Artifact Removal Research Articles

Optimization of CBCT data with image processing methods and production with fused deposition modeling 3D printing.

The present study has investigated the effect of the removal of artifacts in cone beam computed tomography (CBCT) images with image processing techniques to dental implant planning. The aim of this study has been to benefit from the novel image processing techniques and additive manufacturing technologies in order to change the existing approach in the usage of the 3D model in the orthogonal surgery, traumatic cases, and tumor operations and to solve the restrictions in surgical operations. In the study, firstly, 3 × 3, 5 × 5, and 7 × 7 kernel values were determined on the CBCT image data of the patient. The determined kernel values were applied on CBCT images by choosing median, median-mean-Gaussian (MMG), and bilateral filters, which are quite successful in removing noise in medical images. A thresholding process to separate teeth and bones from soft tissue regions on CBCT images, histogram normalization for a balanced color distribution, morphology operations to reduce noise areas, and tooth and bone boundaries were determined as closely as possible to patient anatomy. The original image and the images obtained from image enhancement techniques were compared. Results showed that the 3 × 3 median filtering method from three different kernel values out of three different image processing methods used in the study greatly improved the artifacts. It has also been shown that the availability of image processing and additive manufacturing methods on CBCT images has been shown to be a highly important factor before dental surgery planning.

The Application of Deep Learning to Electroencephalograms, Magnetic Resonance Imaging, and Implants for the Detection of Epileptic Seizures: A Narrative Review.

Epilepsy is a neurological disorder characterized by recurrent seizures affecting millions worldwide. Medically intractable seizures in epilepsy patients are not only detrimental to the quality of life but also pose a significant threat to their safety. Outcomes of epilepsy therapy can be improved by early detection and intervention during the interictal window period. Electroencephalography is the primary diagnostic tool for epilepsy, but accurate interpretation of seizure activity is challenging and highly time-consuming. Machine learning (ML) and deep learning (DL) algorithms enable us to analyze complex EEG data, which can not only help us diagnose but also locate epileptogenic zones and predict medical and surgical treatment outcomes. DL models such as convolutional neural networks (CNNs), inspired by visual processing, can be used to classify EEG activity. By applying preprocessing techniques, signal quality can be enhanced by denoising and artifact removal. DL can also be incorporated into the analysis of magnetic resonance imaging (MRI) data, which can help in the localization of epileptogenic zones in the brain. Proper detection of these zones can help in good neurosurgical outcomes. Recent advancements in DL have facilitated the implementation of these systems in neural implants and wearable devices, allowing for real-time seizure detection. This has the potential to transform the management of drug-refractory epilepsy. This review explores the application of ML and DL techniques to Electroencephalograms (EEGs), MRI, and wearable devices for epileptic seizure detection. This review briefly explains the fundamentals of both artificial intelligence (AI) and DL, highlighting these systems' potential advantages and undeniable limitations.

An Image-Denoising Framework Using ℓq Norm-Based Higher Order Variation and Fractional Variation with Overlapping Group Sparsity

As one of the most significant issues in imaging science, image denoising plays a major role in plenty of image processing applications. Due to the ill-posed nature of image denoising, total variation regularization is widely used in image denoising problems for its capability to suppress noise and preserve image edges. Nevertheless, traditional total variation will inevitably yield undesirable staircase artifacts when applied to recorded images. Inspired by the success of ℓq norm minimization and overlapping group sparsity in image denoising, and the effective staircase artifacts removal by fractional total variation, the hybrid model which combines the fractional order total variation with overlapping group sparsity and higher order total variation with ℓq norm is developed in this paper to restore images corrupted by Gaussian noise. An efficient algorithm based on the parallel linear alternating direction method of multipliers is developed for solving the corresponding model and the numerical experiments demonstrate the effectiveness of the proposed approach against several state-of-the-art methods, in terms of peak signal-to-noise ratio and structure similarity index measure values.

A PCA Based Artifact Removal Algorithm for Neural Signal Acquisition with kS/s Sampling Rate.

