Retinal image blood vessel segmentation

The appearance and structure of blood vessels in retinal images play an important role in diagnosis of eye diseases. This paper proposes a method for segmentation of blood vessels in color retinal images. We present a method that uses 2-D Gabor wavelet to enhance the vascular pattern. We locate and segment the blood vessels using adaptive thresholding. The technique is tested on publicly available DRIVE database of manually labeled images which has been established to facilitate comparative studies on segmentation of blood vessels in retinal images. The proposed method achieves an area under the receiver operating characteristic curve of 0.963 on DRIVE database.

[1]  Kaleem Siddiqi,et al.  Flux Maximizing Geometric Flows , 2001, ICCV.

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

[3]  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).

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

[5]  Chia-Ling Tsai,et al.  Model-based method for improving the accuracy and repeatability of estimating vascular bifurcations and crossovers from retinal fundus images , 2004, IEEE Transactions on Information Technology in Biomedicine.

[6]  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.

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

[8]  R. Davis,et al.  Telemedicine improves eye examination rates in individuals with diabetes: a model for eye-care delivery in underserved communities. , 2003, Diabetes care.

[9]  A. Arneodo,et al.  A wavelet-based method for multifractal image analysis. I. Methodology and test applications on isotropic and anisotropic random rough surfaces , 2000 .

[10]  Abdhish R Bhavsar Diabetic retinopathy. The diabetes eye exam initiative. , 2002, Minnesota medicine.

[11]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

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

[13]  Jerry Cavallerano,et al.  Emerging trends in ocular telemedicine: the diabetic retinopathy model , 2005, Journal of telemedicine and telecare.

[14]  S. Sinclair,et al.  The internist's role in managing diabetic retinopathy: screening for early detection. , 2004, Cleveland Clinic journal of medicine.

[15]  Anthony J. Yezzi,et al.  Vessel Segmentation Using a Shape Driven Flow , 2004, MICCAI.

[16]  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.

[17]  Jean-Pierre Antoine,et al.  Image analysis with two-dimensional continuous wavelet transform , 1993, Signal Process..

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

[19]  Anil A. Bharath,et al.  Retinal Blood Vessel Segmentation by Means of Scale-Space Analysis and Region Growing , 1999, MICCAI.

[20]  Marc Lalondey,et al.  Non-recursive paired tracking for vessel extraction from retinal images , 2000 .

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

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

[23]  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.

[24]  Jordi Vitrià,et al.  Tracking elongated structures using statistical snakes , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[25]  N. Younis,et al.  Prevalence of diabetic eye disease in patients entering a systematic primary care‐based eye screening programme , 2002, Diabetic medicine : a journal of the British Diabetic Association.

[26]  S. L. Eddins,et al.  Digital Image Processing Using MATLAB: AND Mathworks, MATLAB Sim SV 07 , 2007 .

[27]  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.

[28]  Keith A. Soper,et al.  Diagnosis of diabetic eye disease. , 1982, JAMA.

[29]  A. Pinz,et al.  Mapping the human retina , 1996, IEEE Transactions on Medical Imaging.

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

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