Measurement of retinal vessel widths from fundus images based on 2-D modeling

Changes in retinal vessel diameter are an important sign of diseases such as hypertension, arteriosclerosis and diabetes mellitus. Obtaining precise measurements of vascular widths is a critical and demanding process in automated retinal image analysis as the typical vessel is only a few pixels wide. This paper presents an algorithm to measure the vessel diameter to subpixel accuracy. The diameter measurement is based on a two-dimensional difference of Gaussian model, which is optimized to fit a two-dimensional intensity vessel segment. The performance of the method is evaluated against Brinchmann-Hansen's half height, Gregson's rectangular profile and Zhou's Gaussian model. Results from 100 sample profiles show that the presented algorithm is over 30% more precise than the compared techniques and is accurate to a third of a pixel.

[1]  Gregory D. Hager,et al.  Probabilistic Data Association Methods for Tracking Complex Visual Objects , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

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

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

[4]  Andrew Hunter,et al.  Novelty Detection in Video Surveillance Using Hierarchical Neural Networks , 2002, ICANN.

[5]  R. Klein,et al.  Retinal microvascular abnormalities and incident stroke: the Atherosclerosis Risk in Communities Study , 2001, The Lancet.

[6]  J. C. Parr,et al.  Comparison of methods of measuring vessel widths on retinal photographs and the effect of fluorescein injection on apparent retinal vessel calibers. , 1969, American journal of ophthalmology.

[7]  E. Aurell,et al.  Signs in the fundus oculi and arterial hypertension: unconventional assessment and significance. , 1967, Bulletin of the World Health Organization.

[8]  N. Chapman,et al.  Peripheral vascular disease is associated with abnormal arteriolar diameter relationships at bifurcations in the human retina. , 2002, Clinical science.

[9]  Keith D. Baker,et al.  Visual surveillance using deformable models of vehicles , 1997, Robotics Auton. Syst..

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

[11]  Ramakant Nevatia,et al.  Event Detection and Analysis from Video Streams , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Nasser Kehtarnavaz,et al.  Real-Time Vision-based Detection of Waiting Pedestrians , 1997, Real Time Imaging.

[13]  Aleksej Makarov Comparison of background extraction based intrusion detection algorithms , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[14]  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.

[15]  Anthony D. Worrall,et al.  A statistically-based Newton method for pose refinement , 1998, Image Vis. Comput..

[16]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  E M Kohner,et al.  Accurate vessel width measurement from fundus photographs: a new concept. , 1994, The British journal of ophthalmology.

[18]  R. Klein,et al.  Retinal arteriolar diameters and elevated blood pressure: the Atherosclerosis Risk in Communities Study. , 1999, American journal of epidemiology.

[19]  T Behrendt,et al.  Scanning densitometer for photographic fundus measurements. , 1966, American journal of ophthalmology.

[20]  Fabio Roli,et al.  Learning and Classification of Suspicious Events for Advanced Visual-Based Surveillance , 2000 .

[21]  Olaf Brinchmann‐Hansen,et al.  The apparent and true width of the blood column in retinal vessels , 1986 .

[22]  Gian Luca Foresti,et al.  Multimedia Video-Based Surveillance Systems: Requirements, Issues and Solutions , 2000 .

[23]  Brett D. Thackray,et al.  Semi-automatic segmentation of vascular network images using a rotating structuring element (ROSE) with mathematical morphology and dual feature thresholding , 1993, IEEE Trans. Medical Imaging.

[24]  Olaf Brinchmann-Hansen,et al.  Theoretical relations between light streak characteristics and optical properties of retinal vessels , 1986 .

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

[26]  Xiaohong W. Gao,et al.  Quantification and characterisation of arteries in retinal images , 2000, Comput. Methods Programs Biomed..

[27]  Fundus photographic risk factors for progression of diabetic retinopathy. ETDRS report number 12. Early Treatment Diabetic Retinopathy Study Research Group. , 1991, Ophthalmology.

[28]  R. Stephenson A and V , 1962, The British journal of ophthalmology.

[29]  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.

[30]  Gian Luca Foresti A real-time system for video surveillance of unattended outdoor environments , 1998, IEEE Trans. Circuits Syst. Video Technol..

[31]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[32]  A. Bharath,et al.  Computer algorithms for the automated measurement of retinal arteriolar diameters , 2001, The British journal of ophthalmology.

[33]  P. Gregson,et al.  Automated grading of venous beading. , 1995, Computers and biomedical research, an international journal.

[34]  C. Zheng,et al.  ; 0 ; , 1951 .

[35]  Andrew Hunter,et al.  Application of the self-organising map to trajectory classification , 2000, Proceedings Third IEEE International Workshop on Visual Surveillance.

[36]  Tieniu Tan,et al.  An Integrated Traffic and Pedestrian Model-Based Vision System , 1997, BMVC.

[37]  Oddbjørn Engvold,et al.  Microphotometry of the blood column and the light streak on retinal vessels in fundus photographs , 1986 .

[38]  Bjarne K. Ersbøll,et al.  Quantitative measurement of changes in retinal vessel diameter in ocular fundus images , 2000, Pattern Recognit. Lett..

[39]  Sergio A. Velastin,et al.  Image Processing System for Pedestrian Monitoring Using Neural Classification of Normal Motion Patterns , 1999 .