Retinal image registration using topological vascular tree segmentation and bifurcation structures

Abstract This paper presents a new retinal image segmentation and registration approaches. The contribution of this paper is two-fold. First, the conventional vessel-tracking methods use local sequential searching, which can be easily trapped by local intensity discontinuity or vessel rupture. The proposed method uses global graph-based decision that can segment the topological vascular tree with 1-pixel width and fully connection from retinal images. Staring from initial multi-scale ridge segmentation, the disconnected vessels are retrospectively connected and then spurious ridges are removed using a shortest path algorithm on a specially defined graph. The hypothesis testing is defined in terms of probability of pixel belong to foreground and background, which enables that the false detections could be removed. Second, the conventional point-matching methods largely depend on the branching angles of single bifurcation point. The feature correspondence across two images may not be unique due to the similar angle values. In view of this, structure-matching registration is favored. The bifurcation structure is composed of a master bifurcation point and its three connected neighboring pixels or vessel segments. The characteristic vector of each bifurcation structure consists of the normalized branching angle and length, which is fairly robust to be against translation, rotation, scaling, and even modest distortion. The experimental results are presented to demonstrate the superior performance of the proposed approach.

[1]  Kotagiri Ramamohanarao,et al.  An effective retinal blood vessel segmentation method using multi-scale line detection , 2013, Pattern Recognit..

[2]  Robyn A. Owens,et al.  Registration of stereo and temporal images of the retina , 1999, IEEE Transactions on Medical Imaging.

[3]  Yang Xiang,et al.  Retinal image registration using bifurcation structures , 2011, 2011 18th IEEE International Conference on Image Processing.

[4]  Milan Sonka,et al.  Vessel Boundary Delineation on Fundus Images Using Graph-Based Approach , 2011, IEEE Transactions on Medical Imaging.

[5]  Konstantina S. Nikita,et al.  Automatic retinal image registration scheme using global optimization techniques , 1999, IEEE Transactions on Information Technology in Biomedicine.

[6]  Stephen A. Davis,et al.  Retina Verification System Based on Biometric Graph Matching , 2013, IEEE Transactions on Image Processing.

[7]  Charles V. Stewart,et al.  A Feature-Based, Robust, Hierarchical Algorithm for Registering Pairs of Images of the Curved Human Retina , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  George K. Matsopoulos,et al.  Multimodal registration of retinal images using self organizing maps , 2004, IEEE Transactions on Medical Imaging.

[9]  Mathews Jacob,et al.  Design of steerable filters for feature detection using canny-like criteria , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Amir Averbuch,et al.  Pseudopolar-based estimation of large translations, rotations, and scalings in images , 2005, IEEE Trans. Image Process..

[11]  Lei Zhang,et al.  Retinal vessel extraction by matched filter with first-order derivative of Gaussian , 2010, Comput. Biol. Medicine.

[12]  Xing Zhang,et al.  Salient Feature Region: A New Method for Retinal Image Registration , 2011, IEEE Transactions on Information Technology in Biomedicine.

[13]  Ronald M. Summers,et al.  Grey-Scale Skeletonization of Small Vessels in Magnetic Resonance Angiography , 2000, IEEE Trans. Medical Imaging.

[14]  Hong Shen,et al.  Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms , 1999, IEEE Transactions on Information Technology in Biomedicine.

[15]  Yasser M. Kadah,et al.  Survey of Retinal Image Segmentation and Registration , 2006 .

[16]  Jayaram K. Udupa,et al.  User-Steered Image Segmentation Paradigms: Live Wire and Live Lane , 1998, Graph. Model. Image Process..

[17]  Chia-Ling Tsai,et al.  The dual-bootstrap iterative closest point algorithm with application to retinal image registration , 2003, IEEE Transactions on Medical Imaging.

[18]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[19]  Demetri Terzopoulos,et al.  T-snakes: Topology adaptive snakes , 2000, Medical Image Anal..

[20]  Mona Kathryn Garvin,et al.  Multimodal Retinal Vessel Segmentation From Spectral-Domain Optical Coherence Tomography and Fundus Photography , 2012, IEEE Transactions on Medical Imaging.

[21]  Bunyarit Uyyanonvara,et al.  An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation , 2012, IEEE Transactions on Biomedical Engineering.

[22]  Jürgen Weese,et al.  A comparison of similarity measures for use in 2-D-3-D medical image registration , 1998, IEEE Transactions on Medical Imaging.

[23]  Frédéric Zana,et al.  A multimodal registration algorithm of eye fundus images using vessels detection and Hough transform , 1999, IEEE Transactions on Medical Imaging.

[24]  Bram van Ginneken,et al.  Comparative study of retinal vessel segmentation methods on a new publicly available database , 2004, SPIE Medical Imaging.

[25]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[26]  Yulong Shen,et al.  Registration and fusion of retinal images-an evaluation study , 2003, IEEE Transactions on Medical Imaging.

[27]  Stefan Carlsson,et al.  Combining Appearance and Topology for Wide Baseline Matching , 2002, ECCV.

[28]  P. Eichel,et al.  A method for a fully automatic definition of coronary arterial edges from cineangiograms. , 1988, IEEE transactions on medical imaging.

[29]  Kotagiri Ramamohanarao,et al.  Retinal Image Matching Using Hierarchical Vascular Features , 2011, Comput. Intell. Neurosci..

[30]  Salah Bourennane,et al.  Retinal vessel segmentation using a probabilistic tracking method , 2012, Pattern Recognit..

[31]  Max A. Viergever,et al.  A survey of medical image registration , 1998, Medical Image Anal..

[32]  Kotagiri Ramamohanarao,et al.  Automatic Detection of Vascular Bifurcations and Crossovers from Color Retinal Fundus Images , 2007, 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System.

[33]  Martina Broehan,et al.  Real-Time Multimodal Retinal Image Registration for a Computer-Assisted Laser Photocoagulation System , 2011, IEEE Transactions on Biomedical Engineering.

[34]  José Manuel Bravo,et al.  A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features , 2011, IEEE Transactions on Medical Imaging.

[35]  Liang Zhou,et al.  The detection and quantification of retinopathy using digital angiograms , 1994, IEEE Trans. Medical Imaging.

[36]  Kotagiri Ramamohanarao,et al.  Retinal artery-vein caliber grading using color fundus imaging , 2013, Comput. Methods Programs Biomed..

[37]  Francis K. H. Quek,et al.  A review of vessel extraction techniques and algorithms , 2004, CSUR.

[38]  Frédéric Zana,et al.  Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation , 2001, IEEE Trans. Image Process..