Segmentation of the Blood Vessels and Optic Disk in Retinal Images

Retinal image analysis is increasingly prominent as a nonintrusive diagnosis method in modern ophthalmology. In this paper, we present a novel method to segment blood vessels and optic disk in the fundus retinal images. The method could be used to support nonintrusive diagnosis in modern ophthalmology since the morphology of the blood vessel and the optic disk is an important indicator for diseases like diabetic retinopathy, glaucoma, and hypertension. Our method takes as first step the extraction of the retina vascular tree using the graph cut technique. The blood vessel information is then used to estimate the location of the optic disk. The optic disk segmentation is performed using two alternative methods. The Markov random field (MRF) image reconstruction method segments the optic disk by removing vessels from the optic disk region, and the compensation factor method segments the optic disk using the prior local intensity knowledge of the vessels. The proposed method is tested on three public datasets, DIARETDB1, DRIVE, and STARE. The results and comparison with alternative methods show that our method achieved exceptional performance in segmenting the blood vessel and optic disk.

[1]  Andrew Hunter,et al.  Optic nerve head segmentation , 2004, IEEE Transactions on Medical Imaging.

[2]  Mohammed Al-Rawi,et al.  An improved matched filter for blood vessel detection of digital retinal images , 2007, Comput. Biol. Medicine.

[3]  Xiaohui Liu,et al.  Optic disc segmentation by incorporating blood vessel compensation , 2011, 2011 IEEE Third International Workshop On Computational Intelligence In Medical Imaging.

[4]  Sung Yong Shin,et al.  On pixel-based texture synthesis by non-parametric sampling , 2006, Comput. Graph..

[5]  Bram van Ginneken,et al.  Segmentation of the Optic Disc, Macula and Vascular Arch in Fundus Photographs , 2007, IEEE Transactions on Medical Imaging.

[6]  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.

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

[8]  Ana Guadalupe Salazar Gonzalez,et al.  Structure analysis and lesion detection from retinal fundus images , 2011 .

[9]  Jayanthi Sivaswamy,et al.  Unsupervised curvature-based retinal vessel segmentation , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[10]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

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

[12]  Joni-Kristian Kämäräinen,et al.  The DIARETDB1 Diabetic Retinopathy Database and Evaluation Protocol , 2007, BMVC.

[13]  Roberto Marcondes Cesar Junior,et al.  Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification , 2005, IEEE Transactions on Medical Imaging.

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

[15]  Xiaohui Liu,et al.  Segmentation of Retinal Blood Vessels Using Gaussian Mixture Models and Expectation Maximisation , 2013, HIS.

[16]  Manuel Emilio Gegúndez-Arias,et al.  Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques , 2010, IEEE Transactions on Medical Imaging.

[17]  Heinrich Niemann,et al.  Automated segmentation of the optic nerve head for diagnosis of glaucoma , 2005, Medical Image Anal..

[18]  Lili Xu,et al.  A novel method for blood vessel detection from retinal images , 2010, Biomedical engineering online.

[19]  Di Wu,et al.  On the adaptive detection of blood vessels in retinal images , 2006, IEEE Transactions on Biomedical Engineering.

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

[21]  Dogan Aydin,et al.  Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm , 2009, Comput. Methods Programs Biomed..

[22]  Elisa Ricci,et al.  Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification , 2007, IEEE Transactions on Medical Imaging.

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

[24]  C. Sinthanayothin,et al.  Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images , 1999, The British journal of ophthalmology.

[25]  Marie-Pierre Jolly,et al.  Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[26]  Vladimir Kolmogorov,et al.  What metrics can be approximated by geo-cuts, or global optimization of length/area and flux , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[27]  Elisa Ricci,et al.  Cellular Neural Networks With Virtual Template Expansion for Retinal Vessel Segmentation , 2007, IEEE Transactions on Circuits and Systems II: Express Briefs.

[28]  Anil A. Bharath,et al.  Segmentation of blood vessels from red-free and fluorescein retinal images , 2007, Medical Image Anal..

[29]  Xiaohui Liu,et al.  Retinal blood vessel segmentation via graph cut , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[30]  Chia-Ling Tsai,et al.  Automated Model-Based Segmentation, Tracing, and Analysis of Retinal Vasculature from Digital Fundus Images , 2003 .

[31]  Vladimir Kolmogorov,et al.  Graph cut based image segmentation with connectivity priors , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[32]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.

[33]  Yongmin Li,et al.  MRF Reconstruction of Retinal Images for the Optic Disc Segmentation , 2012, HIS.

[34]  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.

[35]  Marie-Pierre Jolly,et al.  Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images , 2001, ICCV.

[36]  Dimitris Samaras,et al.  Topology cuts: A novel min-cut/max-flow algorithm for topology preserving segmentation in N-D images , 2008, Comput. Vis. Image Underst..

[37]  Ana Maria Mendonça,et al.  Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction , 2006, IEEE Transactions on Medical Imaging.

[38]  Xiaoyi Jiang,et al.  Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[39]  Jacob Scharcanski,et al.  Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach , 2010, Comput. Biol. Medicine.