The contamination of stimulus artifacts during Deep Brain Stimulation (DBS) brings challenges to the signal processing, especially when the ratio of the kS/s sampling rate to the stimulation frequency is not an integer. In this work we study to deal with this problem. A transfer function is built to describe the relationship between the stimulation signal and the artifact at the acquisition site. A principal component analysis (PCA) based linear regression algorithm for eliminating the artifact is proposed. The algorithm can be used for the artifact removal with low sampling rate of the neural signal. Higher than 60% correlation coefficient of the artifact-free signal and the predetermined self-generated signal is achieved when the artifact is 60dB larger than the predetermined signal. The numerical recipe for the critical algorithm is also proposed, lowering the complexity from cubic degree to square degree.

Pulsation artifact removal from intra-operatively recorded local field potentials using sparse signal processing and data-specific dictionary.

Neural recordings frequently get contaminated by ECG or pulsation artifacts. These large amplitude components can mask the neural patterns of interest and make the visual inspection process difficult. The current study describes a sparse signal representation strategy that targets to denoise pulsation artifacts in local field potentials (LFPs) recorded intraoperatively. To estimate the morphology of the artifact, we first detect the QRS-peaks from the simultaneously recorded ECG trace as an anchor point. After the LFP data has been epoched with respect to each beat, a pool of raw data segments of a specific length is generated. Using the K-singular value decomposition (K-SVD) algorithm, we constructed a data-specific dictionary to represent each contaminated LFP epoch in a sparse fashion. Since LFP is aligned to each QRS complex and the background neural activity is uncorrelated to the anchor points, we assumed that constructed dictionary will be formed to mainly represent the pulsation artifact. In this scheme, we performed an orthogonal matching pursuit to represent each LFP epoch as a linear combination of the dictionary atoms. The denoised LFP data is thus obtained by calculating the residual between the raw LFP and its approximation. We discuss and demonstrate the improvements in denoised data and compare the results with respect to principal component analysis (PCA). We noted that there is a comparable change in the signal for visual inspection to observe various oscillating patterns in the alpha and beta bands. We also see a noticeable compression of signal strength in the lower frequency band (<13 Hz), which was masked by the pulsation artifact, and a strong increase in the signal-to-noise ratio (SNR) in the denoised data.Clinical Relevance- Pulsation artifact can mask relevant neural activity patterns and make their visual inspection difficult. Using sparse signal representation, we established a new approach to reconstruct the quasiperiodic pulsation template and computed the residue signal to achieve noise-free neural activity.

Enhancing EEG Artifact Removal Efficiency by Introducing Dense Skip Connections to IC-U-Net.

Electroencephalographic (EEG) data is considered contaminated with various types of artifacts. Deep learning has been successfully applied to developing EEG artifact removal techniques to increase the signal-to-noise ratio (SNR) and enhance brain-computer interface performance. Recently, our research team has proposed an end-to-end UNet-based EEG artifact removal technique, IC-U-Net, which can reconstruct signals against various artifacts. However, this model suffers from being prone to overfitting with a limited training dataset size and demanding a high computational cost. To address these issues, this study attempted to leverage the architecture of UNet++ to improve the practicability of IC-U-Net by introducing dense skip connections in the encoder-decoder architecture. Results showed that this proposed model obtained superior SNR to the original model with half the number of parameters. Also, this proposed model achieved comparable convergency using a quarter of the training data size.

Enhanced Contactless Heart Rate Monitoring Using Camera with Motion Artifact Removal During Physical Activities.

Contactless monitoring of heart rate (HR) can improve passive and continuous tracking of cardiovascular activities and overall people's health. Remote photoplethysmography (rPPG) using a camera eliminates the need for a wearable device. rPPG-based HR has shown promising results to be accurate and comparable to conventional methods such as contact PPG. Most experiments use stationary subjects while motion is known to affect the accuracy of remote PPG. In this paper, a novel methodology is introduced to enhance the accuracy and reliability of HR monitoring based on rPPG in the presence of physical activities like Yoga. This method quickly and accurately tracks HR and analyzes head motion to exclude unreliable data within short windows of rPPG signals. The method was tested with smartphone video data collected from 60 subjects when they are doing activities with varying levels of movement. Results show that our method without motion removal improves the accuracy of the HR readings by 0.7 bpm, reaching 3.57 bpm on average for a 30-sec-window. The accuracy is further improved by another 1.3 bpm after removing the motion artifacts, and reaches 2.29 bpm.Clinical relevance- The enhancement of HR readings from shorter rPPG signal with motion tolerance during physical activities can ultimately help with a more reliable HR tracking of people in uncontrolled settings like home which is a critical step towards remote health-care or wellness tracking.

