Remove Background Noise Research Articles

Chromosome Extraction Based on U-Net and YOLOv3

Karyotype analysis based on chromosome banding and microscopic imaging is an important means for the diagnosis of genetic symptoms. Chromosome extraction is one of the key steps in karyotype analysis, but it faces some complex situations i.e. chromosome overlaps and adhesions, which are still a challenge for traditional algorithms. Here, we proposed a method for chromosome extraction based on deep learning. In this method, U-Net was used to segment the original micrographs to remove background noise such as nuclei and other interferences. Then YOLOv3 was used to detect and extract each chromosome. Further, U-Net was used again to extract the single chromosomes precisely. The results show that this method can remove effectively the interferences outside the chromosomes, and accurately extract the overlapping and adhesive chromosomes. The accuracy of extracting chromosomes from the raw G-band chromosome images reaches 99.3%. This method is of great significance for the development of automatic karyotype analysis technology.

An improved combined vegetation difference index and burn scar index approach for mapping cropland burned areas using combined data from Landsat 8 multispectral and thermal infrared bands

The accurate extraction of agricultural burned area is essential for fire-induced air quality models and assessments of agricultural grain loss and wildfire disasters. The present study provides an improved approach for mapping uncontrolled cropland burned areas, which involves pre-classification using a difference vegetation index model for various agricultural land scenarios. Land surface temperature was analysed in burned and unburned areas and integrated into a previous burn scar index (BSI) model, and multispectral and thermal infrared information were combined to create a new temperature BSI (TBSI) to remove background noise. The TBSI model was applied to a winter wheat agricultural region in the Haihe River Basin in northern China. The extracted burned areas were validated using Gaofen-1 satellite data and compared with those produced by the previous BSI model. The producer and user accuracy of the new TBSI model were measured at 92.42 and 95.31% respectively, with an overall kappa value of 0.92, whereas those of the previous BSI model were 83.33, 87.30% and 0.86. The results indicate that the new method is more appropriate for mapping uncontrolled winter wheat burned area. Potential applications of this research include trace gas emission models, agricultural fire management and agricultural wildfire disaster assessment.

Binary Image Analysis Technique for Preprocessing of Excessively dilated characters in Aged Kannada Document Images

In this paper, variety of document image enhancement techniques are applied for removal of background noise in the degraded document images. The noise removal techniques are applied on different forms of noise including non-uniform illuminations, complex stain marks, user annotations, show through effect and foxing effect. In this work, Binary Image Analysis (BIA) Technique is proposed for removal of aging degradation in ancient document images of Kannada literature. The method involves multiple phases comprising of contrast enhancement, Gaussian smoothing, binarization, morphological processing, object detection using connected component analysis and filtering followed by marginal noise removal of non-textual regions. The document samples employed for experimentation comprised of more than 175 aged and highly degraded scanned documents of old Kannada literature and poetry that are massively affected by noise and 25 images from DIBCO datasets collected across 2009 to 2017. The results of the experimentation are quite satisfactory and suitable enough for processing of document images in the subsequent stages of OCR..The experimentations are compared with some widely used approaches like Sauvola, Otsu, Gaussian. It is noticed that the proposed method outperforms other noise removal methods in terms of character retentions for extensively degraded and aged document images.

Binary Image Analysis Technique for Preprocessing of Excessively dilated characters in Aged Kannada Document Images

In this paper, variety of document image enhancement techniques are applied for removal of background noise in the degraded document images. The noise removal techniques are applied on different forms of noise including non-uniform illuminations, complex stain marks, user annotations, show through effect and foxing effect. In this work, Binary Image Analysis (BIA) Technique is proposed for removal of aging degradation in ancient document images of Kannada literature. The method involves multiple phases comprising of contrast enhancement, Gaussian smoothing, binarization, morphological processing, object detection using connected component analysis and filtering followed by marginal noise removal of non-textual regions. The document samples employed for experimentation comprised of more than 175 aged and highly degraded scanned documents of old Kannada literature and poetry that are massively affected by noise and 25 images from DIBCO datasets collected across 2009 to 2017. The results of the experimentation are quite satisfactory and suitable enough for processing of document images in the subsequent stages of OCR..The experimentations are compared with some widely used approaches like Sauvola, Otsu, Gaussian. It is noticed that the proposed method outperforms other noise removal methods in terms of character retentions for extensively degraded and aged document images.

Method for Distinguishing Humans and Animals in Vital Signs Monitoring Using IR-UWB Radar.

