Ultrasound Imaging Enhancement Using Denoising AutoEncoders

In ultrasound imaging, speckle noise is acknowledged as a major challenge, affecting visual clarity and compromising diagnostic accuracy. This degradation can lead to erroneous clinical evaluations, highlighting the urgent need to enhance denoising techniques. Various solutions have been proposed to tackle this issue. Convolutional Neural Network-based Denoising AutoEncoders (CNN-DAE) aim to mitigate this issue. However, this strategy suffers from potential loss of intricate details, excessive smoothing of images, and limited ability to adapt to different noise characteristics. In this research, we propose a CNN based Edge Detection Denoising AutoEncoders (CNN-EDDA). The proposed approach focuses on speckle denoising and enhancing the contours of anatomical structures, providing sharper and more precise definitions. This clarity enables the algorithm to effectively denoise the image by identifying regions with abrupt variations, allowing for the distinction of important details from speckle noise. By clarifying the edges, we preserve essential image details while reducing noise. This synergy between filtering and edge detection is crucial for ensuring reliable, high-quality results, facilitating more effective clinical interpretation. Our proposed CNN-EDDA method was evaluated according to subjective visual analyses as well as objective measurements, demonstrating better results than current state-of-the-art methods, with optimal performance metrics.