Background: Linear Anisotropic Diffusion Filtering (LADF) can improve magneticresonance imaging (MRI) scans for separating brain tumors. MRI scans often suffer from noise and artifact interference, which hinders the identification and segmentation of brain tumors. LADF has the advantage of retaining edge details of the image along its direction while reducing noise along the direction perpendicular to it. This makes the image look good and useful for diagnosing the brain tumor.

Objectives: This project aims to conduct quantitative and qualitative analyses to evaluate the performance of LADF in terms of SNR and edge sharpness. The study focuses on how LADF when applied to simulated MR images with different types and levels of noise (such as Gaussian and speckle noise), produces substantial im-provements in image SNR and edge sharpness. The objective is to demonstrate that better image delineation of the tumor boundary is achieved, which is crucial for ac-curate medical diagnosis and optimal treatment planning.

Methods: The LADF method was applied to simulated MR images contaminated with various noise types and levels. Quantitative measures such as Signal-to-Noise Ratio (SNR) and edge sharpness were used to assess the improvements. The study involved generating noise-added MRI images, applying the LADF algorithm, and analyzing the results to determine the enhancement in image quality.

Results: The results indicated that LADF significantly improves image SNR and edge sharpness. Substantial improvements were observed across all types of noise tested, leading to better delineation of tumor boundaries. This enhancement in image quality supports the hypothesis that LADF can aid in more accurate and effective brain tumor diagnosis.

Conclusions: The clinical implications of integrating LADF into standard MRI pro-cedures were explored. Enhanced imaging could lead to improved accuracy in braintumor diagnosis, facilitating earlier and more effective interventions. The thesis also discusses the real-time deployment of LADF, emphasizing the need for system au-tomation and user-friendly interfaces to ensure the effective utilization of enhanced MRI scans in clinical settings.