Robust and accurate optic disk localization using vessel symmetry line measure in fundus images

Abstract Accurate optic disk (OD) localization is an important step in fundus image based computer-aided diagnosis of glaucoma and diabetic retinopathy. Robust OD localization becomes more challenging with the presence of common pathological variations which could alter its overall appearance. This paper presents a novel OD localization method by incorporating salient visual cues of retinal vasculature: (1) global vessel symmetry, (2) vessel component count and (3) local vessel symmetry inside OD region. In the proposed method, a new vessel symmetry line ( VSL ) measure is designed to demarcate the lines that divide the retinal vasculature into approximately similar halves. The initial OD center location is computed using the highest number of major blood vessel components in the skeleton image. The final OD center localization involves an iterative center of mass computation to exploit the local vessel symmetry in the OD region of interest. The proposed method shows effectiveness in diseased retinas having diverse symptoms like bright lesions, hemorrhages, and tortuous vessels that create potential ambiguity for OD localization. A total of ten publicly available retinal image databases are considered for extensive evaluation of the proposed method. The experimental results demonstrate high average OD detection accuracy of 99.49%, while achieving state-of-the-art OD localization error in all databases.

[1]  Qin Li,et al.  Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs , 2010, IEEE Transactions on Medical Imaging.

[2]  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.

[3]  Thomas Walter,et al.  Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques , 2001, ISMDA.

[4]  Mariano Rincón,et al.  Identification of the optic nerve head with genetic algorithms , 2008, Artif. Intell. Medicine.

[5]  Sreeparna Banerjee,et al.  Detection of hard exudates using mean shift and normalized cut method , 2016 .

[6]  Enrico Grisan,et al.  Detection of optic disc in retinal images by means of a geometrical model of vessel structure , 2004, IEEE Transactions on Medical Imaging.

[7]  Jayanthi Sivaswamy,et al.  Drishti-GS: Retinal image dataset for optic nerve head(ONH) segmentation , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[8]  Ron Shonkwiler,et al.  An Image Algorithm for Computing the Hausdorff Distance Efficiently in Linear Time , 1989, Inf. Process. Lett..

[9]  Manuel G. Penedo,et al.  Personal authentication using digital retinal images , 2006, Pattern Analysis and Applications.

[10]  Yuanyuan Zhao,et al.  Novel Accurate and Fast Optic Disc Detection in Retinal Images With Vessel Distribution and Directional Characteristics , 2016, IEEE Journal of Biomedical and Health Informatics.

[11]  Hiroshi Fujita,et al.  Automated segmentation of optic disc region on retinal fundus photographs: Comparison of contour modeling and pixel classification methods , 2011, Comput. Methods Programs Biomed..

[12]  N.B. Puhan,et al.  Iris recognition on edge maps , 2007, 2007 6th International Conference on Information, Communications & Signal Processing.

[13]  Shijian Lu,et al.  Automatic Optic Disc Detection From Retinal Images by a Line Operator , 2011, IEEE Transactions on Biomedical Engineering.

[14]  Shijian Lu,et al.  Accurate and Efficient Optic Disc Detection and Segmentation by a Circular Transformation , 2011, IEEE Transactions on Medical Imaging.

[15]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Diego Marin,et al.  Automated Optic Disc Detection in Retinal Images of Patients with Diabetic Retinopathy and Risk of Macular Edema , 2009 .

[17]  Elli Angelopoulou,et al.  Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database , 2013, IET Image Process..

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

[19]  Ganapati Panda,et al.  New Binary Hausdorff Symmetry measure based seeded region growing for retinal vessel segmentation , 2016 .

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

[21]  U. Rajendra Acharya,et al.  Computer-aided diagnosis of diabetic retinopathy: A review , 2013, Comput. Biol. Medicine.

[22]  R. Venkatesh Babu,et al.  Approximate Nearest Neighbour Field based Optic Disk Detection , 2014, Comput. Medical Imaging Graph..

[23]  Andrea Giachetti,et al.  The use of radial symmetry to localize retinal landmarks , 2013, Comput. Medical Imaging Graph..

[24]  Miguel Castelo-Branco,et al.  Optic Disc Localization in Retinal Images Based on Cumulative Sum Fields , 2016, IEEE Journal of Biomedical and Health Informatics.

[25]  Remco Duits,et al.  Template Matching via Densities on the Roto-Translation Group , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  R. GeethaRamani,et al.  Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis , 2016 .

[27]  Ahmed S. Fahmy,et al.  Fast Localization of the Optic Disc Using Projection of Image Features , 2010, IEEE Transactions on Image Processing.

[28]  Guy Cazuguel,et al.  TeleOphta: Machine learning and image processing methods for teleophthalmology , 2013 .

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

[30]  S. Edward Rajan,et al.  Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images , 2014 .

[31]  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.

[32]  Aliaa A. A. Youssif,et al.  Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter , 2008, IEEE Transactions on Medical Imaging.

[33]  N. B. Puhan,et al.  Global vessel symmetry for optic disc detection in retinal images , 2015, 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG).

[34]  Michael H. Goldbaum,et al.  Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels , 2003, IEEE Transactions on Medical Imaging.

[35]  Ana Maria Mendonça,et al.  Optic disc segmentation using the sliding band filter , 2015, Comput. Biol. Medicine.