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We propose a new nonparametric technique for clutter rejection. We consider the Doppler data sampled using a sufficiently large dynamic range to allow for the clutter rejection to be implemented on the digital side. The Doppler signal is modeled as the summation of the true velocity signal, a clutter component, and a random noise component. To simplify the analysis, the first two components are assumed as deterministic yet unknown signals. The Doppler data are collected from the sample volume of interest as well as from several sample volumes in its neighborhood. Given that the shape of the clutter component will be similar in all these signals and given its relatively higher magnitude, it is possible to separate this component using principal component analysis (PCA). In particular, the clutter component appears as the first eigenvector (principal component) in PCA. Given this principal component, the projection of the Doppler signal of interest onto this component is removed and the remaining signal is subsequently used to derive the Doppler spectrogram. We describe an efficient implementation methodology that allows the added computational complexity of the new system to be reasonable.

No abstract

Ultrasound imaging technology is one of the most important clinical imaging modalities due to its safety, low cost, in addition to its versatile applications. The main technical problem in this technology is its noisy appearance due to the presence of speckle, which makes reading imaging more difficult. In this study, a new method of speckle reduction in medical ultrasound images is proposed based on adaptive shifting of the contrast sensitivity function of human vision using a bias field map estimated from the original image. The aim of this work is to have an effective image enhancement strategy that reduces speckle while preserving diagnostically useful image features and allowing practical implementation in real-time for medical ultrasound imaging applications. The new method is used to improve the visual perception of image quality of ultrasound images by adding a local brightness bias to the areas with speckle noise. This allows the variations in image pixels due to speckle noise to be better perceived by the human observer because of the visual perception model. The performance of the proposed method is objectively assessed using quantitative image quality metrics and compared to previous methods. Furthermore, given that image quality perception is subjective, the level of added bias is controlled by a single parameter that accommodates the different needs for different users and applications. This method has potential to offer better viewing conditions of ultrasound images, which translates to higher diagnostic accuracy.

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.