Improving retinal artery and vein classification by means of a minimal path approach

This paper describes a technique for the retinal vessel classification into artery and vein categories from fundus images within a framework to compute the arteriovenous ratio. This measure is used to assess the patient condition, mainly in hypertension and it is computed as the ratio between artery and vein widths. To this end, the vessels are segmented and measured in several circumferences concentric to the optic nerve. The resulting vessel segments at each radius are classified as artery or vein independently. After that, a tracking procedure joins vessel segments in different radii that belong to the same vessel. Finally, a voting system is applied to obtain the final class of the whole vessel. The methodology has been tested in a data set of 100 images labeled manually by two medical experts and a classification rate of over 87.68 % has been obtained.

[1]  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).

[2]  Laurent D. Cohen,et al.  Global Minimum for Active Contour Models: A Minimal Path Approach , 1997, International Journal of Computer Vision.

[3]  Joan Serrat,et al.  Multilocal Creaseness Based on the Level-Set Extrinsic Curvature , 2000, Comput. Vis. Image Underst..

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

[5]  Heinrich Niemann,et al.  Automated Calculation of Retinal Arteriovenous Ratio for Detection and Monitoring of Cerebrovascular Disease Based on Assessment of Morphological Changes of Retinal Vascular System , 2002, MVA.

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

[7]  Manuel G. Penedo,et al.  On the Automatic Computation of the Arterio-Venous Ratio in Retinal Images: Using Minimal Paths for the Artery/Vein Classification , 2010, 2010 International Conference on Digital Image Computing: Techniques and Applications.

[8]  Manuel G. Penedo,et al.  Improvements in retinal vessel clustering techniques: towards the automatic computation of the arterio venous ratio , 2010, Computing.

[9]  Jorge Novo,et al.  Sirius: A web-based system for retinal image analysis , 2010, Int. J. Medical Informatics.

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

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

[12]  J A Sethian,et al.  A fast marching level set method for monotonically advancing fronts. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[13]  B. Girard,et al.  Retinal microvascular abnormalities and incident stroke : the atherosclerosis risk in communities study. , 2008 .

[14]  Antonio Pose-Reino,et al.  Computerized measurement of retinal blood vessel calibre: description, validation and use to determine the influence of ageing and hypertension , 2005, Journal of hypertension.

[15]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

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

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

[18]  Paul Mitchell,et al.  The eye in hypertension , 2007, The Lancet.

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

[20]  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).

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

[22]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[23]  Ian Lewis,et al.  Proceedings of the SPIE , 2012 .

[24]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[25]  R. Klein,et al.  Retinal microvascular abnormalities and their relationship with hypertension, cardiovascular disease, and mortality. , 2001, Survey of ophthalmology.

[26]  Bram van Ginneken,et al.  Automatic classification of retinal vessels into arteries and veins , 2009, Medical Imaging.

[27]  Esther de Ves,et al.  Segmentation of macular fluorescein angiographies. A statistical approach , 2001, Pattern Recognit..

[28]  Til Aach,et al.  Vessel Segmentation in Angiograms using Hysteresis Thresholding , 2005, MVA.