digital fundus imaging is used to diagnose various eye diseases like diabetic retinopathy, diabetic maculopathy and age related macular degeneration. Macula is the main central part of retina which is responsible for sharp vision and any changes in macula cause severe effects on vision. In this paper, we propose a novel method for automated detection of macula from digital fundus images. The proposed system performs preprocessing, optic disc detection and blood vessel segmentation prior to macula detection. In macula detection, it formulates a feature vector and uses Gaussian Mixture Model for detection of macular region. We evaluate the proposed technique using publicly available fundus image database MESSIDOR. The results show the validity of proposed system and are found to be competitive with previous results in the literature.


Digital fundus imaging Diabetic maculopathy Macula Gaussian Mixture Model 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Causes and Risk Factors. Diabetic Retinopathy. United States National Library of Medicine (2009)Google Scholar
  2. 2.
    Iwasaki, M., Inomara, H.: Relation Between Superficial Capillaries and Fovea Structures in the Human Retina. J. Invest. Ophthalm. 27, 1698–1705 (1986)Google Scholar
  3. 3.
    Sagar, A.V., Balasubramanian, S., Chandrasekaran, V.: Automatic Detection of Anatomical Structures in Digital Fundus Retinal Images. In: Conference on Machine Vision Applications, pp. 483–486 (2007)Google Scholar
  4. 4.
    Li, H., Chutatape, O.: A Model-Based Approach for Automated Feature Extraction in Fundus Images. In: Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV 2003), pp. 394–399 (2003)Google Scholar
  5. 5.
    Sinthanayothin, C., Boyce, J.F., Cook, H.L., Williamson, T.H.: Automated Localisation of the Optic Disc, Fovea, and Retinal Blood Vessels from Digital Colour Fundus Images. British J. Ophthalm. 83, 902–910 (1999)CrossRefGoogle Scholar
  6. 6.
    Tan, N.M., Wong, D.W.K., Liu, J., Ng, W.J., Zhang, Z., Lim, J.H., Tan, Z., Tang, Y., Li, H., Lu, S., Wong, T.Y.: Automatic Detection of the Macula in the Retinal Fundus Image by Detecting Regions with Low Pixel Intensity. In: International Conference on Biomedical and Pharmaceutical Engineering, pp. 1–5 (2009)Google Scholar
  7. 7.
    Lu, S., Lim, J.H.: Automatic Macula Detection from Retinal Images by a Line Operator. In: Proceedings of 2010 IEEE 17th International Conference on Image Processing, pp. 4073–4076 (2010)Google Scholar
  8. 8.
    Susman, E.J., Tsiaras, W.J., Soper, K.A.: Diagnosis of Diabetic Eye Disease. JAMA 247, 3231–3234 (1982)CrossRefGoogle Scholar
  9. 9.
    Tariq, A., Akram, M.U.: An Automated System for Colored Retinal Image Background and Noise Segmentation. In: IEEE Symposium on Industrial Electronics and Applications, pp. 405–409 (2010)Google Scholar
  10. 10.
    Akram, M.U., Khan, A., Iqbal, K., Butt, W.H.: Retinal Images: Optic Disk Localization and Detection. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010, Part II. LNCS, vol. 6112, pp. 40–49. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Akram, M.U., Khan, S.A.: Multilayered Thresholding-based Blood Vessel Segmentation for Screening of Diabetic Retinopathy. Engineer. Comput. (2012), doi:10.1007/s00366-011-0253-7Google Scholar
  12. 12.
    Gonzalez, R.C.: Digital Image Processing, 3rd edn. Prentice Hall (2008)Google Scholar
  13. 13.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 1st edn. Academic, Burlington (1999)Google Scholar
  14. 14.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley (2001)Google Scholar
  15. 15.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Anam Tariq
    • 1
  • Arslan Shaukat
    • 1
  • Shoab A. Khan
    • 1
  1. 1.Department of Computer Engineering, College of Electrical & Mechanical EngineeringNational University of Sciences & TechnologyPakistan

Personalised recommendations