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Multimedia Tools and Applications

, Volume 78, Issue 10, pp 12783–12803 | Cite as

Using orthogonal locality preserving projections to find dominant features for classifying retinal blood vessels

  • Devanjali RelanEmail author
  • Lucia Ballerini
  • Emanuele Trucco
  • Tom MacGillivray
Article

Abstract

Automatically classifying retinal blood vessels appearing in fundus camera imaging into arterioles and venules can be problematic due to variations between people as well as in image quality, contrast and brightness. Using the most dominant features for retinal vessel types in each image rather than predefining the set of characteristic features prior to classification may achieve better performance. In this paper, we present a novel approach to classifying retinal vessels extracted from fundus camera images which combines an Orthogonal Locality Preserving Projections for feature extraction and a Gaussian Mixture Model with Expectation-Maximization unsupervised classifier. The classification rate with 47 features (the largest dimension tested) using OLPP on our own ORCADES dataset and the publicly available DRIVE dataset was \(90.56\%\) and \(86.7\%\) respectively.

Keywords

Retina Fundus images Vessel classification Feature extraction Orthogonal locality preserving projections (OLPP) 

Notes

Acknowledgment

This work was supported by Leverhulme Trust grant RPG-419 “Discovery of retinal biomarkers for genetics with large cross-linked datasets”. Support from NHS Lothian R&D and the Edinburgh Clinical Research Imaging Centre is gratefully acknowledged.

