Abstract
Accurate registration of retinal fundus images is vital in computer aided diagnosis of retinal diseases. This paper presents a robust registration method that makes use of the intensity as well as structural information of the retinal vasculature. In order to correct for illumination variation between images, a normalized-convolution based luminosity and contrast normalization technique is proposed. The normalized images are then aligned based on a vasculature-weighted mean squared difference (MSD) similarity metric. To increase robustness, we designed a multiresolution matching strategy coupled with a hierarchical registration model. The latter employs a deformation model with increasing complexity to estimate the parameters of a global second-order transformation model. The method was applied to combine 400 fundus images from 100 eyes, obtained from an ongoing diabetic retinopathy screening program, into 100 mosaics. Accuracy assessment by experienced clinical experts showed that 89 (out of 100) mosaics were either free of any noticeable misalignment or have a misalignment smaller than the width of the misaligned vessel.
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References
Abrà moff, M.D., Garvin, M., Sonka, M.: Retinal imaging and image analysis. IEEE Reviews in Biomedical Engineering 3, 169–208 (2010)
Zhou, L., Rzeszotarski, M.S., Singerman, L.J., Chokreff, J.M.: The detection and quantification of retinopathy using digital angiograms. IEEE Transactions on Medical Imaging 13(4), 619–626 (1994)
Ritter, N., Owens, R., Cooper, J., Eikelboom, R.H., Van Saarloos, P.P.: Registration of stereo and temporal images of the retina. IEEE Transactions on Medical Imaging 18(5), 404–418 (1999)
Matsopoulos, G.K., Mouravliansky, N.A., Delibasis, K.K., Nikita, K.S.: Automatic retinal image registration scheme using global optimization techniques. IEEE Transactions on Information Technology in Biomedicine 3(1), 47–60 (1999)
Can, A., Stewart, C.V., Roysam, B., Tanenbaum, H.L.: A feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 347–364 (2002)
Stewart, C.V., Tsai, C.L., Roysam, B.: The dual-bootstrap iterative closest point algorithm with application to retinal image registration. IEEE Transactions on Medical Imaging 22(11), 1379–1394 (2003)
Chanwimaluang, T., Fan, G., Fransen, S.R.: Hybrid retinal image registration. IEEE Transactions on Information Technology in Biomedicine 10(1), 129–142 (2006)
Lee, S., Abrà moff, M.D., Reinhardt, J.M.: Feature-based pairwise retinal image registration by radial distortion correction. In: Medical Imaging, p. 651220. International Society for Optics and Photonics (2007)
Zheng, J., Tian, J., Deng, K., Dai, X., Zhang, X., Xu, M.: Salient feature region: a new method for retinal image registration. IEEE Transactions on Information Technology in Biomedicine 15(2), 221–232 (2011)
Foracchia, M., Grisan, E., Ruggeri, A.: Luminosity and contrast normalization in retinal images. Medical Image Analysis 9(3), 179–190 (2005)
Knutsson, H., Westin, C.F.: Normalized and differential convolution. In: Proceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1993, pp. 515–523 (1993)
van Wijk, C., Truyen, R., van Gelder, R.E., van Vliet, L.J., Vos, F.M.: On normalized convolution to measure curvature features for automatic polyp detection. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 200–208. Springer, Heidelberg (2004)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
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Adal, K.M. et al. (2014). A Hierarchical Coarse-to-Fine Approach for Fundus Image Registration. In: Ourselin, S., Modat, M. (eds) Biomedical Image Registration. WBIR 2014. Lecture Notes in Computer Science, vol 8545. Springer, Cham. https://doi.org/10.1007/978-3-319-08554-8_10
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DOI: https://doi.org/10.1007/978-3-319-08554-8_10
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