Unwanted Artifacts Research Articles

High-density diffuse correlation tomography with enhanced depth localization and minimal surface artefacts.

A spatially weighted filter applied to both the measurement and the Jacobian is proposed for high-density diffuse correlation tomography (DCT) to remove unwanted extracerebral interferences and artefacts along with better depth localization in the reconstructed blood flow images. High-density DCT is implemented by appropriate modification of recently introduced Multi-speckle Diffuse Correlation Spectroscopy (M-DCS) system. Additionally, we have used autocorrelation measurements at multiple delay-times in an iterative manner to improve the reconstruction results. The proposed scheme has been validated by simulations, phantom experiments and in-vivo human experiments.

Addressing Practical Issues in Atomic Force Microscopy-Based Micro-indentation on Human Articular Cartilage Explants

Without a doubt, atomic force microscopy (AFM) is currently one of the most powerful and useful techniques to assess micro and even nano-cues in the biological field. However, as with any other microscopic approach, methodological challenges can arise. In particular, the characteristics of the sample, sample preparation, type of instrument, and indentation probe can lead to unwanted artifacts. In this protocol, we exemplify these emerging issues on healthy as well as osteoarthritic articular cartilage explants. To this end, we first show via a step-by-step approach how to generate, grade, and visually classify ex vivo articular cartilage discs according to different stages of degeneration by means of large 2D mosaic fluorescence imaging of the whole tissue explants. The major strength of the ex vivo model is that it comprises aged, native, human cartilage that allows the investigation of osteoarthritis-related changes from early onset to progression. In addition, common pitfalls in tissue preparation, as well as the actual AFM procedure together with the subsequent data analysis, are also presented. We show how basic but crucial steps such as sample preparation and processing, topographic sample characteristics caused by advanced degeneration, and sample-tip interaction can impact data acquisition. We also subject to scrutiny the most common problems in AFM and describe, where possible, how to overcome them. Knowledge of these limitations is of the utmost importance for correct data acquisition, interpretation, and, ultimately, the embedding of findings into a broad scientific context.

Ultrafast Ambient-Air Exsolution on Metal Oxide via Momentary Photothermal Effect

The process of exsolution for the synthesis of strongly anchored metal nanoparticles (NPs) on host oxide lattices has been proposed as a promising strategy for designing robust catalyst-support composite systems. However, because conventional exsolution processes occur in harsh reducing environments at high temperatures for long periods of time, the choice of support materials and dopant metals are limited to those with inherently high thermal and chemical stability. Herein, we report the exsolution of a series of noble metal catalysts (Pt, Rh, and Ir) from metal oxide nanofibers (WO3 NFs) supports in an entirely ambient environment induced by intense pulsed light (IPL)-derived momentary photothermal treatment (>1000 °C). Since the exsolution process spans an extremely short period of time (<20 ms), unwanted structural artifacts such as decreased surface area and phase transition of the support materials are effectively suppressed. At the same time, exsolved NPs (<5 nm) with uniform size distributions could successfully be formed. To prove the practical utility of exsolved catalytic NPs functionalized on WO3 NFs, the chemiresistive gas sensing characteristics of exsolved Pt-decorated WO3 NFs were analyzed, exhibiting high durability (>200 cyclic exposures), enhanced response (Rair/Rgas > 800 @ 1 ppm/350 °C), and selectivity toward H2S target gas. Altogether, we successfully demonstrated that ultrafast exsolution within a few milliseconds could be induced in ambient conditions using the IPL-derived momentary photothermal treatment and contributed to expanding the practical viability of the exsolution-based synthetic approaches for the production of highly stable catalyst systems.

Systematic replication enables normalization of high-throughput imaging assays.