References

  1. 1.
    Abràmoff MD, Garvin MK, Sonka M (2010) Retinal imaging and image analysis. IEEE Rev Biomed Eng 3:169–208CrossRefGoogle Scholar
  2. 2.
    Adlakha A, Chhikara RR (2016) Comparative analysis of filter feature selection techniques with different classifiers for image steganalysis. In: 2016 international conference on computing, communication and automation (ICCCA). IEEE, pp 1122–1127Google Scholar
  3. 3.
    Al-Juaid AAG, Nouf A, Khan E (2018) Enhancing pc data security via combining rsa cryptography and video based steganography. J Inf Secur Cyber Res (JISCR) 1(1):8–18Google Scholar
  4. 4.
    Al-Otaibi NA, Gutub AA (2014) 2-leyer security system for hiding sensitive text data on personal computers. Lecture Notes Inf Theory 2(2):151–157Google Scholar
  5. 5.
    Alassaf N, Alkazemi B, Gutub A (2017) Applicable light-weight cryptography to secure medical data in iot systems. J Res Eng Appl Sci (JREAS) 04:50–58Google Scholar
  6. 6.
    Alharthi N, Gutub A (2017) Data visualization to explore improving decision-making within Hajj services. Sci Modell Res 2(1):9–18Google Scholar
  7. 7.
    Aly S, Gutub A (2018) Intelligent recognition system for identifying items and pilgrims. NED University Journal of Research, vol. Thematic Issue on Advances in Image and Video Processing, pp 17–23Google Scholar
  8. 8.
    Baker ML, Hand PJ, Wang JJ, Wong TY (2008) Retinal signs and stroke. Stroke 39(4):1371–1379CrossRefGoogle Scholar
  9. 9.
    Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer-Verlag New York Inc., SecaucuszbMATHGoogle Scholar
  10. 10.
    Cai D, He X (2005) Orthogonal locality preserving indexing. In: Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval AMC, pp 3–10Google Scholar
  11. 11.
    Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8:679–98CrossRefGoogle Scholar
  12. 12.
    Chen Y, He F, Wu Y, Hou N (2017) A local start search algorithm to compute exact hausdorff distance for arbitrary point sets. Pattern Recogn 67:139–148CrossRefGoogle Scholar
  13. 13.
    Chrástek R, Wolf M, Donath K, Niemann H, Paulus D, Hothorn T, Lausen B, Lämmer R, Mardin CY, Michelson G (2005) Automated segmentation of the optic nerve head for diagnosis of glaucoma. Med Image Anal 9:297–314CrossRefGoogle Scholar
  14. 14.
    Dashtbozorg B, Mendonċa AM, Campilho A (2014) An automatic graph-based approach for artery/vein classification in retinal images. IEEE Trans Image Process 23 (3):1073–1083MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Frost S, Kanagasingam Y, Sohrabi H, Vignarajan J, Bourgeat P, Salvado O, Villemagne V, Rowe C, Macaulay SL, Szoeke C et al (2013) Retinal vascular biomarkers for early detection and monitoring of alzheimer’s disease. Transl Psychiatry 3(2):e233CrossRefGoogle Scholar
  16. 16.
  17. 17.
    Giachetti A, Chin KS, Trucco E, Cobb C, Wilson PJ (2011) Multiresolution localization and segmentation of the optical disc in fundus images using inpainted background and vessel information. In: 2011 18th IEEE international conference on image processing, pp 2145–2148Google Scholar
  18. 18.
    Grisan E, Ruggeri A (2003) A divide et impera strategy for automatic classification of retinal vessels into arteries and veins. In: Proceedings of the 25th annual international conference of the IEEE engineering in medicine and biology society, 2003, vol 1. IEEE, pp 890–893Google Scholar
  19. 19.
    Gutub A, Alharthi N (2016) Improving hajj and umrah services utilizing exploratory data visualization techniques. In: 16th scientific Hajj research Forum, Organized by the Custodian of the Two Holy Mosques Institute for Hajj Research, Umm Al-Qura University - King Abdulaziz Historical Hall, Makkah, Saudi ArabiaGoogle Scholar
  20. 20.
    Gutub A, Aljuaid N (2018) Multi-bits stego-system for hiding text in multimedia images based on user security priority. Journal of Computer Hardware Engineering, 04Google Scholar
  21. 21.
    Gutub A, Al-Juaid N, Khan E (2017) Counting-based secret sharing technique for multimedia applications. Multimedia Tools and Applications, pp 1–29Google Scholar
  22. 22.
    Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. ACM SIGKDD Explor Newsl 11 (1):10–18CrossRefGoogle Scholar
  23. 23.
    Hamednejad G, Pourghassem H (2015) Retinal blood vessel classification based on color and directional features in fundus images. In: 2015 22nd Iranian conference on biomedical engineering (ICBME). IEEE, pp 257–262Google Scholar
  24. 24.
    Hatami N, Goldbaum M (2016) Automatic identification of retinal arteries and veins in fundus images using local binary patterns. arXiv:1605.00763
  25. 25.
    He X, Cai D, Liu H, Ma W-Y (2004) Locality preserving indexing for document representation. In: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 96–103Google Scholar
  26. 26.
    Jelinek H, Depardieu C, Lucas C, Cornforth D, Huang W, Cree M (2005) Towards vessel characterisation in the vicinity of the optic disc in digital retinal images. In: Image vision on computer conference. Citeseer, pp 2–7Google Scholar
  27. 27.
    Jordan KC, Menolotto M, Bolster NM, Livingstone IA, Giardini ME (2017) A review of feature-based retinal image analysis. Expert Rev Ophthalmol 12 (3):207–220CrossRefGoogle Scholar
  28. 28.
    Joshi VS, Garvin MK, Reinhardt JM, Abramoff MD (2012) Automated artery-venous classification of retinal blood vessels based on structural mapping method. In: Proceedings of SPIE medical imaging, computer-aided diagnosis, vol 8315, p 83150IGoogle Scholar
  29. 29.
    Kim S, Guy SJ, Hillesland K, Zafar B, Gutub AA-A, Manocha D (2015) Velocity-based modeling of physical interactions in dense crowds. Vis Comput 31(5):541–555CrossRefGoogle Scholar
  30. 30.
    Kondermann C, Kondermann D, Yan M (2007) Blood vessel classification into arteries and veins in retinal images. In: Medical imaging. International Society for Optics and Photonics, pp 651247–651247Google Scholar
  31. 31.
    Leskovec J, Rajaraman A, Ullman JD (2014) Dimensionality reduction. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  32. 32.
