Fully automatized parallel segmentation of the optic disc in retinal fundus images

Abstract This paper presents a fully automatic parallel software for the localization of the optic disc (OD) in retinal fundus color images. A new method has been implemented with the Graphics Processing Units (GPU) technology. Image edges are extracted using a new operator, called AGP-color segmentator. The resulting image is binarized with Hamadani’s technique and, finally, a new algorithm called Hough circle cloud is applied for the detection of the OD. The reliability of the tool has been tested with 129 images from the public databases DRIVE and DIARETDB1 obtaining an average accuracy of 99.6% and a mean consumed time per image of 7.6 and 16.3 s respectively. A comparison with several state-of-the-art algorithms shows that our algorithm represents a significant improvement in terms of accuracy and efficiency.

[1]  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.

[2]  Marios S. Pattichis,et al.  Multiscale AM-FM decompositions with GPU acceleration for diabetic retinopathy screening , 2012, 2012 IEEE Southwest Symposium on Image Analysis and Interpretation.

[3]  Hideki Kuga,et al.  A computer method of understanding ocular fundus images , 1982, Pattern Recognit..

[4]  J. Liu,et al.  Optic cup and disk extraction from retinal fundus images for determination of cup-to-disc ratio , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

[5]  P. C. Siddalingaswamy,et al.  Automatic grading of diabetic maculopathy severity levels , 2010, 2010 International Conference on Systems in Medicine and Biology.

[6]  Rangaraj M. Rangayyan,et al.  Detection of the Optic Nerve Head in Fundus Images of the Retina Using the Hough Transform for Circles , 2010, Journal of Digital Imaging.

[7]  N.M. Tan,et al.  Intelligent fusion of cup-to-disc ratio determination methods for glaucoma detection in ARGALI , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  J. Liu,et al.  Automatic glaucoma diagnosis from fundus image , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Charles V. Stewart,et al.  Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy , 2006, IEEE Transactions on Biomedical Engineering.

[10]  Mishra Madhusudhan,et al.  Image Processing Techniques for Glaucoma Detection , 2011, ACC.

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

[12]  T. Chaichana,et al.  Edge Detection of the Optic Disc in Retinal Images Based on Identification of a Round Shape , 2008, 2008 International Symposium on Communications and Information Technologies.

[13]  András Hajdu,et al.  Automatic detection of the optic disc using majority voting in a collection of optic disc detectors , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[14]  Shankar M. Krishnan,et al.  Automatic image analysis of fundus photograph , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[15]  J. Olson,et al.  Automatic detection of retinal anatomy to assist diabetic retinopathy screening , 2007, Physics in medicine and biology.

[16]  Jayanthi Sivaswamy,et al.  Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment , 2011, IEEE Transactions on Medical Imaging.

[17]  Andrés G. Marrugo,et al.  Retinal image analysis: preprocessing and feature extraction , 2011 .

[18]  B. Thomas,et al.  Automated identification of diabetic retinal exudates in digital colour images , 2003, The British journal of ophthalmology.

[19]  Daniel Díaz-Pernil,et al.  Segmenting images with gradient-based edge detection using Membrane Computing , 2013, Pattern Recognit. Lett..

[20]  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.

[21]  Daniel Díaz-Pernil,et al.  Parallel Skeletonizing of Digital Images by Using Cellular Automata , 2012, CTIC.

[22]  J. Liu,et al.  Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Young H. Kwon,et al.  Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features. , 2007, Investigative ophthalmology & visual science.

[24]  D. Kavitha,et al.  Automatic detection of optic disc and exudates in retinal images , 2005, Proceedings of 2005 International Conference on Intelligent Sensing and Information Processing, 2005..

[25]  Huiqi Li,et al.  Boundary detection of optic disk by a modified ASM method , 2003, Pattern Recognit..

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

[27]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[28]  Tun-Wen Pai,et al.  An Atomatic Fundus Image Analysis System for Clinical Diagnosis of Glaucoma , 2011, 2011 International Conference on Complex, Intelligent, and Software Intensive Systems.

[29]  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.

[30]  Haizhou Li,et al.  ARGALI: An Automatic Cup-to-Disc Ratio Measurement System for Glaucoma Analysis Using Level-set Image Processing , 2009 .

[31]  Bram van Ginneken,et al.  Fast detection of the optic disc and fovea in color fundus photographs , 2009, Medical Image Anal..

[32]  Hamed Sadjedi,et al.  A new method for automatic detection and diagnosis of retinopathy diseases in colour fundus images based on Morphology , 2010, 2010 International Conference on Bioinformatics and Biomedical Technology.

[33]  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.

[34]  S. Moulik,et al.  The role of GPU computing in medical image analysis and visualization , 2011, Medical Imaging.

[35]  Langis Gagnon,et al.  Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching , 2001, IEEE Transactions on Medical Imaging.

[36]  Joseph M. Reinhardt,et al.  Simultaneous automatic detection of optic disc and fovea on fundus photographs , 2011, Medical Imaging.

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

[38]  Asoke K. Nandi,et al.  Automated localisation of retinal optic disk using Hough transform , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[39]  V. Saravanan,et al.  Graphical user interface for enhanced retinal image analysis for diagnosing diabetic retinopathy , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

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

[41]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[42]  Daniel Díaz-Pernil,et al.  A Parallel Implementation of the Thresholding Problem by Using Tissue-Like P Systems , 2011, CAIP.

[43]  P.V.C. Hough,et al.  Machine Analysis of Bubble Chamber Pictures , 1959 .

[44]  Mira Park,et al.  Locating the Optic Disc in Retinal Images , 2006, International Conference on Computer Graphics, Imaging and Visualisation (CGIV'06).

[45]  M. Usman Akram,et al.  Automated system for macula detection in digital retinal images , 2011, 2011 International Conference on Information and Communication Technologies.

[46]  Naser A Hamadani Automatic target cueing in IR imagery , 1981 .