Gliomas are among the most common types of brain tumors found in adults, originating from glial cells and infiltrating surrounding brain tissues. Accurate identification and segmentation of these tumors are crucial for diagnosis, treatment planning, and patient monitoring. Despite significant advancements in medical imaging and computational analysis, glioma detection remains challenging due to the high variability in tumor shape, size, and location across different patients. Conventional segmentation methods, particularly level set approaches, often require manual intervention, limiting their efficiency and reproducibility in clinical settings. In this study, we propose a fully automated glioma segmentation method based on a modified level set framework. Unlike traditional semi-automatic level set techniques, our approach eliminates the need for manual initialization, thereby improving consistency and reducing operator dependency. The proposed method enhances boundary detection and region refinement, leading to more accurate segmentation results. To evaluate the effectiveness of our approach, we conducted extensive experiments using the standard BraTS 2017 dataset. Performance was assessed through both quantitative and qualitative evaluation metrics, including the Dice similarity coefficient. Our method achieved an average Dice coefficient of 79% for the entire tumor, demonstrating its reliability and effectiveness compared to conventional techniques. The fully automated nature of this approach offers promising potential for integration into clinical workflows, aiding radiologists and medical professionals in the early detection and precise delineation of gliomas.