Determining diabetes using iris recognition system

Original Article

Abstract

Iridology is a science which correlates the apparitions of iris to tissue weaknesses in the body. It merely reveals weaknesses, inflammation, or toxicity in organs or tissues. It also indicates weakness long before the symptoms appear. In this paper, support vector machine-based iris recognition system utilizing iridology has been used to determine diabetes. Features from eye image database of 40 people having healthy eye (normal) and having affected eye (diabetes) have been extracted by 2-D wavelet tree. The overall accuracy is obtained to be 87.50 % which reasonably demonstrates the effectiveness of the system.

Keywords

Iridology Diabetes Wavelets Support vector machine 

References

  1. 1.
    Wibawa AD, Purnomo MH. Early detection on the condition of pancreas organ as the cause of diabetes mellitus by real time iris image processing. IEEE Asia Pacific Conference on Circuits and Systems. APCCS-2006; 1008-10.Google Scholar
  2. 2.
    Ma L, Li N. Texture feature extraction and classification for iris diagnosis. International conference on medical biometrics, Lecture notes in computer science, Springer-Verlag. 2007; 168-75Google Scholar
  3. 3.
    Othman Z, Prabuwono A S. Preliminary study on iris recognition system: tissues of body organs in iridology. IEEE, EMBS, Conference on Biomedical Engineering & Sciences (IECBES 2010), Kuala Lumpur, Malaysia, 30th Nov.-2nd Dec. 2010Google Scholar
  4. 4.
    Ramlee RA, Ranjit S. Using iris recognition algorithm, detecting cholesterol presence. IEEE: Proceedings of International Conference on Information Management and Engineering; 2009.CrossRefGoogle Scholar
  5. 5.
    Lai C, Chiu C. Health examination based on iris images. 9th International Conference on Machine Learning and Cybernetics, Qingdo, 11-14 July 2010.Google Scholar
  6. 6.
    Lesmana IPD, Purnama IKE, Purnomo MH. Abnormal condition detection of pancreatic beta-cells as the cause of diabetes mellitus based on iris image. International Conference on Instrumentation, Communication, Information Technology and Biomedical Engineering. 2011Google Scholar
  7. 7.
    Stearn N, Swanepoel DW. Identifying hearing loss by means of iridology. Afr J Tradit, Compliment Altern Med. 2007;4:205–10.Google Scholar
  8. 8.
    Jensen B. The science and practice of iridology. California Bernard Jensen Co. 1985;1Google Scholar
  9. 9.
    A.D.A.M. Medical encyclopedia. Diabetes. US National library of Medicine, http://www.ncbi.nlm.nih.gov/pubmedhealth/PMH0002194/. Accessed 15 Sep. 2011.
  10. 10.
    Diabetes mellitus. http://en.wikipedia.org/wiki/Diabetes_mellitus. Accessed 15 Sep. 2011.
  11. 11.
    I-SCAN-2 dual iris scanner. http://www.crossmatch.com/i-scan-2.php. Accessed 16 July 2011.
  12. 12.
    Daughman J. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell. 1993;15:1148–61.CrossRefGoogle Scholar
  13. 13.
    Daughman J. How iris recognition works? IEEE Trans Circuits Syst for Video Technol. 2004;14:21–30.CrossRefGoogle Scholar
  14. 14.
    Ritter N. Location of the pupil-iris border in slit-lamp images of the cornea. International Conference on Image Analysis and Processing, 1999Google Scholar
  15. 15.
    Wildes R, Asmuth J, Green G, Hsu S, Kolczynski R, Matey J, McBride S. A system for automated iris recognition. IEEE Workshop on Applications of Computer Vision, Sarasota, FL. 1994; 121-28.Google Scholar
  16. 16.
    Kong W, Zhang D. Accurate iris segmentation based on novel reflection and eyelash detection model. International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong. 2001.Google Scholar
  17. 17.
    Tisse C, Martin L, Torres L, Robert M. Person identification technique using human iris recognition. International Conference on Vision Interface, Canada. 2002Google Scholar
  18. 18.
    Ma L, Wang Y, Tan T. Iris recognition using circular symmetric filters. National Laboratory of Pattern Recognition. Institute of Automation, Chinese Academy of Sciences. 2002.Google Scholar
  19. 19.
    Zhu Y, Tan T, Wang Y. Biometric personal identification based on iris patterns. IEEE international conference on pattern recognition. 2000; 2801-04.Google Scholar
  20. 20.
    Jensen B. Iridology charts. http://www.bernardjensen.com/iridology-charts-c-38_42.html. Accessed 5 Jun. 2011.
  21. 21.
    He X, Shi P. Extraction of complex wavelet features for iris recognition. 19th International Conference on Pattern Recognition, ICPR’08. 2008; 1–4.Google Scholar
  22. 22.
    Wang J, Xie M. Iris feature extraction based on wavelet packet analysis. Proc Int Conf Commun Circuits Syst. 2006;1:31–4.Google Scholar
  23. 23.
    Rydgren E, Thomas EA, Amiel F, Rossant F, Amara A. Iris features extraction using wavelet packets. International Conference on Image Processing, ICIP'04. 2004; 2:861–4.Google Scholar
  24. 24.
    Ko Jong-Gook, Gil Yeon-Hee, Yoo Jang-Hee. Iris recognition using cumulative sum based change analysis. International symposium on intelligent signal processing and communication system. 2006; 275-278Google Scholar
  25. 25.
    Jafar MH, Ali Hassanien AE. An iris recognition system to enhance E-security environment based on wavelet theory. AMO J. 2003;5:93–104.Google Scholar
  26. 26.
    Poursaberi A, Arrabi BN. A novel iris recognition system using morphological edge detector and wavelet phase features. International Congress on GVIP-2005.Google Scholar
  27. 27.
    Ali H, Salami MJE, Wahyudi. Iris recognition system by using support vector machines. International Conference on Computer and Communication Engineering. 2008.Google Scholar
  28. 28.
    Roy K, Bhattacharya P. Collarette area localization and asymmetrical SVM for efficient iris recognition. 14th International Conference on Image Analysis and Processing, ICIAP 2007.Google Scholar
  29. 29.
    Cortes C, Vapnik V. Support vector networks. Mach Learn. 1995;20:273–97.Google Scholar
  30. 30.
    Burges CJC. A tutorial on support vector machines for pattern recognition. Boston: Kluwer Academic Publishers; 1998.Google Scholar
  31. 31.
    Cristianini N, Shawe TD. An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press; 2000.CrossRefGoogle Scholar
  32. 32.
    Haykin S. Neural networks—a comprehensive foundation. Pearson Education, second edition. 2004.Google Scholar

Copyright information

© Research Society for Study of Diabetes in India 2015

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringG.L.A. UniversityMathuraIndia
  2. 2.Electrical and Instrumentation Engineering DepartmentThapar UniversityPatialaIndia
  3. 3.School of Mathematics and Computer ApplicationsThapar UniversityPatialaIndia

Personalised recommendations