Determination of optimal parameters for bilateral filter in brain MR image denoising

Enhanced Wavelet-Based Medical Image Denoising with Bayesian-Optimized Bilateral Filtering

Medical image denoising is essential for improving the clarity and accuracy of diagnostic images. In this paper, we present an enhanced wavelet-based method for medical image denoising, aiming to effectively remove noise while preserving critical image details. After applying wavelet denoising, a bilateral filter is utilized as a post-processing step to further enhance image quality by reducing noise while maintaining edge sharpness. The bilateral filter's effectiveness heavily depends on its parameters, which must be carefully optimized. To achieve this, we employ Bayesian optimization, a powerful technique that efficiently identifies the optimal filter parameters, ensuring the best balance between noise reduction and detail preservation. The experimental results demonstrate a significant improvement in image denoising performance, validating the effectiveness of our approach.

MRI Denoising Using Pixel-Wise Threshold Selection

Magnetic resonance imaging (MRI) has emerged as a promising technique for non-invasive medical imaging. The primary challenge in MRI is the trade-off between image visual quality and acquisition time. Current MRI image denoising algorithms employ global thresholding to denoise the whole image, which leads to inadequate denoising or image distortion. This study introduces a novel pixel-wise (localized) thresholding approach of singular vectors, obtained from singular value decomposition, to denoise magnetic resonance (MR) images. The pixel-wise thresholding of singular vectors is performed using separate singular values as thresholds at each pixel, which is advantageous given the spatial noise variation throughout the image. The method presented is validated on MR images of a standard phantom approved by the magnetic resonance accreditation program (MRAP). The denoised images display superior visual quality and recover minute structural information otherwise suppressed in the noisy image. The increase in peak-signal-to-noise-ratio (PSNR) and contrast-to-noise-ratio (CNR) values of ≥ 18% and ≥ 200% of the denoised images, respectively, imply efficient noise removal and visual quality enhancement. The structural similarity index (SSIM) of ≥ 0.95 for denoised images indicates that the crucial structural information is recovered through the presented method. A comparison with the standard filtering methods widely used for MRI denoising establishes the superior performance of the presented method. The presented pixel-wise denoising technique reduces the scan time by 2-3 times and has the potential to be integrated into any MRI system to obtain faster and better quality images.

Combining variational mode decomposition with regularisation techniques to denoise MRI data

In this paper, we propose a novel method for removing noise from MRI data by exploiting regularisation techniques combined with variational mode decomposition. Variational mode decomposition is a new decomposition technique for sparse decomposition of a 1D or 2D signal into a set of modes. In turn, regularisation is a method that can translate the ill-posed problem (e.g., image denoising) into a well-posed problem. The proposed method aims to remove the noise from the image in two steps. In the first step, the MR imaging data are decomposed by the 2D variational mode decomposition algorithm. In the second step, for effective suppression of Rician noise from MRI data, we used the fused lasso signal approximator with all modes acquired from the medical scan. The performance of the proposed approach was compared with state-of-the-art reference methods based on different metrics, that is, the peak signal-to-noise ratio, the structural similarity index metrics, the high-frequency error norm, the quality index based on local variance, and the sharpness index. The experiments were performed on the basis of both simulated and real images. The presented results prove the high denoising performance of the proposed algorithm; particularly under heavy noise conditions.