High-throughput fluorescent microscopy is a popular class of techniques for studying tissues and cells through automated imaging and feature extraction of hundreds to thousands of samples. Like other high-throughput assays, these approaches can suffer from unwanted noise and technical artifacts that obscure the biological signal. In this work, we consider how an experimental design incorporating multiple levels of replication enables the removal of technical artifacts from such image-based platforms. We develop a general approach to remove technical artifacts from high-throughput image data that leverages an experimental design with multiple levels of replication. To illustrate the methods, we consider microenvironment microarrays (MEMAs), a high-throughput platform designed to study cellular responses to microenvironmental perturbations. In application to MEMAs, our approach removes unwanted spatial artifacts and thereby enhances the biological signal. This approach has broad applicability to diverse biological assays. Raw data are on synapse (syn2862345), analysis code is on github: gjhunt/mema_norm, a reproducible Docker image is available on dockerhub: gjhunt/mema_norm. Supplementary data are available at Bioinformatics online.

Approaching closed spherical, full-view detection for photoacoustic tomography.

.Significance: Photoacoustic tomography (PAT) is a widely explored imaging modality and has excellent potential for clinical applications. On the acoustic detection side, limited-view angle and limited-bandwidth are common key issues in PAT systems that result in unwanted artifacts. While analytical and simulation studies of limited-view artifacts are fairly extensive, experimental setups capable of comparing limited-view to an ideal full-view case are lacking.Aims: A custom ring-shaped detector array was assembled and mounted to a 6-axis robot, then rotated and translated to achieve up to steradian view angle coverage of an imaged object.Approach: Minimization of negativity artifacts and phantom imaging were used to optimize the system, followed by demonstrative imaging of a star contrast phantom, a synthetic breast tumor specimen phantom, and a vascular phantom.Results: Optimization of the angular/rotation scans found effective detectors were needed for high-quality images, while 15-mm steps were used to increase the field of view as required depending on the size of the imaged object. Example phantoms were clearly imaged with all discerning features visible and minimal artifacts.Conclusions: A near full-view closed spherical system has been developed, paving the way for future work demonstrating experimentally the significant advantages of using a full-view PAT setup.

A Survey on Denoising Techniques of Electroencephalogram Signals Using Wavelet Transform

Electroencephalogram (EEG) artifacts such as eyeblink, eye movement, and muscle movements widely contaminate the EEG signals. Those unwanted artifacts corrupt the information contained in the EEG signals and degrade the performance of qualitative analysis of clinical applications and as well as EEG-based brain–computer interfaces (BCIs). The applications of wavelet transform in denoising EEG signals are increasing day by day due to its capability of handling non-stationary signals. All the reported wavelet denoising techniques for EEG signals are surveyed in this paper in terms of the quality of noise removal and retrieving important information. In order to evaluate the performance of wavelet denoising techniques for EEG signals and to express the quality of reconstruction, the techniques were evaluated based on the results shown in the respective literature. We also compare certain features in the evaluation of the wavelet denoising techniques, such as the requirement of reference channel, automation, online, and performance on a single channel.

Medical image fusion using fuzzy adaptive reduced pulse coupled neural networks

This paper addresses a novel neuro-fuzzy-based approach to set the weighted linking strength of parameter - adaptive reduced pulse coupled neural networks. In reduced PCNN based medical image fusion algorithms, it is quite essential to evaluate the prominence of each pixel in an image. The fusion performance in turn depends on the linking factor, internal activity. Thus, we need to set these values of reduced PCNN in a more adaptive manner with fewer complications and uncertainties. For this, the weighted linking strength i.e., lambda of the reduced PCNN neurons is attentively set by a fuzzy-based approach. Here, lambda of neurons is represented as fuzzy membership values using the activity level measures such as local information entropy and energy. Finally, a new model called-Fuzzy adaptive reduced pulse coupled neural networks is developed by reducing the number of parameters and fuzzy adaptive settings of them. This leads to a very less complicated network and more computational efficacy, which is a prominent part of health care requirements. The proposed scheme is free from the shortcomings such as loss of boundaries, structural details, unwanted artifacts, degradations, etc. Subjective and objective evaluations show better performance of this new approach compared to the existing techniques.

Co-optimization method to improve lateral resolution in photoacoustic computed tomography.

