Medical Ultrasound Image Filtering: Classic Algorithms and Implementation Approaches

This paper discusses the critical importance of medical ultrasound image filtering and presents several classical algorithms specifically designed for speckle noise reduction. The core algorithms covered include Lee's adaptive filtering method (which utilizes local statistics for noise suppression), Frost's exponential weighting approach (employing a convolution kernel with distance-dependent weights), Kuan's local linear minimum mean square error filter (applying scene statistics models), and MAP (Maximum a Posteriori) Bayesian estimation filtering. Additionally, we provide in-depth analysis of other filtering algorithms' applications and performance characteristics. Each algorithm's implementation typically involves calculating local window statistics, applying specific mathematical operations, and optimizing parameters based on noise characteristics. Through detailed examination of these algorithms' core principles and practical implementation considerations, we aim to provide valuable insights and guidance for research and practice in medical ultrasound image filtering. These filtering algorithm studies contribute significantly to improving medical ultrasound image quality, enhancing diagnostic accuracy, and ultimately delivering better healthcare services for both physicians and patients.