A sparse dictionary representation approach for anti-scattering grid artifact removal in X-ray images

The aim of this work is to introduce a new joint estimation approach for anatomical content and artifacts in digital radiography. Herein, we apply this approach to the anti-scattering grid suppression problem. These grids are generally used in low-dose radiography to improve the image contrast by absorbing the scattered radiation. However, they introduce an irregular stripe pattern in the clinical images which can hinder the diagnosis made by the radiologist.The problem is tackled in a sparse dictionary representation framework with an observation and prior models accounting for the physical model of image contents. Thus, the anatomical content is supposed to be sparsely represented by a Fourier or K-SVD dictionary. Furthermore, the artifact is modeled by a frequency dictionary. In addition, the noise is modeled by a locally stationary Gaussian distribution with zero mean and unknown variance. The joint estimation of all the projections is then performed using two methods: Beam-forming (IAA) and Bayesian (SUBLIM). Moreover, patch-wise processing is applied to avoid models’ errors due to spatial variations of the clinical contents and the artifact.IAA and SUBLIM performance is demonstrated for the anti-scattering grid extraction problem using experimental and real clinical images. The proposed methods remove gridline artifacts while maintaining the image details and avoiding ringing artifacts. In addition, they produce better results compared to the conventional filtering approaches, especially when the metallic materials are present in or on the patients. SUBLIM outperforms IAA in those regions thanks to a better clinical content model and physical noise prior.

Comprehensive evaluation of artifact reduction and tissue recovery effects of metal artifact reduction technique based on full-reference metric

For the comprehensive evaluation of metal artifact reduction (MAR) technique, not only the removal of metal artifacts but also the evaluation of the area restored by MAR is required. We propose a method to comprehensively evaluate the effect by MAR in this study. We have conducted the computed tomography scan to acquire both the evaluation image and the reference image for the full-reference based evaluation. The evaluation image and reference image were reconstructed into 24 image sets according to the tube potentials, image reconstruction method, and use of the MAR technique. Images of two different positions were selected according to the distance from metal and material (bone, tissue) distribution, and bone and tissue were automatically segmented in both evaluation and reference images. The values of full width at half the maximum (FWHM) and centroid were extracted after Gaussian modeling of each segmented region. Then, we computed four evaluation metrics (FWHMNM: non-MAR to non-metal ratio of FWHM, FWHMM: MAR to non-metal ratio of FWHM, CENTNM: non-MAR to non-metal ratio of centroid, CENTM: MAR to non-metal ratio of centroid), and the MAR image and non-MAR image were compared. The overlap ratio automatically segmented from the evaluation image and reference image were position 1 (bone: 99.61%, tissue: 99.23%) with 80 kVp, position 1 (bone: 99.32%, tissue: 99.56%) with 120 kVp, position 2 (bone: 99.20%, tissue: 99.73%) with 80 kVp, and position 2 (bone: 99.23%, tissue: 99.67%) with 120 kVp. The FWHMNM showing the change of image pixel value by metal artifact was calculated as (bone: 1.32–1.46, tissue: 1.08–1.16) at 80 kVp and (bone: 1.19–1.27, tissue: 1.02–1.05) at 120 kVp. More metal artifacts occurred at 80 kVp tube potential. Regardless of the tube potential and image reconstruction method, the MAR showed an overall artifact reduction effect (1 < FWHMM < FWHMNM). However, distortion of pixel values occurred due to the MAR in regions where metal artifacts were high in proximity to metal (1 < FWHMNM < FWHMM). Overall, the average value of the medium was maintained (CENTM: 0.98–1.03) after MAR application, but there was a change of image value in region around the metal (CENTM: 0.97–1.11). In this study, we propose a new method to evaluate the effect of metal artifacts and MAR technique using full-reference based method. Metal artifacts, effect of MAR technique, and side-effect caused by MAR technique were quantitatively analyzed through proposed method. There are some limitations in applying it to clinical imaging since our method is a reference-based evaluation. However, our experimental results were important for understanding the effects of the MAR technique and its functional properties.

