Brain Region Segmentation using Dual Optimized Total Variation Denoising and Semantic Segmentation

The identification of specific regions of the brain is an essential part of neurological diagnoses and research. The traditional methods have often been impeded by noise interference, which has led to inaccurate region boundary definitions as a consequence. This paper presents an improved method for reliable brain area identification by capitalising on the combined strengths of Total Variation (TV) denoising, which is increased by the use of a dual algorithm, and sophisticated semantic segmentation approaches. Evaluations that compare results demonstrate significant increases in the Peak Signal-to Noise Ratio (PSNR) and Mean Squared Error (MSE), both of which are indicators of the method’s effectiveness in reducing noise. This improved precision implies that constant and exact segmentation of brain areas may be achieved, even under imaging situations that are difficult. Our technique, with its promise of high fidelity and computational efficiency, is set to redefine the landscape of brain mapping, offering substantial implications for both academic research and therapeutic practises. This is because our methodology is positioned to reshape the landscape of brain mapping.