Diabetic Retinopathy (DR) has become nowadays a considerable world-wide threat due to increased growth of blind people at early ages. From the engineering viewpoint, the detection of DR pathologies (microaneurysms, hemorrhages and exudates) through computer vision techniques is of prime importance in medical assistance. Such methodologies outperform traditional screening of retinal color fundus images. Moreover, the identification of landmark features as the optic disk (OD), fovea and retinal vessels is a key pre-processing step to detect the aforementioned potential pathologies. In the same vein, this paper works with the well-known Convexity Shape Prior algorithm to segment the main anatomical structure of the retina, the OD. At first, some pre-processing techniques such as the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) are applied to enhance the image contrast and eliminate the artifacts. Subsequently, several morphological operations are performed to improve the post-segmentation of the OD. Finally, blood vessels are extracted through a novel fusion of the average, median, Gaussian and Gabor wavelet filters.