Free scan real time 3D ultrasound imaging with shading artefacts removal

Ultrasound imaging (USI) is a widely adopted imaging method in clinical diagnosis owing to its low cost, convenience, and safety. However, due to the complex acoustic attenuation, two-dimensional (2D) USI lacks the capability to achieve a clear imaging result when the target is shaded by high echo tissues. This paper proposes a three-dimensional (3D) free-scan real-time ultrasound imaging (FRUSI) method. By integrating 2D ultrasound image sequences around the region of interest (ROI) with a real-time and spatially accurate probe tracking method, the proposed FRUSI system provides clear and accurate ultrasound images for medical study. The experiment results on reconstruction precision and accuracy show the potential ability of our proposed system to provide high-quality 3D ultrasound imaging. Moreover, previously shaded targets can be discerned clearly in the same scan plane in both phantom studies and in vivo studies on the human finger joint. The performance of the proposed FRUSI system has demonstrated its potential value for clinical diagnosis to provide high ultrasound imaging quality and rich details in spatial information. Due to the convenient setup, the FRUSI system might potentially be expanded to other ultrasound imaging modalities.

Optimized detection and segmentation of nuclei in gastric cancer images using stain normalization and blurred artifact removal

Histological analysis with microscopy is the gold standard to diagnose and stage cancer, where slides or whole slide images are analyzed for cell morphological and spatial features by pathologists. The nuclei of cancerous cells are characterized by nonuniform chromatin distribution, irregular shapes, and varying size. As nucleus area and shape alone carry prognostic value, detection and segmentation of nuclei are among the most important steps in disease grading. However, evaluation of nuclei is a laborious, time-consuming, and subjective process with large variation among pathologists. Recent advances in digital pathology have allowed significant applications in nuclei detection, segmentation, and classification, but automated image analysis is greatly affected by staining factors, scanner variability, and imaging artifacts, requiring robust image preprocessing, normalization, and segmentation methods for clinically satisfactory results. In this paper, we aimed to evaluate and compare the digital image analysis techniques used in clinical pathology and research in the setting of gastric cancer. A literature review was conducted to evaluate potential methods of improving nuclei detection. Digitized images of 35 patients from a retrospective cohort of gastric adenocarcinoma at Oulu University Hospital in 1987–2016 were annotated for nuclei (n = 9085) by expert pathologists and 14 images of different cancer types from public TCGA dataset with annotated nuclei (n = 7000) were used as a comparison to evaluate applicability in other cancer types. The detection and segmentation accuracy with the selected color normalization and stain separation techniques were compared between the methods. The extracted information can be supplemented by patient’s medical data and fed to the existing statistical clinical tools or subjected to subsequent AI-assisted classification and prediction models. The performance of each method is evaluated by several metrics against the annotations done by expert pathologists. The F1-measure of 0.854 ± 0.068 is achieved with color normalization for the gastric cancer dataset, and 0.907 ± 0.044 with color deconvolution for the public dataset, showing comparable results to the earlier state-of-the-art works. The developed techniques serve as a basis for further research on application and interpretability of AI-assisted tools for gastric cancer diagnosis.

A truncated generalized Huber prior for image smoothing

Image smoothing is a fundamental task in computer vision and graphics. This paper presents a new image smoothing method based on a truncated generalized Huber prior. The proposed model is neither convex nor concave and is hard to optimize. We first transform the prior into a concave one, then utilize the technique of half-quadratic minimization to get an equivalent convex surrogate function. Thus the numerical algorithm is obtained by solving a weighted least square problem and iteratively updating the weights. The convergence of the algorithm is theoretically guaranteed. The proposed method is flexible and powerful in preserving edge/structure and eliminating undesired information. The effectiveness of the proposed method is demonstrated by several applications, including scale space filtering, texture removal, and clip-art JPEG artifacts removal.

