Implementation of SRAD (Speckle Reducing Anisotropic Diffusion) Algorithm for Ultrasound Image Denoising

This implementation provides a MATLAB version of the SRAD (Speckle Reducing Anisotropic Diffusion) algorithm, which serves as an effective image denoising method particularly suitable for ultrasound image processing. The SRAD algorithm reduces speckle noise through anisotropic diffusion, thereby improving image quality and clarity. The core algorithm involves implementing a partial differential equation that performs diffusion based on local image gradients, preserving edges while smoothing homogeneous regions. Key implementation aspects include calculating diffusion coefficients using local statistics and iteratively applying the diffusion process through finite difference methods. In ultrasound image processing, denoising represents a critical task since speckle noise can degrade both visual quality and diagnostic accuracy. The MATLAB implementation typically involves functions for gradient computation, diffusion coefficient calculation, and iterative updating of pixel values using discretized diffusion equations. By implementing the SRAD algorithm, we can effectively eliminate noise in ultrasound images, resulting in clearer images that are easier to analyze. The algorithm's ability to maintain important anatomical structures while reducing noise makes it particularly valuable for medical imaging applications. Therefore, the SRAD algorithm holds significant potential for widespread application in ultrasound image processing workflows.