An Effective Implementation of Exudate Extraction from Fundus Images of the Eye for a Content Based Image Retrieval System Through Hardware Description Language

Conference paper

Abstract

Data retrieval plays a critical role in the progress of the technology in the present day technological scenario with huge databases. Content Based Image Retrieval (CBIR) is one of the popular image retrieval techniques which find its application in varied fields including medical image analysis, historical research, military applications etc. Teleconferencing is gaining widespread acceptance in the field of medical analysis. One of the methods for testing through the above method is to use images of the fundus of the eye. Although there are CBIR algorithms that are accurate, their implementation on PC based systems suffer from long execution time. The paper proposes to accelerate the algorithm through its implementation in a mixed hardware/software platform. The first towards this process is implementation through a Verilog HDL code which can be used on a VLSI system. The extraction of features from these images may indicate the presence of infirmities namely exudates that are determined and its possible implementation through Verilog HDL is addressed in this paper. An improvement in terms of effective implementation was observed using the standard DRIVE database. The algorithm has been implemented and optimized on Xilinx ISE Design Suite version 14.2 and simulated on Modelsim simulator version 10.1d.

Keywords

Exudate Content based image retrieval CBIR Fundus Verilog Optical disc 

Notes

Acknowledgments

The authors would like to acknowledge the management of BMS College of Engineering, Bangalore for sponsoring the publishing of this work through the TEQIP—II grants.

References

  1. 1.
    Ibrahim, F., Ali, J.B., Jaais, A.F., Taib, M.N.: Expert system for early diagnosis of eye diseases infecting the Malaysian population. In: TENCON 2001. Proceedings of IEEE Region 10 International Conference on Electrical and Electronic Technology, vol. 1, pp. 430, 432 (2001)Google Scholar
  2. 2.
    Salazar-Gonzalez, A., Kaba, D., Li, Y., Liu, X.: Segmentation of blood vessels and optic disc in retinal images. IEEE J. Biomed. Health Inf. 18(99)Google Scholar
  3. 3.
    Chaum, E., Karnowski, T.P., Govindasamy, V.P., Abdelrahman, M., Tobin, K.W.: Automated diagnosis of retinopathy by content-based image retrieval. Retina 28(10), 1463–1477 (2008)CrossRefGoogle Scholar
  4. 4.
    Gururaj, C., Jayadevappa, D., Tunga, S.: Novel algorithm for exudate extraction from fundus images of the eye for a content based image retrieval system. In: 4th IEEE International Conference on Control System, Computing and Engineering (ICCSCE—2014), ISBN-978-1-4799-5685-2, pp 395–400, Penang, Malaysia, 28–30 Nov 2014Google Scholar
  5. 5.
    Asim, K.M., Basit, A., Jalil, A.: Detection and localization of fovea in human retinal fundus images. In: 2012 International Conference on Emerging Technologies (ICET), pp. 1, 5. 8–9 Oct 2012Google Scholar
  6. 6.
    Pereira, T., Barbeiro, P., Lemos, J., Morgado, M., Silva, E.: Digital image acquisition for ophthalmoscope. In: 2012 IEEE 2nd Portuguese Meeting in Bioengineering (ENBENG), pp. 1, 6. 23–25 Feb 2012Google Scholar
  7. 7.
  8. 8.
    Vidyasari, R., Sovani, I., Mengko, T.L.R., Zakaria, H.: Vessel enhancement algorithm in digital retinal fundus microaneurysms filter for nonproliferative diabetic retinopathy classification. In: 2011 2nd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), pp. 278, 281. 8–9 Nov 2011Google Scholar
  9. 9.
  10. 10.
    Russ, J.C.: Processing binary images. In: The Image Processing Handbook, CRC Press, North Carolina, p. 468 (2011)Google Scholar
  11. 11.
    Gupta, A., Moezzi, S., Taylor, A., Chatterjee, S., Jain, R., Goldbaum, I., Burgess, S.: Content-based retrieval of ophthalmological images. In: Proceedings., International Conference on Image Processing, vol. 3, pp. 703–706. IEEE (1996)Google Scholar
  12. 12.
    Sopharak, A., Dailey, M.N., Uyyanonvara, B., Barman, S., Williamson, T., Nwe, K.T., Moe, Y.A.: Machine learning approach to automatic exudate detection in retinal images from diabetic patients. J. Mod. Opt. 57(2), 124–135 (2010)CrossRefGoogle Scholar
  13. 13.
    Reza, A.W., Eswaran, C., Hati, S.: Automatic tracing optic disc and exudates from color fundus images using fixed variable thresholds. J. Med. Syst. 33(1), 73–80 (2009)CrossRefGoogle Scholar
  14. 14.
    Li, H., Chutatape, O.: Fundus image features extraction. In: Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 4 (2000)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of TelecommunicationBMS College of Engineering, Jain UniversityBengaluruIndia
  2. 2.Department of Instrumentation TechnologyJSSATE (VTU)BengaluruIndia
  3. 3.Department of TelecommunicationMSRIT, Jain UniversityBengaluruIndia

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