Performance Evaluation of State-of-the-Art Local Feature Detectors and Descriptors in the Context of Longitudinal Registration of Retinal Images

  • Sajib K. Saha
  • Di Xiao
  • Shaun Frost
  • Yogesan Kanagasingam
Image & Signal Processing
Part of the following topical collections:
  1. Image & Signal Processing

Abstract

In this paper we systematically evaluate the performance of several state-of-the-art local feature detectors and descriptors in the context of longitudinal registration of retinal images. Longitudinal (temporal) registration facilitates to track the changes in the retina that has happened over time. A wide number of local feature detectors and descriptors exist and many of them have already applied for retinal image registration, however, no comparative evaluation has been made so far to analyse their respective performance. In this manuscript we evaluate the performance of the widely known and commonly used detectors such as Harris, SIFT, SURF, BRISK, and bifurcation and cross-over points. As of descriptors SIFT, SURF, ALOHA, BRIEF, BRISK and PIIFD are used. Longitudinal retinal image datasets containing a total of 244 images are used for the experiment. The evaluation reveals some potential findings including more robustness of SURF and SIFT keypoints than the commonly used bifurcation and cross-over points, when detected on the vessels. SIFT keypoints can be detected with a reliability of 59% for without pathology images and 45% for with pathology images. For SURF keypoints these values are respectively 58% and 47%. ALOHA descriptor is best suited to describe SURF keypoints, which ensures an overall matching accuracy, distinguishability of 83%, 93% and 78%, 83% for without pathology and with pathology images respectively.

Keywords

Feature detector Feature descriptor Registration Fundus image Image registration 

Notes

Compliance with Ethical Standards

Conflict of Interest

Sajib Kumar Saha declares that he has no conflict of interest. Di Xiao declares that he has no conflict of interest. Shaun Frost declares that he has no conflict of interest. Yogesan Kanagasingam declares that he has no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

