Automated extraction of retinal vasculature.

PURPOSE The authors propose an algorithm that automatically extracts retinal vasculature and provides a simple measure to correct the extraction. The output of the method is a network of salient points, and blood vessels are drawn by connecting the salient points using a centripetal parameterized Catmull-Rom spline. METHODS The algorithm starts by background correction. The corrected image is filtered with a bank of Gabor kernels, and the responses are consolidated to form a maximal image. After that, the maximal image is thinned to get a network of 1-pixel lines, analyzed and pruned to locate forks and form branches. Finally, the Ramer-Douglas-Peucker algorithm is used to determine salient points. When extraction is not satisfactory, the user simply shifts the salient points to edit the segmentation. RESULTS On average, the authors' extractions cover 93% of ground truths (on the Drive database). CONCLUSIONS By expressing retinal vasculature as a series of connected points, the proposed algorithm not only provides a means to edit segmentation but also gives knowledge of the shape of the blood vessels and their connections.

[1]  T. Teng,et al.  Progress towards automated diabetic ocular screening: A review of image analysis and intelligent systems for diabetic retinopathy , 2006, Medical and Biological Engineering and Computing.

[2]  Urs Ramer,et al.  An iterative procedure for the polygonal approximation of plane curves , 1972, Comput. Graph. Image Process..

[3]  Cem Yuksel,et al.  Parameterization and applications of Catmull-Rom curves , 2011, Comput. Aided Des..

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

[5]  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.

[6]  Andrew Hunter,et al.  An Active Contour Model for Segmenting and Measuring Retinal Vessels , 2009, IEEE Transactions on Medical Imaging.

[7]  Roberto Marcondes Cesar Junior,et al.  Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification , 2005, ArXiv.

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

[9]  Bunyarit Uyyanonvara,et al.  An approach to localize the retinal blood vessels using bit planes and centerline detection , 2012, Comput. Methods Programs Biomed..

[10]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

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

[12]  U. Rajendra Acharya,et al.  Active spline model: A shape based model - interactive segmentation , 2014, Digit. Signal Process..

[13]  Bunyarit Uyyanonvara,et al.  Blood vessel segmentation methodologies in retinal images - A survey , 2012, Comput. Methods Programs Biomed..

[14]  J S Wolffsohn,et al.  Improving the description of the retinal vasculature and patient history taking for monitoring systemic hypertension , 2001, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.

[15]  Yoshihiko Hamamoto,et al.  A gabor filter-based method for recognizing handwritten numerals , 1998, Pattern Recognit..

[16]  Nilanjan Ray,et al.  Image thresholding by variational minimax optimization , 2009, Pattern Recognit..

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

[19]  Cevat Ikibas,et al.  A personal identification system using retinal vasculature in retinal fundus images , 2011, Expert Syst. Appl..

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

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

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