Automatic Detection of Left and Right Eye in Retinal Fundus Images

Retinal fundus images are commonly used by ophthalmologists to diagnose and monitor the development of eye diseases. One of the key information in the fundus images is to distinguish between the left and right eye. This work is motivated to provide an accurate and automated method to detect the left and right eye based on the retinal images. In current clinical practices, a common method to differentiate left and right eye in the retinal images is via the position of the optic nerve head with respect to the macula. This paper introduces a novel method to automatically detect the orientation of the retina in fundus images from the position of the central retinal artery and veins of the physiologic optic nerve head. Our method first locates the centre of the optic cup using pixel intensity. A 100 x 100 pixel ROI is then formed based on this centriod. Thresholding by pixel intensity and morphology is then used to segment the optic disc from this ROI to obtain an accurate optic disc and its blood vessels within. The pixel intensity in the green channel of this segmented disc along its horizontal axis is projected. The sum of the pixel intensity in the left half of the disc is then compared to the right half. The prevalence of higher green pixel intensity in either half of the optic disc would then indicate if this is a nasal or temporal sector, which would then indicate if this is a left or right eye. This method is tested on 194 images collected from patients at the Singapore Eye Research Institute and achieves an accuracy of 92.23%. This method is novel and its results are encouraging to provide an accurate and automatic system to make a distinction between left and right eye from retinal images.

Duke Scholars

Author Tien Yin Wong Ophthalmology