Computerized Medical Imaging and Graphics Automated Characterization of Blood Vessels as Arteries and Veins in Retinal Images

In recent years researchers have found that alternations in arterial or venular tree of the retinal vasculature are associated with several public health problems such as diabetic retinopathy which is also the leading cause of blindness in the world. A prerequisite for automated assessment of subtle changes in arteries and veins, is to accurately separate those vessels from each other. This is a difficult task due to high similarity between arteries and veins in addition to variation of color and non-uniform illumination inter and intra retinal images. In this paper a novel structural and automated method is presented for artery/vein classification of blood vessels in retinal images. The proposed method consists of three main steps. In the first step, several image enhancement techniques are employed to improve the images. Then a specific feature extraction process is applied to separate major arteries from veins. Indeed, vessels are divided to smaller segments and feature extraction and vessel classification are applied to each small vessel segment instead of each vessel point. Finally, a post processing step is added to improve the results obtained from the previous step using structural characteristics of the retinal vascular network. In the last stage, vessel features at intersection and bifurcation points are processed for detection of arterial and venular sub trees. Ultimately vessel labels are revised by publishing the dominant label through each identified connected tree of arteries or veins. Evaluation of the proposed approach against two different datasets of retinal images including DRIVE database demonstrates the good performance and robustness of the method. The proposed method may be used for determination of arteriolar to venular diameter ratio in retinal images. Also the proposed method potentially allows for further investigation of labels of thinner arteries and veins which might be found by tracing them back to the major vessels.

[1]  Arturo Espinosa-Romero,et al.  Graph-Based Methods for Retinal Mosaicing and Vascular Characterization , 2007, GbRPR.

[2]  F. Tajeripour,et al.  Investigating image enhancement methods for better classification of retinal blood vessels into arteries and veins , 2012, The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012).

[3]  R. Klein,et al.  Revised formulas for summarizing retinal vessel diameters , 2003, Current eye research.

[4]  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.

[5]  Xiaoyi Jiang,et al.  Separation of the retinal vascular graph in arteries and veins based upon structural knowledge , 2009, Image Vis. Comput..

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

[7]  Peter Rockett An improved rotation-invariant thinning algorithm , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Aliaa A. A. Youssif,et al.  A comparative evaluation of preprocessing methods for automatic detection of retinal anatomy , 2007 .

[9]  Nico Karssemeijer,et al.  Medical Imaging 2009: Computer-aided Diagnosis , 2009 .

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

[11]  A. Hofman,et al.  Are retinal arteriolar or venular diameters associated with markers for cardiovascular disorders? The Rotterdam Study. , 2004, Investigative ophthalmology & visual science.

[12]  Hiroshi Fujita,et al.  Automated selection of major arteries and veins for measurement of arteriolar-to-venular diameter ratio on retinal fundus images , 2011, Comput. Medical Imaging Graph..

[13]  T. Wong,et al.  Retinal Vascular Changes in Pre-Diabetes and Prehypertension , 2007, Diabetes Care.

[14]  S. G. Vázquez,et al.  Using Retinex Image Enhancement to Improve the Artery/Vein Classification in Retinal Images , 2010, ICIAR.

[15]  Daniel Kondermann,et al.  Blood vessel classification into arteries and veins in retinal images , 2007, SPIE Medical Imaging.

[16]  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.

[17]  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).

[18]  Herbert F. Jelinek,et al.  Towards vessel characterisation in the vicinity of the optic disc in digital retinal images , 2005 .

[19]  Evangelos Dermatas,et al.  Multi-scale retinal vessel segmentation using line tracking , 2010, Comput. Medical Imaging Graph..

[20]  Reza Pourreza,et al.  Segmentation of blood vessels in fundus color images by Radon transform and morphological reconstruction , 2010, Third International Workshop on Advanced Computational Intelligence.

[21]  R. Klein,et al.  Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study. , 1999, Ophthalmology.

[22]  A. Ruggeri,et al.  An improved system for the automatic estimation of the Arteriolar-to-Venular diameter Ratio (AVR) in retinal images , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Rabab Kreidieh Ward,et al.  A Rotation Invariant Rule-Based Thinning Algorithm for Character Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Zia-ur Rahman,et al.  A multiscale retinex for bridging the gap between color images and the human observation of scenes , 1997, IEEE Trans. Image Process..

[25]  T. Wong,et al.  Retinal vascular caliber: systemic, environmental, and genetic associations. , 2009, Survey of ophthalmology.

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

[27]  Manuel G. Penedo,et al.  Development of an automated system to classify retinal vessels into arteries and veins , 2012, Comput. Methods Programs Biomed..

[28]  Bram van Ginneken,et al.  Automated Measurement of the Arteriolar-to-Venular Width Ratio in Digital Color Fundus Photographs , 2011, IEEE Transactions on Medical Imaging.

[29]  Mong-Li Lee,et al.  A piecewise Gaussian model for profiling and differentiating retinal vessels , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[30]  Enrico Grisan,et al.  Luminosity and contrast normalization in retinal images , 2005, Medical Image Anal..

[31]  Yu-Kumg Chen,et al.  A Weighting Mean-Separated Sub-Histogram Equalization for Contrast Enhancement , 2010, 2010 International Conference on Biomedical Engineering and Computer Science.

[32]  Chun-Ming Tsai,et al.  Contrast enhancement by automatic and parameter-free piecewise linear transformation for color images , 2008, IEEE Transactions on Consumer Electronics.

[33]  Zia-ur Rahman,et al.  Properties and performance of a center/surround retinex , 1997, IEEE Trans. Image Process..