A Hybrid Speckle Reduction Model for Effective Noise Removal in Ultrasound Images

A hybrid speckle reduction model aiming to enhance the quality of ultrasound images by synergistically combining Speckle Reduction Anisotropic Diffusion (SRAD) and Deep Neural Network (DNN) architectures. The model employs SRAD as a pre-processing step to locally smooth ultrasound images, preserving structural details while reducing speckle noise. Subsequently, a DNN refines the images by learning intricate patterns associated with both noise and anatomical structures. The effectiveness of the proposed model is evaluated on four diverse ultrasound images, representing various anatomical regions. Performance metrics, including Signal-to-Noise Ratio (SNR) and Structural Similarity Index (SSI), are used to quantify improvements in image quality compared to traditional methods. Experimental results demonstrate that the hybrid model consistently outperforms existing techniques, showcasing its ability to reduce noise and preserve diagnostic information. This novel approach holds promise for improving diagnostic accuracy in ultrasound-based clinical applications. The integration of SRAD and DNN leverages their complementary strengths, offering a robust solution for effective speckle noise removal in ultrasound images. The proposed model stands as a valuable contribution to advancing the quality of medical imaging and ultimately enhancing patient care