    Li C, Diao Y, Ma H, Li Y (2008) A statistical pca method for face recognition. In: 2nd international symposium on intelligent information technology application, 2008. IITA’08, vol 3. IEEE, pp 376–380Google Scholar
  33. 33.
    Li K, He F-Z, Yu H-P, Chen X (2017) A correlative classifiers approach based on particle filter and sample set for tracking occluded target. Appl Math-A J Chinese Universities 32(3):294–312MathSciNetCrossRefGoogle Scholar
  34. 34.
    Luhach AK, et al. (2016) Analysis of lightweight cryptographic solutions for internet of things. Indian J Sci Techn 9:28Google Scholar
  35. 35.
    McQuillan R, Leutenegger A-L, Abdel-Rahman R, Franklin CS, Pericic M, Barac-Lauc L, Smolej-Narancic N, Janicijevic B, Polasek O, Tenesa A et al (2008) Runs of homozygosity in european populations. Am J Hum Genet 83 (3):359–372CrossRefGoogle Scholar
  36. 36.
    Miri M, Amini Z, Rabbani H, Kafieh R (2017) A comprehensive study of retinal vessel classification methods in fundus images. J Med Signals Sensors 7(2):59Google Scholar
  37. 37.
    Mirsharif Q, Tajeripour F, Pourreza H (2013) Automated characterization of blood vessels as arteries and veins in retinal images. Comput Med Imaging Graph 37 (7):607–617CrossRefGoogle Scholar
  38. 38.
    Muramatsu C, Hatanaka Y, Iwase T, Hara T, Fujita H (2011) Automated selection of major arteries and veins for measurement of arteriolar-to-venular diameter ratio on retinal fundus images. Comput Med Imaging Graph 35:472–80CrossRefGoogle Scholar
  39. 39.
    Niemeijer M, van Ginneken B, Abràmoff MD (2009) Automatic classification of retinal vessels into arteries and veins. In: SPIE medical imaging. International Society for Optics and Photonics, pp 72601F–72601FGoogle Scholar
  40. 40.
    Niemeijer M, Xu X, Dumitrescu AV, Gupta P, van Ginneken B, Folk JC, Abramoff MD (2011) Automated measurement of the arteriolar-to-venular width ratio in digital color fundus photographs. IEEE Trans Med Imaging 30:1941–50CrossRefGoogle Scholar
  41. 41.
    Ong EP, Lee JA, Xu G, Lee BH, Wong DW (2016) An automatic quantitative measurement method for performance assessment of retina image registration algorithms. In: 2016 IEEE 38th annual international conference of the engineering in medicine and biology society (EMBC). IEEE, pp 3252–3255Google Scholar
  42. 42.
    Ong EP, Xu Y, Wong DWK, Liu J (2015) Retina verification using a combined points and edges approach. In 2015 IEEE international conference on image processing (ICIP). IEEE, pp 2720–2724Google Scholar
  43. 43.
    Phillips PJ, Flynn PJ, Scruggs T, Bowyer KW, Chang J, Hoffman K, Marques J, Min J, Worek W (2005) Overview of the face recognition grand challenge. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1. IEEE, pp 947–954Google Scholar
  44. 44.
    Piramuthu S (1999) The hausdorff distance measure for feature selection in learning applications. In: Proceedings of the 32nd annual hawaii international conference on systems sciences, 1999. HICSS-32. IEEE, pp 6–ppGoogle Scholar
  45. 45.
    Pudil P (1994) Floating search methods in feature selection. Pattern Recogn Lett 15:1119–1125CrossRefGoogle Scholar
  46. 46.
    Relan D, MacGillivray T, Ballerini L, Trucco E (2013) Retinal vessel classification: sorting arteries and veins. In: 35th annual international conference of the IEEE EMBS engineering in medicine and biology society (EMBC). Osaka, Japan, pp 7396 –7399Google Scholar
  47. 47.
    Saez M, González-Vázquez S, González-Penedo M, Barceló M. A, Pena-Seijo M, de Tuero GC, Pose-Reino A (2012) Development of an automated system to classify retinal vessels into arteries and veins. Comput Methods Programs Biomed 108(1):367–376CrossRefGoogle Scholar
  48. 48.
    Sangariand S, Manickam L (2014) A light-weight cryptography analysis for wireless based healthcare applications. J Comput Sci 10(10):2088–2094CrossRefGoogle Scholar
  49. 49.
    Staal J, Abràmoff MD, Niemeijer M, Viergever MA, Van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509CrossRefGoogle Scholar
  50. 50.
    Tang B, Li F, Qin Y (2011) Fault diagnosis model based on feature compression with orthogonal locality preserving projection. Chin J Mech Eng 24(5):891–898CrossRefGoogle Scholar
  51. 51.
    Vazquez S, Cancela B, Barreira N, Penedo MG, Saez M (2010) On the automatic computation of the arterio-venous ratio in retinal images: using minimal paths for the artery/vein classification. In: 2010 international conference on digital image computing: Techniques and applications (DICTA), pp 599–604 IEEEGoogle Scholar
  52. 52.
    Vijayakumar V, Koozekanani DD, White R, Kohler J, Roychowdhury S, Parhi KK (2016) Artery/vein classification of retinal blood vessels using feature selection. In: 2016 IEEE 38th annual international conference of the engineering in medicine and biology society (EMBC). IEEE, pp 1320–1323Google Scholar
  53. 53.
    Xu X, Ding W, Abràmoff MD, Cao R (2017) An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image. Comput Methods Programs Biomed 141:3–9CrossRefGoogle Scholar
  54. 54.
    Yan X, He F, Hou N, Ai H (2018) An efficient particle swarm optimization for large-scale hardware/software co-design system. Int J Coop Inf Syst 27(1):1741001Google Scholar
  55. 55.
    Yang C, Zhang Y, Wang P, Luo X, Liu F, Lu J (2017) Steganalysis feature subspace selection based on fisher criterion, pp 514–521, vol 10Google Scholar
  56. 56.
    Yoon JW (2013) An efficient model selection for gaussian mixture model in a bayesian framework. arXiv:1307.0995
  57. 57.
    Zamperini A, Giachetti A, Trucco E, Chin KS (2012) Effective features for artery-vein classification in digital fundus images. In: 2012 25th international symposium on computer-based medical systems (CBMS). IEEE, pp 1–6Google Scholar
  58. 58.
    Zhou Y, He F, Hou N, Qiu Y (2018) Parallel ant colony optimization on multi-core simd cpus. Futur Gener Comput Syst 79:473–487CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUK
  2. 2.Computing, School of Science and EngineeringUniversity of DundeeDundeeUK

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