A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features

This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.

[1]  J. Babel,et al.  La rétinopathie diabétique (Part 1 of 3) , 1958 .

[2]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

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

[4]  R. Wootton,et al.  Measurement of capillary dropout in retinal angiograms by computerised image analysis , 1992, Pattern Recognit. Lett..

[5]  Ying Sun,et al.  Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme , 1993, IEEE Trans. Medical Imaging.

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

[7]  R. Klein,et al.  Retinal microaneurysm counts and 10-year progression of diabetic retinopathy. , 1995, Archives of ophthalmology.

[8]  P F Sharp,et al.  An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. , 1996, Computers and biomedical research, an international journal.

[9]  T. Williamson,et al.  Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. , 1996, The British journal of ophthalmology.

[10]  O. Chutatape,et al.  Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[11]  M. Cree,et al.  A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms , 1998, Comput. Biol. Medicine.

[12]  B. Roysam,et al.  Image processing algorithms for retinal montage synthesis, mapping, and real-time location determination , 1998, IEEE Transactions on Biomedical Engineering.

[13]  Yannis A. Tolias,et al.  A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering , 1998, IEEE Transactions on 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]  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.

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

[17]  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.

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

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

[20]  Hong Shen,et al.  Optimal scheduling of tracing computations for real-time vascular landmark extraction from retinal fundus images , 2001, IEEE Transactions on Information Technology in Biomedicine.

[21]  A. Ruggeri,et al.  Quantitative description of vessel features in hypertensive retinopathy fundus images , 2001 .

[22]  Xiaohong W. Gao,et al.  A method of vessel tracking for vessel diameter measurement on retinal images , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[23]  H. Taylor,et al.  World blindness: a 21st century perspective , 2001, The British journal of ophthalmology.

[24]  Langis Gagnon,et al.  Procedure to detect anatomical structures in optical fundus images , 2001, SPIE Medical Imaging.

[25]  H. Taylor,et al.  Costs of mobile screening for diabetic retinopathy: a practical framework for rural populations. , 2001, The Australian journal of rural health.

[26]  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.

[27]  T. Sano,et al.  [Diabetic retinopathy]. , 2001, Nihon rinsho. Japanese journal of clinical medicine.

[28]  Shankar M. Krishnan,et al.  Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter , 2002, IEEE Transactions on Biomedical Engineering.

[29]  Anil A. Bharath,et al.  Retinal vascular tree morphology: a semi-automatic quantification , 2002, IEEE Transactions on Biomedical Engineering.

[30]  John Flynn,et al.  Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis , 2002, Medical Image Anal..

[31]  Alfredo Ruggeri,et al.  A divide et impera strategy for automatic classification of retinal vessels into arteries and veins , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[32]  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..

[33]  M. Larsen,et al.  Automated detection of fundus photographic red lesions in diabetic retinopathy. , 2003, Investigative ophthalmology & visual science.

[34]  Michael J. Cree,et al.  Microaneurysm Detection in Colour Fundus Images , 2003 .

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

[36]  Hiroshi Fujita,et al.  Automated analysis of the distributions and geometries of blood vessels on retinal fundus images , 2004, SPIE Medical Imaging.

[37]  Huiqi Li,et al.  Automated feature extraction in color retinal images by a model based approach , 2004, IEEE Transactions on Biomedical Engineering.

[38]  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.

[39]  Andrew Hunter,et al.  Measurement of retinal vessel widths from fundus images based on 2-D modeling , 2004, IEEE Transactions on Medical Imaging.

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

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

[42]  S. Wild,et al.  Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. , 2004, Diabetes care.

[43]  Bram van Ginneken,et al.  Automatic detection of red lesions in digital color fundus photographs , 2005, IEEE Transactions on Medical Imaging.

[44]  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.

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

[46]  Martha Elizabeth Shenton,et al.  On evaluating brain tissue classifiers without a ground truth , 2007, NeuroImage.

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

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

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

[50]  Mohammed Al-Rawi,et al.  Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images , 2007, Comput. Methods Programs Biomed..

[51]  Manuel G. Penedo,et al.  A Snake for Retinal Vessel Segmentation , 2007, IbPRIA.

[52]  Pascale Massin,et al.  Automatic detection of microaneurysms in color fundus images , 2007, Medical Image Anal..

[53]  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.

[54]  J. Leahy.  Economic Costs of Diabetes in the U.S. in 2007 , 2008 .

[55]  Hong Yan,et al.  A Novel Vessel Segmentation Algorithm for Pathological Retina Images Based on the Divergence of Vector Fields , 2008, IEEE Transactions on Medical Imaging.

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

[57]  Qin Li,et al.  Detection of microaneurysms using multi-scale correlation coefficients , 2010, Pattern Recognit..