In biomedical imaging, photoacoustic computed tomography (PACT) has recently gained increased interest as this imaging technique has good optical contrast and depth of acoustic penetration. However, a spinning blur will be introduced during the image reconstruction process due to the limited size of the ultrasonic transducers (UT) and a discontinuous measurement process. In this study, a damping UT and adaptive back-projection co-optimization (CODA) method is developed to improve the lateral spatial resolution of PACT. In our PACT system, a damping aperture UT controls the size of the receiving area, which suppresses image blur at the signal acquisition stage. Then, an innovative adaptive back-projection algorithm is developed, which corrects the undesirable artifacts. The proposed method was evaluated using agar phantom and ex-vivo experiments. The results show that the CODA method can effectively compensate for the spinning blur and eliminate unwanted artifacts in PACT. The proposed method can significantly improve the lateral spatial resolution and image quality of reconstructed images, making it more appealing for wider clinical applications of PACT as a novel, cost-effective modality.

Reduction of Artifacts and Edge Preservation of Underwater Images Using Deep Convolution Neural Network

Underwater exploration has been one of the areas of active research over the last few decades. Image enhancement is difficult for computer vision-based underwater research because of the degradation of the underwater environment’s images. Several types of artifact reduction approaches are already available of value averaging filters to smooth the continuity that artifact reduction approaches appear across image boundaries. While some of these approaches can somewhat reduce these unwanted artifacts’ severity, other approaches have some limitations that can cause blurring to high-contrast edges in the image. Therefore we need novel methods to overcome the theses’ drawbacks. This research work introduced a Deep Convolution Neural Network (DCNN)-based deep learning method to overcome the drawbacks of blurring to low-contrast edges in the image. The proposed system’s performance is validated through simulation, and the simulation results are obtained using Python simulation software. The obtained simulation results demonstrate the superiority of the proposed underwater image enhancement method. The number of different coefficients is compared with the results of the algorithm of the Underwater Image Colorfulness Measure (UICM), Underwater Image Sharpness Measure (UISM), Underwater Image Contrast Measure (UIconM) and Underwater Image Quality Measure (UIQM) values. After that, it was found to be an effective comparison of visualization techniques.

Open-source software for two-dimensional Fourier processing of gridded magnetic data

Magnetic data are widely available and useful in many exploration and applied-geophysics projects. The magnetic data are usually processed, imaged, and interpreted in commercial software packages. The algorithms used in these packages are sometimes difficult to check or tune, and the code is not available for review. However, these packages often have an application programming interface (API) for people to access data and undertake their own processing and data enhancement. In many cases, these APIs use the Python programming language. In the course of developing a new method for transforming magnetic data called reduction to pole and vertical dip (RTPVD), the initial test code was written in Python. This initial code was then rewritten and incorporated into GAMS, an open-source software package capable of using a Python API to read from and then write transformed (or enhanced) data to a commercial database. In addition to RTPVD, the other enhancements GAMS can generate are the zeroth-order analytic-signal amplitude (ASA0), tilt, spatial derivatives of ASA0, the zeroth-order local wavenumber, the first-order analytic-signal amplitude, and the apparent susceptibility. These transformations require that the data be transformed to the wavenumber domain using a fast Fourier transform (FFT), operated on, and then transformed back to the space domain. The FFT and some of the preprocessing steps can be done with a number of built-in Python tools. For the preprocessing steps, some of the available Python options are fast, but they can occasionally introduce unwanted artifacts. Our open-source tool allows users to test the different options and check the intermediate steps to ensure the result is appropriate.

Modeling of pure visco-qP-wave propagation in attenuating tilted transversely isotropic media based on decoupled fractional Laplacians

The pseudoviscoacoustic anisotropic wave equation is widely used in the oil and gas industry for modeling wavefields in attenuating anisotropic media. Compared to the full viscoelastic anisotropic wave equation, it can greatly reduce the computational cost of wavefield modeling while maintaining the visco-qP-wave kinematics very well. However, even if we place the source in a thin isotropic layer, there will be some unwanted S-wave artifacts in the qP wavefield simulated by the pseudoviscoacoustic anisotropic wave equation due to the stepped approximation of inclined layer interfaces. Furthermore, the wavefield simulated by the pseudoviscoacoustic anisotropic wave equation may suffer from numerical instabilities when the anisotropy parameter epsilon is less than delta. To overcome these problems, we derive a pure-viscoacoustic tilted transversely isotropic (TTI) wave equation in media with anisotropy in velocity and attenuation based on the exact complex-valued phase velocity formula. The pure-viscoacoustic TTI wave equation has decoupled amplitude dissipation and phase dispersion terms, which is suitable for further reverse time migration with Q compensation. For numerical simulations, we adopt the second-order Taylor series expansion to replace the mixed-domain spatially variable fractional Laplacian operator, which guarantees the decoupling of the wavenumber from the space-related fractional order. Then, we use an efficient and stable hybrid finite-difference and pseudospectral method (HFDPSM) to solve the pure-viscoacoustic TTI wave equation. Numerical tests indicate that the simulation results of the newly derived pure-viscoacoustic TTI wave equation are stable, free from S-wave artifacts, and accurate. We further demonstrate that HFDPSM outperforms the pseudospectral method in terms of numerical simulation stability and computing efficiency.