Radar has been widely applied in many scenarios as a critical remote sensing tool for non-contact vital sign monitoring, particularly for sleep monitoring and heart rate measurement within the home environment. For non-contact monitoring with radar, interference from house pets is an important issue that has been neglected in the past. Many animals have respiratory frequencies similar to those of humans, and they are easily mistaken for human targets in non-contact monitoring, which would trigger a false alarm because of incorrect physiological parameters from the animal. In this study, humans and common pets in families, such as dogs, cats, and rabbits, were detected using an impulse radio ultrawideband (IR-UWB) radar, and the echo signals were analyzed in the time and frequency domains. Subsequently, based on the distinct in-body structure between humans and animals, we propose a parameter, the respiratory and heartbeat energy ratio (RHER), which reflects the contribution rate of breathing and heartbeat in the detected vital signs. Combining this parameter with the energy index, we developed a novel scheme to distinguish between humans and animals. In the developed scheme, after background noise removal and direct-current component suppression, an energy indicator is used to initially identify the target. The signal is then decomposed using a variational mode decomposition algorithm, and the variational intrinsic mode functions that represent human respiration and heartbeat components are obtained and utilized to calculate the RHER parameter. Finally, the RHER index is applied to rapidly distinguish between humans and animals. Our experimental results demonstrate that the proposed approach more effectively distinguishes between humans and animals in terms of monitoring vital signs than the existing methods. Furthermore, its rapidity and need for only minimal calculation resources enable it to meet the needs of real-time monitoring.

Image Processing Based Fault Detection And Isolation For Mechanical Components

Fault Detection and Isolation (FDI) is essential in mechanical industry to detect and isolate objects with manufacturing defect. At present in assembly line, mechanical components are transported from one stage to other stage for assembly, packing etc. During this process, components are randomly drawn from the conveyor belt and manually inspected. Since the random inspection is done manually, there is a chance of missing out defected components in the assembly line. Manual inspection is time consuming and all the features of the components cannot be verified accurately. Hence, there is a need for a image processing based system to detect the anomalies in the components sent in the conveyor belt. In this work, camera is mounted above the conveyor module and captures the images of nuts and bolts which moving on conveyor belt. Captured images are preprocessed to remove background noise, then image is enhanced to get the appropriate features and Region of Interest (diameter of nut) is extracted to measure the diameter. If any anomaly is found in the attributes (diameter) of the mechanical components, an electrical signal will be sent to the Solenoid valve and then it actuates deflector plate by the pneumatic cylinder. Defected component is then carried by the secondary conveyor to the re-matching and the quality product are then carried to the packaging will passed to the separator through microcontroller. In this way, components with manufacturing defect are identified and isolated from assembly line.

Whispered Speech Enhancement Based on Improved Mel Frequency Scale and Modified Compensated Phase Spectrum

The whispered speech enhancement based on a novel improved Mel frequency scale is investigated in the proposed algorithm. The scale is derived from the characteristics of whispered speech. The whispered speech magnitude spectrum recombines with a changed phase spectrum in the process of synthesis rather than preserving the noisy whispered speech phase spectrum. The significance of phase correction is that the low-energy component of the new complex spectrum cancels more than the high-energy component, thus removing background noise as much as possible. Moreover, the noise estimation parameter in the compensated phase is obtained by a new method. This algorithm tries to find a trade-off mechanism between the whispered speech distortion, the noise reduction and the level of remnant music noise. The objective and subjective evaluations show that the proposed algorithm outperforms comparable whispered speech enhancement algorithms.

Processing Digital Image for Measurement of Crack Dimensions in Concrete

The elements of the concrete structure are most frequently affected by cracking. Crack detection is essential to ensure safety and performance during its service life. Cracks do not have a regular shape, in order to achieve the exact dimensions of the crack; the general mathematical formulae are by no means applicable. The authors have proposed a new method which aims to measure the crack dimensions of the concrete by utilizing digital image processing technique. A new algorithm has been defined in MATLAB. The acquired data has been analyzed to obtain the most precise results. Here both the length and width of the crack are obtained from image processing by removing background noise for the accuracy of measurement. A semi-automatic methodology is adapted to measure the crack length and crack width. The applicability of the program is verified with the past literature works.

An intelligent algorithm for coherent sound source localization based on a strong tracking filter

Background noise is inevitable when sensor arrays are used for aeroacoustic measurements in wind tunnels. The direct removal of background noise, however, would affect the measurement accuracy. In particular, the existing array signal processing algorithms are either invalid or inefficient for removing the noise that is coherent with the signal of interest. In this paper, an intelligent algorithm is developed to localize the coherent sound sources and background noise in real time by iteratively checking the collected data from the array. The proposed method can automatically adjust the suboptimal fading factor to extract useful information as much as possible in residual sequence. This algorithm is tested in simulations and then demonstrated in an experiment. Compared to the two existing methods, the results indicate that the new method has a good phase-shift tracking ability and rapid estimation-error convergence speed, and can achieve an acceptable performance even for low-cost acoustic sensors. Overall, the proposed method should assist array beamforming and hence benefit aeroacoustic measurement.

Osteological analysis of 3D printed skeletal remains.

