Impact of Training Data Quality on Deep Speckle Noise Reduction in Ultrasound Images

Abstract

Speckle noise reduction is an essential step in ultrasound image analysis. One of the challenges in speckle noise reduction is removing noise without significantly losing image detail. Various studies have been conducted using image processing and deep learning approaches. This research offers a simple framework using a deep learning method, which shows that the quality of the images used in the training process influences the performance of the denoising results using the trained network. The training data in the form of ultrasound images in this study was processed separately using various speckle noise reduction methods. We also compare with one of the pre-trained networks, namely denoising convolutional neural networks (DnCNNs). The research shows that denoising results using training data processed using the hybrid speckle noise method provide high image edge preservation performance. The tests in MATLAB reveal a significant reduction in the speckle noise of the ultrasound image, with a peak signal-to-noise ratio of 20.68 dB, a mean structural similarity index measure (MSSIM) of 0.83, and Pratt's Figure Of Merit metric indicating an edge preservation index value reaching 91.76%. Reducing speckle noise using this approach takes less time, ensuring well-maintained edge information and clear visibility of image details, making it applicable for ultrasound diagnosis.