This research paper addresses the critical task of optic disk segmentation from retinal fundus images, essential for accurate glaucoma detection. It introduces a novel methodology that integrates image preprocessing, validation, and segmentation to enhance the precision of optic disk delineation. The proposed approach consists of two phases: Image Validation and Image Segmentation. Image preprocessing includes grayscale conversion, Gaussian denoising, and contrast enhancement. The Image Validation phase employs histogram analysis to determine the necessity of segmentation. Optic disk segmentation is then performed using Simple Linear Iterative Clustering (SLIC) and Normalized Graph Cut algorithms. A comparative evaluation is conducted against image processing and deep learning approaches using standard datasets. Performance is assessed using the Jaccard index and Dice coefficient. The segmentation algorithms are evaluated on standard datasets comprising 1456 images from Dhrishti-GS1, RIM-ONE, and ACRIMA. Experimental results demonstrate the effectiveness of the proposed method in achieving accurate optic disk segmentation. The Jaccard index and Dice coefficient indicate that the proposed method closely matches the performance of the deep learning-based UNet model without resizing the original images, thereby avoiding information loss during segmentation.