Accurate cerebrovascular segmentation is crucial for the early diagnosis and treatment of stroke and aneurysm, both of which pose significant health risks. Time-of-Flight Magnetic Resonance Angiography is widely used for non-invasive vascular assessment, but manual segmentation remains time-consuming, labor-intensive, and highly dependent on the skill level of medical experts. To address this challenge, we propose a fully automated cerebrovascular segmentation framework that integrates conventional voxel-based image analysis with graph-derived vascular features. Our model employs a dual-encoder architecture, where a CNN-based encoder processes MRA images while a second encoder extracts structural vessel information from graph feature-based images. The fusion of these complementary feature representations enhances segmentation accuracy by preserving vessel morphology and improving connectivity. The proposed model was trained and validated on the IXI dataset from COSTA dataset and further evaluated on ADAM and LocH1 datasets. The model achieved a Dice similarity score of 0.8510, Hausdorff distance (HD95) of 3.1150 mm, and average surface distance (ASD) of 0.4646 mm on the IXI dataset, outperforming the conventional 3D UNet model. The proposed model also demonstrated superior performance on external datasets, surpassing the baseline model and proving its generalizability. These results indicate that the proposed model provides a more robust and accurate cerebrovascular segmentation framework, demonstrating its potential for clinical applications.