Over the past several years, although the resolution, signal-to-noise ratio and acquisition
speed of magnetic resonance imaging (MRI) technology have been increased, MR images …

In this paper, we provide an extensive overview of machine learning techniques applied to
structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We …

A new method is presented to denoise 1-D experimental signals using wavelet transforms.
Although the state-of-the-art wavelet denoising methods perform better than other denoising …

A lateralization index (LI) is commonly computed to describe the asymmetry of activation as
detectable by various functional imaging techniques, particularly functional magnetic …

It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in
ultrasound imaging. The recent advancements in ultrasound instrumentation and portable …

Abstract Magnetic Resonance Images (MRI) are often contaminated by rician noise at the
acquisition time. This type of noise typically deteriorates the performance of disease …

The discrete wavelet transform (DWT) is widely used for multiresolution analysis and
decorrelation or “whitening” of nonstationary time series and spatial processes. Wavelets …

Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex
production environments, smart manufacturing as envisioned under Industry 4.0 aims to …

This article reviews the evaluation and optimization of the preprocessing steps for blood-
oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). This …

Postacquisition denoising of magnetic resonance (MR) images is of importance for clinical
diagnosis and computerized analysis, such as tissue classification and segmentation. It has …