Paper
12 March 2010 Automatic optic disc segmentation based on image brightness and contrast
Shijian Lu, Jiang Liu, Joo Hwee Lim, Zhuo Zhang, Ngan Meng Tan, Wing Kee Wong, Huiqi Li, Tien Yin Wong
Author Affiliations +
Abstract
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.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shijian Lu, Jiang Liu, Joo Hwee Lim, Zhuo Zhang, Ngan Meng Tan, Wing Kee Wong, Huiqi Li, and Tien Yin Wong "Automatic optic disc segmentation based on image brightness and contrast", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76234J (12 March 2010); https://doi.org/10.1117/12.844654
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Cited by 3 scholarly publications.
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KEYWORDS
Image segmentation

Blood vessels

Image filtering

Optic nerve

Optical filters

Beryllium

Cameras

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