Precision Heart Rate Estimation Using a PPG Sensor Patch Equipped with New Algorithms of Pre-Quality Checking and Hankel Decomposition.

A new method for accurately estimating heart rates based on a single photoplethysmography (PPG) signal and accelerations is proposed in this study, considering motion artifacts due to subjects' hand motions and walking. The method comprises two sub-algorithms: pre-quality checking and motion artifact removal (MAR) via Hankel decomposition. PPGs and accelerations were collected using a wearable device equipped with a PPG sensor patch and a 3-axis accelerometer. The motion artifacts caused by hand movements and walking were effectively mitigated by the two aforementioned sub-algorithms. The first sub-algorithm utilized a new quality-assessment criterion to identify highly noise-contaminated PPG signals and exclude them from subsequent processing. The second sub-algorithm employed the Hankel matrix and singular value decomposition (SVD) to effectively identify, decompose, and remove motion artifacts. Experimental data collected during hand-moving and walking were considered for evaluation. The performance of the proposed algorithms was assessed using the datasets from the IEEE Signal Processing Cup 2015. The obtained results demonstrated an average error of merely 0.7345 ± 8.1129 beats per minute (bpm) and a mean absolute error of 1.86 bpm for walking, making it the second most accurate method to date that employs a single PPG and a 3-axis accelerometer. The proposed method also achieved the best accuracy of 3.78 bpm in mean absolute errors among all previously reported studies for hand-moving scenarios.

A Segmentation-Denoising Network for Artifact Removal From Single-Channel EEG

As an important neurorecording technique, electroencephalography (EEG) is often contaminated by various artifacts, which obstructs subsequent analysis. In recent years, deep learning-based methods have been proven to be promising for artifact removal. However, most denoising methods focus on recovering clean EEG from raw signals contaminated by the noise over the entire recording period, ignoring that the practical EEG recordings may contain clean segments in addition to noise segments. Therefore, the general model may cause distortion when dealing with clean segments. In this paper, we propose a simple, yet effective segmentation-denoising network (SDNet) for artifact removal. The proposed method is capable of differentiating noisy EEG segments from clean ones via semantic segmentation, avoiding the distortion caused by processing clean segments. We conduct a performance comparison on semi-simulated and real EEG data. The experimental results demonstrate that SDNet outperforms the state-of-art approaches. This work provides a novel way to reconstruct artifact-attenuated EEG signals, and may further benefit the EEG-based diagnosis and treatment.

Coordinated underwater dark channel prior for artifact removal of challenging image enhancement

Coordinated underwater dark channel prior for artifact removal of challenging image enhancement

A novel functional stock-well transform for ocular artifact removal from EEG

A novel functional stock-well transform for ocular artifact removal from EEG

Development of acoustic source localization with adaptive neural network using distance mating‐based red deer algorithm

AbstractMultichannel, audio processing approaches are widely examined in human–computer interaction, autonomous robots, audio surveillance, and teleconferencing systems. The numerous applications are linked to the speech technology and acoustic analysis area. Much attention is received to the active speakers and spatial localization of acoustic sources on the acoustic sensor arrays. Baseline approaches provide negotiable performance in a real‐world comprised of far‐field/near‐field monitoring, reverberant and noisy environments, and also the outdoor/indoor scenarios. A practical system to detect defects in complex structures is the time difference mapping (TDM) technique. The significant scope of the research is to search the location using the minimum distance point in the time difference database to be apart from the verification point. In the case of the improved “time difference mapping (I‐TDM)” technique and traditional “time difference mapping (T‐TDM)” technique, the denser grids and vast database permit increased accuracy. In the database, if the location points are not present, then the accurate localization of the I‐TDM and T‐TDM techniques is damaged. Hence, to handle these problems, this article plans to develop acoustic source localization according to the deep learning strategy. The audio dataset is gathered from the benchmark source called the SSLR dataset and is initially subjected to preprocessing, which involves artifact removal and smoothing for effective processing. Further, the adaptive convolutional neural network (CNN)‐based feature set creation is performed. Here, the adaptive CNN is accomplished by the improved optimization algorithm called distance mating‐based red deer algorithm (DM‐RDA). With this trained feature set, the acoustic source localization is done by the weight updated deep neural network, in which the same DM‐RDA is used for optimizing the training weight. The simulation outcome proves that the designed model produced enhanced performance compared to other traditional source localization estimators.

