Automatic optic disc segmentation based on image brightness and contrast

Untreated glaucoma leads to permanent damage of the optic nerve and resultant visual field loss, which can progress to blindness. As glaucoma often produces additional pathological cupping of the optic disc (OD), cupdisc- ratio is one measure that is widely used for glaucoma diagnosis. This paper presents an OD localization method that automatically segments the OD and so can be applied for the cup-disc-ratio based glaucoma diagnosis. The proposed OD segmentation method is based on the observations that the OD is normally much brighter and at the same time have a smoother texture characteristics compared with other regions within retinal images. Given a retinal image we first capture the ODs smooth texture characteristic by a contrast image that is constructed based on the local maximum and minimum pixel lightness within a small neighborhood window. The centre of the OD can then be determined according to the density of the candidate OD pixels that are detected by retinal image pixels of the lowest contrast. After that, an OD region is approximately determined by a pair of morphological operations and the OD boundary is finally determined by an ellipse that is fitted by the convex hull of the detected OD region. Experiments over 71 retinal images of different qualities show that the OD region overlapping reaches up to 90.37% according to the OD boundary ellipses determined by our proposed method and the one manually plotted by an ophthalmologist.

[1]  S. Resnikoff,et al.  Global data on visual impairment in the year 2002. , 2004, Bulletin of the World Health Organization.

[2]  Michael H. Goldbaum,et al.  Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels , 2003, IEEE Transactions on Medical Imaging.

[3]  Pascale Massin,et al.  A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina , 2002, IEEE Transactions on Medical Imaging.

[4]  Haizhou Li,et al.  ARGALI: An Automatic Cup-to-Disc Ratio Measurement System for Glaucoma Analysis Using Level-set Image Processing , 2009 .

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

[6]  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.

[7]  C. Sinthanayothin,et al.  Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images , 1999, The British journal of ophthalmology.

[8]  Kenneth W. Tobin,et al.  Detection of Anatomic Structures in Human Retinal Imagery , 2007, IEEE Transactions on Medical Imaging.

[9]  Aliaa A. A. Youssif,et al.  Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter , 2008, IEEE Transactions on Medical Imaging.

[10]  Andrew W. Fitzgibbon,et al.  Direct Least Square Fitting of Ellipses , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  T. Wong,et al.  Rationale and Methodology for a Population-Based Study of Eye Diseases in Malay People: The Singapore Malay Eye Study (SiMES) , 2007, Ophthalmic epidemiology.

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

[13]  Huiqi Li,et al.  Automatic location of optic disk in retinal images , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

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

[15]  Asoke K. Nandi,et al.  Automated localisation of retinal optic disk using Hough transform , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[16]  Bram van Ginneken,et al.  Segmentation of the Optic Disc, Macula and Vascular Arch in Fundus Photographs , 2007, IEEE Transactions on Medical Imaging.

[17]  Thomas Walter,et al.  Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques , 2001, ISMDA.