Aerial Image Denoising Using a Best-So-Far ABC-based Adaptive Filter Method

Nowadays, digital images play an increasingly important role in helping to explain phenomena and to attract people’s attention through various types of media rather than the use of text. However, the quality of digital images may be degraded due to noise that has occurred either during their recording or their transmission via a network. Therefore, removal of image noise, which is known as “image denoising”, is one of the primary required tasks in digital image processing. Various methods in earlier studies have been developed and proposed to remove the noise found in images. For example, the use of metric filters to eliminate noise has received much attention from researchers in recent literature. However, the convergence speed when searching for the optimal filter coefficient of these proposed algorithms is quite low. Previous research in the past few years has found that biologically inspired approaches are among the more promising metaheuristic methods used to find optimal solutions. In this work, an image denoising approach based on the best-so-far (BSF) ABC algorithm combined with an adaptive filter is proposed to enhance the performance of searching for the optimal filter coefficient in the denoising process. Experimental results indicate that the denoising of images employing the proposed BSF ABC technique yields good quality and the ability to remove noise while preventing the features of the image from being lost in the denoising process. The denoised image quality obtained by the proposed method achieves a 20% increase compared with other recently developed techniques in the field of biologically inspired approaches.

A pervasive health care device computing application for brain tumors with machine and deep learning techniques

Purpose

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

Design/methodology/approach

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

Findings

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

Originality/value

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

A Survey on State-of-the-art Denoising Techniques for Brain Magnetic Resonance Images

The accuracy of the magnetic resonance (MR) image diagnosis depends on the quality of the image, which degrades mainly due to noise and artifacts. The noise is introduced because of erroneous imaging environment or distortion in the transmission system. Therefore, denoising methods play an important role in enhancing the image quality. However, a tradeoff between denoising and preserving the structural details is required. Most of the existing surveys are conducted on a specific MR image modality or on limited denoising schemes. In this context, a comprehensive review on different MR image denoising techniques is inevitable. This survey suggests a new direction in categorizing the MR image denoising techniques. The categorization of the different image models used in medical image processing serves as the basis of our classification. This study includes recent improvements on deep learning-based denoising methods alongwith important traditional MR image denoising methods. The major challenges and their scope of improvement are also discussed. Further, many more evaluation indices are considered for a fair comparison. An elaborate discussion on selecting appropriate method and evaluation metric as per the kind of data is presented. This study may encourage researchers for further work in this domain.

Deep Learning-Based Denoising for High b-Value at 2000 s/mm2 Diffusion-Weighted Imaging

Diffusion-weighted imaging (DWI) allows white matter quantification of the white matter tracts of the brain. However, at a high b-value (≥ 2000 s/mm2), DWI acquisition suffers from noise due to longer acquisition times obscuring white matter interpretation. DWI denoising techniques can be used to acquire high b-value DWI without increasing the number of signal averages. We used a residual learning-based convolutional neural network (DnCNN) to reduce noise in high b-value DWI based on the literature review. We applied the proposed denoising method on high b-value, retrospectively collected DWI data with multiple noise levels. Experimental results show an improved image quality after denoising in retrospective DWI (average PSNR before and after denoising: 27.63 ± 1.06 dB and 51.76 ± 1.95 dB, respectively). The prospective DWI included one and two signal averages for denoising. DWI with four signal averages was used as the reference. Representative images show high b-value prospective DW images denoised using the DnCNN. We demonstrated DnCNN for cases of multiple noise levels and signal averages. For the prospective study, the PSNR values for 1-NEX before and after denoising were 27.39 ± 3.75 dB and 27.68 ± 3.75 dB. For 2-NEX, the PSNR values before and after denoising were 27.51 ± 4.18 dB and 27.75 ± 4.05 dB.

A New ROI-Based performance evaluation method for image denoising using the Squared Eigenfunctions of the Schrödinger Operator

In this paper a new Region Of Interest (ROI) characterization for image denoising performance evaluation is proposed. This technique consists of balancing the contrast between the dark and bright ROIs, in Magnetic Resonance (MR) images, to track the noise removal. It achieves an optimal compromise between removal of noise and preservation of image details. The ROI technique has been tested using synthetic MRI images from the BrainWeb database. Moreover, it has been applied to a recently developed denoising method called Semi-Classical Signal Analysis (SCSA). The SCSA decomposes the image into the squared eigenfunctions of the Schrödinger operator where a soft threshold $h$ is used to remove the noise. The results obtained using real MRI data suggest that this method is suitable for real medical image processing evaluation where the noise-free image is not available.