Polarization image demosaicing and RGB image enhancement for a color polarization sparse focal plane array.

The color division of focal plane (DoFP) polarization sensor structure mostly uses Bayer filter and polarization filter superimposed on each other, which makes the polarization imaging unsatisfactory in terms of photon transmission rate and information fidelity. In order to obtain high-resolution polarization images and high-quality RGB images simultaneously, we simulate a sparse division of focal plane polarization sensor structure, and seek a sweet spot of the simultaneous distribution of the Bayer filter and the polarization filters to obtain both high-resolution polarization images and high-quality RGB images. In addition, From the perspective of sparse polarization sensor imaging, leaving aside the traditional idea of polarization intensity interpolation, we propose a new sparse Stokes vector completion method, in which the network structure avoids the introduction and amplification of noise during polarization information acquisition by mapping the S1 and S2 components directly. The sparsely polarimetric image demosaicing (Sparse-PDM) model is a progressive combined structure of RGB image artifact removal enhancement network and sparsely polarimetric image completion network, which aims to compensate sparsely polarimetric Stokes parameter images with the de-artifacts RGB image as a guide, thus achieving high-quality polarization information and RGB image acquisition. Qualitative and quantitative experimental results on both self-constructed and publicly available datasets prove the superiority of our method over state-of-the-art methods.

Removal of artifact in bio signal using ECG for QRS detection

This paper proposed a removal of artifacts in bio signal, which is used to improve the signal strength by using Pan Tompkins algorithm for QRS signal detection. This algorithm is compared with different filtering technique such as noise cancellation filter, squaring filter, moving average and differentiator filter. Recorded bio signal and SNR ratio measurement will be shown in simulation result using MATLAB 2018a software tool, this filtering technique data contains noisy input signal with 3 second time delay. The sampling rate of the ECG signal is 200Hz on power line noise from machine power supply, ECG signal wandering after cancellation DC drift and normalization process. Efficacy of the filters for removing the artifact and generate the effect on the ECG waveform, in terms of accuracy rate, the SNR ratio of ECG in pipeline architecture has 93% of efficiency is achieved.

PeakDetective: A Semisupervised Deep Learning-Based Approach for Peak Curation in Untargeted Metabolomics.

Peak-detection algorithms currently used to process untargeted metabolomics data were designed to maximize sensitivity at the sacrifice of selectively. Peak lists returned by conventional software tools therefore contain a high density of artifacts that do not represent real chemical analytes, which, in turn, hinder downstream analyses. Although some innovative approaches to remove artifacts have recently been introduced, they involve extensive user intervention due to the diversity of peak shapes present within and across metabolomics data sets. To address this bottleneck in metabolomics data processing, we developed a semisupervised deep learning-based approach, PeakDetective, for classification of detected peaks as artifacts or true peaks. Our approach utilizes two techniques for artifact removal. First, an unsupervised autoencoder is used to extract a low-dimensional, latent representation of each peak. Second, a classifier is trained with active learning to discriminate between artifacts and true peaks. Through active learning, the classifier is trained with less than 100 user-labeled peaks in a matter of minutes. Given the speed of its training, PeakDetective can be rapidly tailored to specific LC/MS methods and sample types to maximize performance on each type of data set. In addition to curation, the trained models can also be utilized for peak detection to immediately detect peaks with both high sensitivity and selectivity. We validated PeakDetective on five diverse LC/MS data sets, where PeakDetective showed greater accuracy compared to current approaches. When applied to a SARS-CoV-2 data set, PeakDetective enabled more statistically significant metabolites to be detected. PeakDetective is open source and available as a Python package at https://github.com/pattilab/PeakDetective.