References

  1. 1.
    Zitova, B., and Flusser, J., Image registration methods: A survey. Image Vis Comput. 21(11):977–1000, 2003.CrossRefGoogle Scholar
  2. 2.
    Saha, S.K., Xiao, D., Frost, S., and Kanagasingam, Y., A two-step approach for longitudinal registration of retinal images. J Med Syst. 40(12):277, 2016.CrossRefPubMedGoogle Scholar
  3. 3.
    Aguilar, W., Frauel, Y., Escolano, F., Martinez-Perez, M.E., Espinosa-Romero, A., and Lozano, M.A., A robust graph transformation matching for non-rigid registration. Image Vis Comput. 27(7):897–910, 2009.CrossRefGoogle Scholar
  4. 4.
    Xing, C., and Qiu, P., Intensity-based image registration by nonparametric local smoothing. IEEE Trans Pattern Anal Mach Intell. 33(10):2081–2092, 2011.CrossRefPubMedGoogle Scholar
  5. 5.
    Zheng, Y., Daniel, E., Hunter, A.A., Xiao, R., Gao, J., Li, H., Maguire, M.G., Brainard, D.H., and Gee, J.C., Landmark matching based retinal image alignment by enforcing sparsity in correspondence matrix. Med Image Anal. 18(6):903–913, 2014.CrossRefPubMedGoogle Scholar
  6. 6.
    Can, A., Stewart, C.V., Roysam, B., and Tanenbaum, H.L., A feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina. IEEE Trans Pattern Anal Mach Intell. 24(3):347–364, 2002.CrossRefGoogle Scholar
  7. 7.
    Xiao, D., Vignarajan, J., Lock, J., Frost, S., Tay-Kearney, M., and Kanagasingam, Y., Retinal image registration and comparison for clinical decision support. Australas Med J. 5(9):507, 2012.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Chen, J., Tian, J., Lee, N., Zheng, J., Smith, R.T., and Laine, A.F., A partial intensity invariant feature descriptor for multimodal retinal image registration. IEEE Trans Biomed Eng. 57(7):1707–1718, 2010.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Ghassabi, Z., Shanbehzadeh, J., Mohammadzadeh, A., and Ostadzadeh, S.S., Colour retinal fundus image registration by selecting stable extremum points in the scale-invariant feature transform detector. IET Image Process. 9(10):889–900, 2015.CrossRefGoogle Scholar
  10. 10.
    Hernandez-Matas C, Zabulis X, Argyros AA (2015) Retinal image registration based on keypoint correspondences, spherical eye modeling and camera pose estimation. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 5650–5654. IEEE.Google Scholar
  11. 11.
    Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L., Speeded-up robust features (SURF). Comput Vis Image Underst. 110(3):346–359, 2008.CrossRefGoogle Scholar
  12. 12.
    Bay, H., Tuytelaars, T., and Van Gool, L., Surf: Speeded up robust features. Eur Conf Comput Vis. 2006:404–417, 2006.Google Scholar
  13. 13.
    Lowe, D., Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 60(2):91–110, 2004.CrossRefGoogle Scholar
  14. 14.
    Işık, Ş., and Özkan, K., A comparative evaluation of well-known feature detectors and descriptors. Int J Appl Math Electron Comput. 3(1):1–6, 2015.Google Scholar
  15. 15.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., and Van Gool, L., A comparison of affine region detectors. Int J Comput Vis. 65(1–2):43–72, 2005.CrossRefGoogle Scholar
  16. 16.
    Leutenegger S, Chli M, Siegwart RY (2011) BRISK: Binary robust invariant scalable keypoints. 2011 I.E. International Conference on Computer Vision (ICCV), 2011, pp. 2548–2555. IEEE.Google Scholar
  17. 17.
    Bhuiyan A, Nath B, Chua J, Ramamohanarao K (2007) Automatic detection of vascular bifurcations and crossovers from color retinal fundus images. Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, 2007, pp. 711–718. IEEE.Google Scholar
  18. 18.
    Harris, C., and Stephens, M., A combined corner and edge detector. Alvey Vision Conference. 15(50):10–5244, 1988.Google Scholar
  19. 19.
    Lindeberg T (1994) Scale-Space Theory in Computer Vision. Kluwer Academic Publishers, 1994, ISBN 0–7923–9418-6.Google Scholar
  20. 20.
    Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. European conference on computer vision, 2006, pp. 430–443.Google Scholar
  21. 21.
    Fairchild MD (2005) Color Appearance Models. Second Edition, John Wiley & Sons.Google Scholar
  22. 22.
    Bhuiyan A, Nath B, Chua J, Kotagiri R (2007) Blood vessel segmentation from color retinal images using unsupervised texture classification. IEEE Int. Conf. Image Process. 2007, pp. V-521.Google Scholar
  23. 23.
    Juan, L., and Gwun, O., A comparison of SIFT, PCA-SIFT and SURF. Int J Image Process (IJIP). 3:143–152, 2009.Google Scholar
  24. 24.
    Ghassabi, Z., Shanbehzadeh, J., Sedaghat, A., and Fatemizadeh, E., An efficient approach for robust multimodal retinal image registration based on UR-SIFT features and PIIFD descriptors. EURASIP J Image Video Process. 1(2013):25, 2013.CrossRefGoogle Scholar
  25. 25.
    Calonder, M., Lepetit, V., Strecha, C., and Fua, P., Brief: Binary robust independent elementary features. Eur Conf Comput Vis. 2010:778–792, 2010.Google Scholar
  26. 26.
    Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., and Fua, P., BRIEF: Computing a local binary descriptor very fast. IEEE Trans Pattern Anal Mach Intell. 34(7):1281–1298, 2012.CrossRefPubMedGoogle Scholar
  27. 27.
    Saha S, Démoulin V (2012) ALOHA: An efficient binary descriptor based on Haar features. 19th IEEE International Conference on Image Processing (ICIP), 2012, pp. 2345–2348.Google Scholar
  28. 28.
    Demoulin V, Saha S, Oisel L, Perez P (2014) Generating a binary descriptor representing an image patch. U.S. Patent 8, 687,892. 2014.Google Scholar
  29. 29.
    Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. Conf. Comput. Vis. Pattern Recognit. 2001, I-I.Google Scholar
  30. 30.
    Viola, P., and Jones, M., Robust real-time object detection. Int J Comput Vis. 4:34–47, 2001.Google Scholar
  31. 31.
    Saha, S., Tahtali, M., Lambert, A., and Pickering, M., Perceptual dissimilarity: A measure to quantify the degradation of medical images. Int Conf Digital Image Comput Tech Appl (DICTA). 2012:1–6, 2012.Google Scholar
  32. 32.
    Hernandez-Matas C, Zabulis X, Triantafyllou A, Anyfanti P, Douma S, Argyros AA (2017) FIRE: Fundus Image Registration Dataset. J. Modeling Ophthalmol.Google Scholar
  33. 33.
    Miksik O, Mikolajczyk K (2012) Evaluation of local detectors and descriptors for fast feature matching. 21st International Conference on Pattern Recognition (ICPR), 2012, pp. 2681–2684.Google Scholar
  34. 34.
    Tuytelaars, T., and Mikolajczyk, K., Local invariant feature detectors: A survey. Found Trends Comput GraphVis. 3(3):177–280, 2008.CrossRefGoogle Scholar
  35. 35.
    Stewart, C.V., Tsai, C., and Roysam, B., The dual-bootstrap iterative closest point algorithm with application to retinal image registration. IEEE Trans Med Imaging. 22(11):1379–1394, 2003.CrossRefPubMedGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Sajib K. Saha
    • 1
  • Di Xiao
    • 1
  • Shaun Frost
    • 1
  • Yogesan Kanagasingam
    • 1
  1. 1.Australian E Health Research Centre, Commonwealth Scientific and Industrial Research OrganisationPerthAustralia

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