Abstract
Existing feature descriptor-based methods on retinal image registration are mainly based on scale-invariant feature transform (SIFT) or partial intensity invariant feature descriptor (PIIFD). While these descriptors are many times being exploited, they have not been applied to color fundus and optical coherence tomography (OCT) fundus image pairs. OCT fundus images are challenging to register as they are often degraded by speckle noise. The descriptors also demand high dimensionality to adequately represent the features of interest. To this end, this paper presents a registration algorithm coined low-dimensional step pattern analysis (LoSPA), tailored to achieve low dimensionality while providing sufficient distinctiveness to effectively register OCT fundus images with color fundus photographs. The algorithm locates hypotheses of robust corner features based on connecting edges from the edge maps, mainly formed by vascular junctions. It continues with describing the corner features in a rotation invariant manner using step patterns. These customized step patterns are insensitive to intensity changes. We conduct comparative evaluation and LoSPA achieves a higher success rate in registration when compared to the state-of-the-art algorithms.
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Can, A., Stewart, C., Roysam, B., Tanenbaum, H.: A feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina. TPAMI 24(3), 347–364 (2002)
Laliberté, F., Gagnon, L., Sheng, Y.: Registration and fusion of retinal images - an evaluation study. T-MI 22(5), 661–673 (2003)
Ritter, N., Owens, R., Cooper, J., Eikelboom, R.H., Saarloos, P.P.V.: Registration of stereo and temporal images of the retina. T-MI 18(5), 404–418 (1999)
Stewart, C., Tsai, C.L., Roysam, B.: The dual-bootstrap iterative closest point algorithm with application to retinal image registration. T-MI 22(11), 1379–1394 (2003)
Yang, G., Stewart, C.V., Sofka, M., Tsai, C.L.: Alignment of challenging image pairs: Refinement and region growing starting from a single keypoint correspondence. TPAMI 23(11), 1973–1989 (2007)
Chen, J., Tian, J., Lee, N., Zheng, J., Smith, R.T., Laine, A.F.: A partial intensity invariant feature descriptor for multimodal retinal image registration. TBME 57(7), 1707–1718 (2010)
Ghassabi, Z., Sedaghat, A., Shanbehzadeh, J., Fatemizadeh, E.: An efficient approach for robust multimodal retinal image registration based on UR-SIFT features and PIIFD descriptors. IJIVP 2013(25) (2013)
Li, Y., Gregori, G., Knighton, R.W., Lujan, B.J., Rosenfeld, P.J.: Registration of OCT fundus images with color fundus photographs based on blood vessel ridge. Opt. Express 19(1), 7–16 (2011)
Tsai, C.L., Li, C.Y., Yang, G., Lin, K.S.: The edge-driven dual-bootstrap iterative closest point algorithm for registration of multimodal fluorescein angiogram sequence. T-MI 29(3), 636–649 (2010)
Golabbakhsh, M., Rabbani, H.: Vessel-based registration of fundus and optical coherence tomography projection images of retina using a quadratic registration model. IET Image Processing 7(8), 768–776 (2013)
Tsai, C.L., Stewart, C.V., Tanenbaum, H.L., Roysam, B.: Model-based method for improving the accuracy and repeatability of estimating vascular bifurcations and crossovers from retinal fundus images. Trans. Info. Tech. Biomed. 8(2), 122–130 (2004)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
Lee, J.A., Lee, B.H., Xu, G., Ong, E.P., Wong, D.W.K., Liu, J., Lim, T.H.: Geometric corner extraction in retinal fundus images. In: Proc. EMBC (2014)
Li, J., Chen, H., Chang, Y., Zhang, X.: A robust feature-based method for mosaic of the curved human color retinal images. In: Proc. BMEI, pp. 845–849 (2008)
Chen, J., Smith, R.T., Tian, J., Laine, A.F.: A novel registration method for retinal images based on local features. In: Proc. EMBC, pp. 2242–2245 (2004)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. AVC, pp. 147–151 (1988)
Bentley, J.L.: Multidimensional binary search trees used for associative searching. Comm. ACM 18(9), 509–517 (1975)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24(6), 381–395 (1981)
Jagoe, R., Blauth, C.I., Smith, P.L., Smith, J.V., Arnold, J.V., Taylor, K., Wootton, R.: Automatic geometrical registration of fluorescein retinal angiograms. Comp. and Biomed. Research 23(5), 403–409 (1990)
Matsopoulos, G.K., Asvestas, P.A., Mouravliansky, N.A., Delibasis, K.K.: Multimodal registration of retinal images using self organizing maps. T-MI 23(12), 1557–1563 (2004)
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Lee, J.A. et al. (2015). Registration of Color and OCT Fundus Images Using Low-dimensional Step Pattern Analysis. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9350. Springer, Cham. https://doi.org/10.1007/978-3-319-24571-3_26
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