A novel method for optic disc detection in retinal images using the cuckoo search algorithm and structural similarity index

Abstract

Accurate and reliable optic disk (OD) localization is vital for eye disease monitoring and fundus image analysis. This paper describes a novel technique to localize the OD in retinal images using the cuckoo search algorithm and the structural similarity index measure (SSIM). SSIM uses the average OD value to compare with candidate OD. Hence, the average OD values were calculated from randomly selected images. The average OD values and the colored retina fundus images were given as input to the proposed algorithm. The adaptive histogram equalization method was applied to ensure that the brightness and contrast values in all images were within a similar range. Next, candidate OD centers were calculated using the search algorithm and the similarity value between each candidate OD and the average OD was determined. Finally, the computed similarity was maximized by the search algorithm and the true OD center was found. The performance of the OD detection algorithm was evaluated on three public datasets. The experimental results showed that proposed method achieved comparable performance, without employing complex image pre-processing, compared with the state-of-the-art techniques. Specifically, the accuracy of 100%, 100%, and 97.5% were obtained for ONHSD, DRIONS and DRIVE datasets, respectively.