Rough Set Approach in Ultrasound Biomicroscopy Glaucoma Analysis

  • Soumya Banerjee
  • Hameed Al-Qaheri
  • El-Sayed A. El-Dahshan
  • Aboul Ella Hassanien
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6059)


In this paper, we present an automated approach for Ultrasound Biomicroscopy (UBM) glaucoma images analysis. To increase the efficiency of the introduced approach, an intensity adjustment process is applied first using the Pulse Coupled Neural Network with a median filter. This is followed by applying the PCNN-based segmentation algorithm to detect the boundary of the anterior chamber of the eye image. Then, glaucoma clinical parameters have been calculated and normalized, followed by application of a rough set analysis to discover the dependency between the parameters and to generate set of reduct that contains minimal number of attributes. Experimental results show that the introduced approach is very successful and has high detection accuracy.


Rough Sets Classification PCNN glaucoma images analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Quigley, H.A., Broman, A.T.: The number of people with glaucoma worldwide in 2010 and 2020. Br. J. Ophthalmol. 90(3), 262–267 (2006)CrossRefGoogle Scholar
  2. 2.
    Razeghinejad, M.R., Kamali-Sarvestani, E.: The plateau iris component of primary angle closure glaucoma. Developmental or acquired Medical Hypotheses 69, 95–98 (2007)CrossRefGoogle Scholar
  3. 3.
    Kaushik, S., Jain, R., Pandav, S.S., Gupta, A.: Evaluation of the anterior chamber angle in Asian Indian eyes by ultrasound biomicroscopy and gonioscopy. Indian Journal of Ophthalmology 54(3), 159–163 (2006)CrossRefGoogle Scholar
  4. 4.
    Quigley, H.A.: Number of people with glaucoma worldwide. Br. J. Ophthalmol. 80, 389–393 (1996)CrossRefGoogle Scholar
  5. 5.
    Radhakrishnan, S., Goldsmith, J., Huang, D., Westphal, V., Dueker, D.K., Rollins, A.M., Izatt, J.A., Smith, S.D.: Comparison of optical coherence tomography and ultrasound biomicroscopy for detection of narrow anterior chamber angles. Arch. Ophthalmol. 123(8), 1053–1059 (2005)CrossRefGoogle Scholar
  6. 6.
    Urbak, S.F.: Ultrasound Biomicroscopy. I. Precision of measurements. Acta Ophthalmol. Scand. 76(11), 447–455 (1998)CrossRefGoogle Scholar
  7. 7.
    Deepak, B.: Ultrasound biomicroscopy An introduction. Journal of the Bombay Ophthalmologists Association 12(1), 9–14 (2002)Google Scholar
  8. 8.
    Zhang, Y., Sankar, R., Qian, W.: Boundary delineation in transrectal ultrasound image for prostate cancer. Computers in Biology and Medicine 37(11), 1591–1599 (2007)CrossRefGoogle Scholar
  9. 9.
    Youmaran, R., Dicorato, P., Munger, R., Hall, T., Adler, A.: Automatic detection of features in ultrasound images of the Eye. Proceedings of the IEEE 3, 1829–1834 (2005); IMTC (16-19 May 2005) Ottawa, CanadaGoogle Scholar
  10. 10.
    Pal, S.K., Polkowski, S.K., Skowron, A. (eds.): Rough-Neuro Computing: Techniques for Computing with Words. Springer, Berlin (2002)Google Scholar
  11. 11.
    Pawlak, Z.: Rough Sets. Int. J. Computer and Information Sci. 11, 341–356 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Grzymala-Busse, J., Pawlak, Z., Slowinski, R., Ziarko, W.: Rough Sets. Communications of the ACM 38(11), 1–12 (1999)Google Scholar
  13. 13.
    El-dahshan, E., Redi, A., Hassanien, A.E., Xiao, K.: Accurate Detection of Prostate Boundary in Ultrasound Images Using Biologically inspired Spiking Neural Network. In: International Symposium on Intelligent Siganl Processing and Communication Systems Proceeding, Xiamen, China, November 28-December 1, pp. 333–336 (2007)Google Scholar
  14. 14.
    Hassanien, A.E.: Pulse coupled Neural Network for Detection of Masses in Digital Mammogram. Neural Network World Journal 2(6), 129–141 (2006)Google Scholar
  15. 15.
    Pavlin, C.J., Harasiewicz, K., Foster, F.S.: Ultrasound biomicroscopy of anterior segment structures in normal and glaucomatous eyes. Am. J. Ophthalmol. 113, 381–389 (1992)Google Scholar
  16. 16.
    Hodge, A.C., Fenstera, A., Downey, D.B., Ladak, H.M.: Prostate boundary segmentation from ultrasound images using 2D active shape models: Optimisation and extension to 3D. Computer Methods and Programs in Biomedicine 8(4), 99–113 (2006)CrossRefGoogle Scholar
  17. 17.
    Gohdo, T., Tsumura, T., Iijima, H., Kashiwagi, K., Tsukahara, S.: Ultrasound biomicroscopic study of ciliary body thickness in eyes with narrow angles. American Journal of Ophthamology 129(3), 342–346 (2000)CrossRefGoogle Scholar
  18. 18.
    Ning, S., Xiaohua, H., Ziarko, W., Cercone, N.: A Generalized Rough Sets Model. In: Proceedings of the 3rd Pacific Rim International Conference on Artificial Intelligence, Beijing, China, vol. 431, pp. 437–443. Int. Acad. Publishers (1994)Google Scholar
  19. 19.
    Sbeity, Z., Dorairaj, S.K., Reddy, S., Tello, C., Liebmann, J.M., Ritch, R.: Ultrasound biomicroscopy of zonular anatomy in clinically unilateral exfoliation syndrome. Acta Ophthalmol. 86(5), 565–568 (2008)CrossRefGoogle Scholar
  20. 20.
    Dorairaj, S.K., Tello, C., Liebmann, J.M., Ritch, R.: Narrow Angles and Angle Closure: Anatomic Reasons for Earlier Closure of the Superior Portion of the Iridocorneal Angle. Acta Ophthalmol. 125, 734–739 (2007); Okamoto F., Nakano S., Okamoto C., et al.: Ultrasound biomicroscopic findings in aniridia. Amer. J. Ophthalmol. 137(5), 858–862 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Soumya Banerjee
    • 1
  • Hameed Al-Qaheri
    • 2
  • El-Sayed A. El-Dahshan
    • 3
  • Aboul Ella Hassanien
    • 4
  1. 1.CS Dept.Birla Inst. of TechnologyMesraIndia
  2. 2.IS Dept.Kuwait University, CBAKuwait
  3. 3.Physics Dept., Faculty of ScienceAin Shams University, AbbassiaCairoEgypt
  4. 4.Information Technology Department, FCICairo UniversityOrmanEgypt

Personalised recommendations