Principal Component Analysis Based Hybrid Speckle Noise Reduction Technique for Medical Ultrasound Imaging

Abstract

Ultrasound imaging is the safest and most widely used medical imaging technique available today. The main disadvantage of ultrasound imaging is the presence of speckle noise in its images that may obscure pathological changes in the body and makes diagnosis more challenging. Therefore, many techniques were proposed to reduce speckle and improve image quality. Unfortunately, variations of their performance with different scan parameters and due to their methodologies make it hard to choose which one to adopt in clinical practice. In this work, we consider the problem of combining the information from multiple speckle filters and propose the use of principal component analysis to find the optimal set of weights that would retain the most information and hence would better represent the data in the final image. The new technique is implemented to process ultrasound images collected from a research system and the outcomes are compared to the individual techniques and their average using quantitative image quality metrics. The proposed technique has potential for utilization in clinical settings to provide consistently better-quality combined images that may help improve diagnostic accuracy.

Edge Detail Preservation Technique for Enhancing Speckle Reduction Filtering Performance in Medical Ultrasound Imaging

“…Wavelet-based methods primarily operate using wavelet shrinkage with different wavelet families and levels of composition [21] , [22], [23] , [24]. Hybrids incorporating elements from these methods have also been introduced [25] , [26], [34], [35]. Other approaches include methods that use a human visual system model to reduce the appearance of noise in ultrasound images [36], and methods involving deep learning using convolutional neural networks to build despeckling models from training custom-designed networks [37], [38].…”