Low-Power Extended Binary Pattern Image Feature Extraction

  • S. Arul JothiEmail author
  • M. Ramkumar Raja
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 65)


The blindness in the people is due to various reasons such as age-related macular degeneration (AMD) and diabetic retinopathy (DR) [1]. This work deals with identification of affected and healthy images based on the discrimination capabilities in fundus image textures. For this purpose, texture descriptor algorithm extended binary pattern (EBP) is used for retinal images and the area and time consumption has been reduced by the means of it. The main aim of the work is to reduce time consumption and also categorize the retinal diseases with the retina background texture.


Extended binary patterns Retinal disease 


  1. 1.
    Morales S, Engan K, Naranjo V, Colomer A (2015) Retinal disease screening through local binary patternsGoogle Scholar
  2. 2.
    Ojala T, Pietikinen M, Menp T (2001) A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification. In: 2nd international conference on advances in pattern recognition, pp 397–406Google Scholar
  3. 3.
    Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12)CrossRefGoogle Scholar
  4. 4.
    Kotu L, Engan K, Eftestol T, Woie L, Orn S, Katsaggelos A (2012) Local binary patterns used on cardiac MRI to classify high and low risk patient groups. In: Proceedings of the 20th European signal processing conference (EUSIPCO), pp 2586–2590Google Scholar
  5. 5.
    Oppedal K, Engan K, Aarsland D, Beyer M, Tysnes OB, Eftestol T (2012) Using local binary pattern to classify dementia in MRI. In: 9th IEEE international symposium on biomedical imaging (ISBI), May 2012, pp 594–597Google Scholar
  6. 6.
    Mookiah M, Acharya UR, Martis RJ, Chua CK, Lim C, Ng E, Laude A (2013) Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach. Knowl-Based Syst 39:9–22CrossRefGoogle Scholar
  7. 7.
    Garnier M, Hurtut T, Ben Tahar H, Cheriet F (2014) Automatic multiresolution age-related macular degeneration detection from fundus images. In: Proceedings of SPIE, vol 9035, pp 903532–903532–7Google Scholar
  8. 8.
    Guo Z, Zhang D, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663MathSciNetCrossRefGoogle Scholar
  9. 9.
    Maenpaa T, Pietikainen M (2003) Multi-scale binary patterns for texture analysis. In: Bigun J, Gustavsson T (eds) Image analysis. Lecture notes in computer science, vol 2749, pp 885–892CrossRefGoogle Scholar
  10. 10.
    Liao S, Law M, Chung A (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118MathSciNetCrossRefGoogle Scholar
  11. 11.
    Song T, Li H, Meng F, Wu Q, Luo B, Zeng B, Gabbouj M (2014) Noise-robust texture description using local contrast patterns via global measures. IEEE Signal Process Lett 21(1):93–96CrossRefGoogle Scholar
  12. 12.
    World Health Organization (WHO) (2013) Universal eye health: a global action plan 2014–2019Google Scholar
  13. 13.
    World Health Organization (WHO) (2010) Action plan for the prevention of avoidable blindness and visual impairment 2009–2013Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.ECE DepartmentSri Ramakrishna Engineering CollegeCoimbatoreIndia
  2. 2.ECE DepartmentCoimbatore Institute of Engineering and TechnologyCoimbatoreIndia

Personalised recommendations