Passive radar downrange imaging with multiple transmitters and one receiver

Passive radar systems use illuminators of opportunity to illuminate targets instead of dedicated radar transmitters. The signals of opportunity have lower bandwidth than dedicated active radar systems, leading to poor downrange resolution. Multiple signals of opportunity can be coherently combined to increase the overall bandwidth of the system, and therefore create finer resolution images. These signals are usually separated in the frequency domain (non-contiguous), which causes large unwanted grating lobe artefacts in the image when using back-projection or Fourier transform based imaging. Additionally, these signals may originate from transmitters not located at the same position. A calibration method is developed to align the downrange responses and coherently combine the two signals. A compressive sensing-based algorithm is used to combine the calibrated non-contiguous frequency data, and is shown to mitigate the grating lobe artefacts that occur when using a back-projection algorithm. This approach is validated with simulated and experimental data from a passive radar system tracking a commercial airline using two digital television (DTV) broadcast transmitters.

On the trade-off between compression efficiency and distortion of a new compression algorithm for multichannel EEG signals based on singular value decomposition

In this article we investigate the trade-off between the compression ratio and distortion of a recently published compression technique specifically devised for multichannel electroencephalograph (EEG) signals. In our previous paper, we proved that, when singular value decomposition (SVD) is already performed for denoising or removing unwanted artifacts, it is possible to exploit the same SVD for compression purpose by achieving a compression ratio in the order of 10 and a percentage root mean square distortion in the order of 0.01 %. In this article, we successfully demonstrate how, with a negligible increase in the computational cost of the algorithm, it is possible to further improve the compression ratio by about 10 % by maintaining the same distortion level or, alternatively, to improve the compression ratio by about 50 % by still maintaining the distortion level below the 0.1 %.

MFSNet: A multi focus segmentation network for skin lesion segmentation

Segmentation is essential for medical image analysis to identify and localize diseases, monitor morphological changes, and extract discriminative features for further diagnosis. Skin cancer is one of the most common types of cancer globally, and its early diagnosis is pivotal for the complete elimination of malignant tumors from the body. This research develops an Artificial Intelligence (AI) framework for supervised skin lesion segmentation employing the deep learning approach. The proposed framework, called MFSNet (Multi-Focus Segmentation Network), uses differently scaled feature maps for computing the final segmentation mask using raw input RGB images of skin lesions. In doing so, initially, the images are preprocessed to remove unwanted artifacts and noises. The MFSNet employs the Res2Net backbone, a recently proposed convolutional neural network (CNN), for obtaining deep features used in a Parallel Partial Decoder (PPD) module to get a global map of the segmentation mask. In different stages of the network, convolution features and multi-scale maps are used in two boundary attention (BA) modules and two reverse attention (RA) modules to generate the final segmentation output. MFSNet, when evaluated on three publicly available datasets: PH2, ISIC 2017, and HAM10000, outperforms state-of-the-art methods, justifying the reliability of the framework. The relevant codes for the proposed approach are accessible at https://github.com/Rohit-Kundu/MFSNet.

