Diabetic retinopathy is one of the primary causes of vision loss worldwide. Early detection of the condition is critical for providing adequate treatment of this ailment to prevent vision loss. This detection is achieved by processing retinal fundus images. A key step in detecting diabetic retinopathy is identifying the optic disc in these images. The optic disc is similar in color and contrast to the exudates that indicate diabetic retinopathy. Hence, the optic disc has to be removed from the fundus image before exudates can be detected. Detecting the optic disc is also required in algorithms used for blood vessel segmentation in fundus images. Therefore, there is a need for approaches that accurately and quickly detect optic disc. This paper proposes a simple, deterministic, and time-efficient approach for optic disc detection by adapting an edge detection algorithm inspired by the gravitational law. Our method introduces novel pre- and post-detection steps that aim to increase the accuracy of the adapted detection method. In addition, a candidate selection technique is proposed to decrease the number of missed optic discs. The proposed methodology was found to have a detection rate of 100, 97.75, 92.90, and 95 % for DRIVE, DiaRet, DMED, and STARE datasets, respectively, which is comparatively better than existing optic disc detection schemes. Experimental results showed an average running time of 0.40 s per image, which is significantly lower than available methods published in the literature.

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In steganography, the cover medium is widely treated as a mere container for the embedded information, even though it affects the stego-image quality, security, and robustness. In addition, there is no consensus on the characteristics of a suitable cover image. In this work, we introduce and practically prove the most suitable cover image (MSCI) framework to automatically select a cover image for a given secret image. This paper proposes choosing the most suitable cover from a set of images based on two steps. First, a set of cover images is filtered based on relative entropy and a histogram in order to identify the most suitable candidates. Second, the local block pixel intensity features of the candidates are analyzed to select the most suitable cover image. Furthermore, cover image local blocks were optimized, using rotation and flipping, during the embedding process to further improve stego-image representation. The proposed framework demonstrated high visual image quality when compared with existing solutions. Steganalysis tests indicated that the proposed solution for cover selection provided an increased resistance to modern steganalyzers with up to 30% lowered detection rate, which improved security.