Towards to Optimal Wavelet Denoising Scheme—A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing

Abstract

Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise.

Magnetic Resonance Imaging Image under Low-Rank Matrix Denoising Algorithm in the Diagnosis and Evaluation of Tibial Plateau Fracture Combined with Meniscus Injury

“…MRI imaging diagnosis plays an important role in the determination of TPF combined with meniscus injury. Among them, the accuracy and resolution of the image are extremely high, and AIbased MRI image denoising has emerged [17]. In this study, the prior information of the noise-free MRI image block was combined with the self-phase prior, added with GMM to cluster the MRI image block; the gradient prior of MRI was introduced to eliminate the ringing effect, and a new MRI image denoising algorithm was constructed and compared with the ADF algorithm.…”

Design of programmable Gaussian‐derived wavelet filter for wearable biomedical sensor

“…Step 4: Perform prey behavior. The next state of the ith artificial fish is obtained according to (11) and then jump to Step 6.…”

“…In this context, the analog implementation of Gaussian and Marr wavelet transforms has been investigated due to low power compared with digital counterpart, but limited to generate fixed-type wavelet base [1]- [4]. So far, the selection of wavelet base is still a major concern since the best wavelet base can be varied depending on specific biomedical signals [9]- [11]. Furthermore, multimodal measurement of physiological signals has been considered as a promising technique in healthcare monitoring and management.…”

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“…Since wavelet transformation offers plenty of settings, it is usually a complicated task to select the most appropriate settings. In Reference [ 4 ], the authors propose a novel scheme that is able to simultaneously evaluate the effectivity of selected wavelet settings via the form of the spatial 2D maps. The authors also study the effect of dynamical noise influence within wavelet smoothing by using the volumetric mapping.…”