Enhanced Brain Region Detection Using Total Variation Denoising and Semantic Segmentation

Accurate identification of brain regions in medical images is crucial for diagnostic and research purposes. In this research, we propose a novel method that leverages Total Variation (TV) denoising and semantic segmentation techniques to achieve improved brain region recognition. Our approach builds upon the synergy of these two methods, which have demonstrated superior performance individually. By comparing our method with the conventional Non-Local Means (NLM) approaches, we observe a significant enhancement in both the quality of denoised images and the accuracy of area recognition. To validate the effectiveness of our method, we employ rigorous evaluation metrics including Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE). The analysis of these metrics demonstrates substantial improvements specifically achieved through the integration of Total Variation with Semantic Segmentation. Consequently, we establish the superiority of our proposed method over other existing techniques, highlighting its potential for accurate brain region recognition in medical imaging applications.