An n-order hold approach for fractional-delay interpolation in auralization

During the signal processing chain of an auralization simulating the propagation of a sound from a moving source to a stationary receiver, it is often necessary to interpolate between the samples of the source signal in order to arrive at uniformly-spaced samples at the receiver. In some cases, this interpolation is done in the receiver time frame—where the “input” samples of the source have become irregularly spaced due to time dilation effects. Canonical band-limited interpolation methods (i.e., sinc and sinc-derived approaches) cannot be applied in this case as they rely on having a uniformly-spaced input. The use of geometric interpolation methods that can handle irregularly-spaced input may not be grounded in signal processing principles and may produce unwanted artifacts and noise. This presentation outlines the possibility of embedding an irregularly spaced n-order hold signal within a highly over-sampled uniformly-spaced signal and then processing down to the desired sampling rate through successive decimations. Initial distortion and noise characteristics of the approach are shown for some basic propagation geometries. The possible benefits of using such an approach in an auralization scheme with a time-varying Doppler shift are discussed including: the prevention of aliasing, processing time advantages, and real-time processing.

Concurrent stimulation and sensing in bi-directional brain interfaces: a multi-site translational experience

Objective. To provide a design analysis and guidance framework for the implementation of concurrent stimulation and sensing during adaptive deep brain stimulation (aDBS) with particular emphasis on artifact mitigations. Approach. We defined a general architecture of feedback-enabled devices, identified key components in the signal chain which might result in unwanted artifacts and proposed methods that might ultimately enable improved aDBS therapies. We gathered data from research subjects chronically-implanted with an investigational aDBS system, Summit RC + S, to characterize and explore artifact mitigations arising from concurrent stimulation and sensing. We then used a prototype investigational implantable device, DyNeuMo, and a bench-setup that accounts for tissue–electrode properties, to confirm our observations and verify mitigations. The strategies to reduce transient stimulation artifacts and improve performance during aDBS were confirmed in a chronic implant using updated configuration settings. Main results. We derived and validated a ‘checklist’ of configuration settings to improve system performance and areas for future device improvement. Key considerations for the configuration include (a) active instead of passive recharge, (b) sense-channel blanking in the amplifier, (c) high-pass filter settings, (d) tissue–electrode impedance mismatch management, (e) time-frequency trade-offs in the classifier, (f) algorithm blanking and transition rate limits. Without proper channel configuration, the aDBS algorithm was susceptible to limit-cycles of oscillating stimulation independent of physiological state. By applying the checklist, we could optimize each block’s performance characteristics within the overall system. With system-level optimization, a ‘fast’ aDBS prototype algorithm was demonstrated to be feasible without reentrant loops, and with noise performance suitable for subcortical brain circuits. Significance. We present a framework to study sources and propose mitigations of artifacts in devices that provide chronic aDBS. This work highlights the trade-offs in performance as novel sensing devices translate to the clinic. Finding the appropriate balance of constraints is imperative for successful translation of aDBS therapies. Clinical trial: Institutional Review Board and Investigational Device Exemption numbers: NCT02649166/IRB201501021 (University of Florida), NCT04043403/IRB52548 (Stanford University), NCT03582891/IRB1824454 (University of California San Francisco). IDE #180 097.

Hybrid optimization based learning technique for multi‐disease analytics from healthcare big data using optimal pre‐processing, clustering and classifier

AbstractWith the rapid development of health information systems in recent years covering big data trends, clinical information is being adapted to understand important health trends and provide timely prophylactic treatment. However, the current plan fails to maintain a highly organized analysis and processing of health data. In addition, it collects issues that play an important role in determining quality. In this article, we propose a hybrid optimization based learning technique for multi‐disease analytics (HOL‐MD) from healthcare big data using optimal pre‐processing, clustering, and classifier. First, we introduce a capuchin search based optimization algorithm for preprocessing which removes the unwanted artifacts to enhance the detection accuracy. Second, we introduce a modified Harris Hawks optimization based clustering (MHHOC) technique used to select optimal features among multiple features which discovers the subgroups and reduce the dimensionality issues. We evaluated the performance of proposed HOL‐MD technique using standard US healthcare organization dataset, SUSY and HIGGS datasets. Finally, we proved the effectiveness of proposed RTL‐DNN techniques in terms of 11.5%, 9.3%, 2.75%, and 3.45% higher than the existing state‐of‐art techniques in terms of accuracy, precision, recall, and F‐measure respectively.

