Brain Image Denoising and Segmentation Using Advanced Mixed Thresholding and Edge Preservation Analysis Technique

In the realm of medical imaging, particularly brain imaging, the clarity and accuracy of images are paramount for effective diagnosis and treatment planning. However, the inherent presence of noise in imaging modalities such as MRI often degrades the quality of these crucial images, complicating the segmentation process and potentially leading to inaccurate diagnoses. Addressing this challenge, our research introduces an innovative approach that synergizes advanced mixed thresholding with edge preservation analysis to significantly enhance brain image denoising and segmentation. This technique leverages a novel mixed thresholding method that dynamically adjusts to the specific noise characteristics of brain images, effectively reducing noise while retaining critical image details. Concurrently, our edge preservation analysis ensures that essential structural information is maintained, preventing the loss of vital features during the denoising process. The combination of these methods provides a robust solution to the persistent issue of noise in brain imaging, promising to improve the accuracy of segmentation and, by extension, the reliability of medical diagnoses derived from these images. By integrating advanced algorithms and mathematical models, our approach not only outperforms existing denoising and segmentation techniques but also establishes a new benchmark for image processing in medical diagnostics.