Cardiac disease detection from ECG signal using discrete wavelet transform with machine learning method

ObjectivesCardiac disease is the leading cause of death worldwide. If a proper diagnosis is made early, cardiovascular problems can be prevented. The ECG test is a diagnostic method used on the screen for heart disease. Based on a combination of multi-field extraction and nonlinear analysis of ECG data, this paper presents a framework for automated detection of heart disease. The main aim of this study is to develop a model for future diagnosis of cardiac vascular disease using ECG analysis and symptom-based detection. MethodsDiscrete wavelet transform and Nonlinear Vector Decomposed Neural Network methods are used to predict Cardiac disease. Here is the discrete wavelet transform used for preprocessing to remove unwanted noise or artifacts. The neural network was fed with thirteen clinical features as input which was then trained using a non-linear vector decomposition of the presence or absence of heart disease. ResultsThe modules were implemented, trained, and tested using UCI and Physio net data repositories. The sensitivity, specificity and accuracy of this research work are 92.0%, 89.33% and 90.67% ConclusionsThe proposed approach can discover complex non-linear correlations between dependent and independent variables without requiring traditional statistical training. The suggested approach improves ECG classification accuracy, allowing for more accurate cardiac disease diagnosis. The accuracy of ECG categorization in identifying cardiac illness is far greater than these other approaches.

Categorization of breast masses based on deep belief network parameters optimized using chaotic krill herd optimization algorithm for frequent diagnosis of breast abnormalities

AbstractSeveral deep learning techniques are utilized for classification of the masses in mammogram images. But the existing method doesnot provide sufficient accuracy . for breast masses classification in mammogram images. In this manuscript, an efficient breast cancer image classification framework utilizing the deep belief network (DBN) through chaotic krill herd optimization (CKHO) algorithm for classification of masses in mammogram images is proposed. Initially the input mammogram images are pre‐processed by altered phase preserving dynamic range compression (APPDRC) method for removing the unwanted noise and artifacts. Then these pre‐processed images are assumed with DBN for classifying that mass in the mammogram images into normal, benign, and malignant lesions. Generally, DBN does not reveal any acceptance of optimization techniques for computing optimal parameters that guarantee an accurate classification. Therefore in this work, proposed CKHO algorithm is employed for optimizing weight parameters of the self‐attention convolutional neural network (SACNN). The simulation process is performed under MATLAB platform. Finally, the proposed DBN with chaotic krill herd optimization algorithm (DBN‐CKHO) attains high accuracy 44.5%, 33.42%, 55.23%, 62.35%, and 43.42% when compared with the existing method such as extreme learning machine classifier with moth flame optimization (ELMC‐MFO), Kernel extreme learning machine classifier with Chaotic salp swarm algorithm (KELMC‐CSSA), Extreme learning machine classifier with fruit fly optimization algorithm (ELMC‐FOA), Kernel extreme learning machine classifier with grasshopper optimization algorithm (KELMC‐GOA) and deep neural network with non‐dominated sorting genetic algorithm‐based classification approach.

Wrist-Worn Hand Gesture Recognition While Walking via Transfer Learning.

Walking, one of the most common daily activities, causes unwanted movement artifacts which can significantly deteriorate hand gesture recognition accuracy. However, traditional hand gesture recognition algorithms are typically developed and validated with wrist-worn devices only during static human poses, neglecting the critical importance of dynamic effects on gesture accuracy. Thus, we developed and validated a signal decomposition approach via empirical mode decomposition to accurately segment target gestures from coupled raw signals during dynamic walking and a transfer learning method based on distribution adaptation to enable gesture recognition through domain transfer between dynamic walking and static standing scenarios. Ten healthy subjects performed seven hand gestures during both walking and standing experiments while wearing an IMU wrist-worn device. Experimental results showed that the signal decomposition approach reduced the gesture detection error by 83.8%, and the transfer learning approach (20% transfer rate) improved hand gesture recognition accuracy by 15.1%. This ground-breaking work demonstrates the feasibility of hand gesture recognition while walking via wrist-worn sensing. These findings serve to inform real-life and ubiquitous adoption of wrist-worn hand gesture recognition for intuitive human-machine interaction in dynamic walking situations.