Analysis of skeletal remains allows researchers to learn about an individual, their way of life, and possible cause of death. After remains are repatriated or cremated we are limited in our ability to learn about that individual. Researchers now are digitizing skeletal remains allowing for more research opportunities once the skeleton is no longer available. Medical CT scanners capture internal and external morphology within 0.01mm accuracy. The purpose of this study was to reanalyze and reconstruct a young skeleton conserved via CT scanner. The CT data was extracted and converted into a 3D file using InVesalius, then edited in 3DBuilder. Editing consisted of removing background noise and the CT bed. Bones were then 3D printed on a ZPrinter 310 Plus composite 3D printer for analysis. Available skull bone fragments were reconstructed to assist with the skeletal analysis. Standard osteometric and anthroposcopic techniques were used on the remains. Preliminary results suggest the skeleton is very young based on formation of deciduous and adult teeth. Sex of the individual will be determined using mandibular shape as the individual was young and the pelvis is not present. Limited axial skeletal fragments and one appendicular skeletal fragment will make the osteological analysis very difficult.

Acoustic microscopy signal processing method for detecting near-surface defects in metal materials

When ultrasonic pulse echo technique is used for the detection of defects in materials, the amplitude of the defect echo is considerably lower than that of the interface echo, and the echo generated by the defect in the near-surface always overlaps with the interface echo, leading to the difficult extraction of defect characteristics in the near-surface of a material. Therefore, a method combining adaptive morphological filtering with sparse minimum entropy deconvolution (M-S-MED) was proposed. First, adaptive morphological filtering was applied for the removal of background noise and for making the defect echo obvious. Then, sparse minimum entropy deconvolution was performed on the characteristic signals for the acquisition of the reflected pulse sequences of ultrasonic signals. The depth and size of the defect were accurately evaluated because of the effective separation of the interface and defect echoes. The effectiveness of the proposed method was validated by simulating the signals and detecting a near-surface defect in an actual galvanized sheet. The experimental result revealed that the depth and size errors of the actual defect were 1.9% and 3.5%, respectively.

Hydroacoustic Data‐Analysis Recommendations to Quantify Prey‐Fish Abundance in Shallow, Target‐Rich Ecosystems

AbstractStandardized sampling methods can benefit fisheries management by facilitating a comparison of fish abundance estimates within and among ecosystems. Towards developing an improved standardized hydroacoustic method for estimating prey‐fish abundance and target strength in small (surface areas <15 km2), shallow (maximum depths <20 m), freshwater ecosystems (e.g., north‐temperate reservoirs), we evaluated how the incorporation of several recommended hydroacoustic data‐analysis procedures from the Great Lakes Standard Operating Procedures (GLSOP) into an existing hydroacoustic protocol used in north‐temperate reservoirs influenced estimates of prey‐fish abundance. To facilitate our evaluation, we conducted hydroacoustic surveys in four Ohio reservoirs during August 2016 and then compared prey‐fish abundance estimates generated from the existing Ohio Department of Natural Resources, Division of Wildlife reservoir protocol with those that included three GLSOP recommendations separately and in combination. The three recommendations were (1) removal of background noise, (2) use of a lower maximum beam compensation (MBC) setting, and (3) application of the Sawada index (Nv) to identify and replace biased, in situ, cell‐specific target strengths. Our analyses showed that the removal of background noise (−0.1% to 0% change) and application of Nv (−7.8% to 0% change) had little substantive effect. However, decreasing the MBC from 12 to 6 dB significantly reduced prey‐fish abundance estimates (4.1% to 16.1% decrease). When we simultaneously applied all three GLSOP recommendations, estimated prey‐fish abundance consistently decreased (−8.8 to −16.0%) across reservoirs. Collectively, our findings suggest the need for incorporation of several GLSOP data‐analysis procedures into protocols developed for shallow, target‐rich ecosystems, such as Ohio reservoirs. By doing so, more precise estimates of fish abundance can be generated to the benefit of fisheries management.

Application of Parameter Optimized Variational Mode Decomposition Method in Fault Diagnosis of Gearbox

The selection of variational mode decomposition (VMD) parameters usually adopts the empirical method, trial-and-error method, or single-objective optimization method. The above-mentioned method cannot achieve the global optimal effect. Therefore, a multi-objective particle swarm optimization (MOPSO) algorithm is proposed to optimize the parameters of VMD, and it is applied to the composite fault diagnosis of the gearbox. The specific steps are: first, symbol dynamic entropy (SDE) can effectively remove background noise, and use state mode probability and state transition to preserve fault information. Power spectral entropy (PSE) reflects the complexity of signal frequency composition. Therefore, the SDE and PSE are selected as fitness functions and then the Pareto frontier optimal solution set is obtained by the MOPSO algorithm. Finally, the optimal combination of VMD parameters (k, a) is obtained by normalization. The improved VMD is used to analyze the simulation signal and gearbox fault signal. The effectiveness of the proposed method is verified by comparing with the ensemble empirical mode decomposition (EEMD).

An Integrated Deep Learning Framework for Occluded Pedestrian Tracking

Numerous object-tracking and multiple-person-tracking algorithms have been developed in the field of computer vision, but few trackers can properly address the issue of when a pedestrian is partially or fully occluded by other objects or persons. In order to achieve efficient pedestrian tracking in various occlusion conditions, a pedestrian tracking framework is proposed and developed based on the deep learning networks. First, a pedestrian detector is trained as a tracking mechanism based on the Faster R-CNN, which narrows the search range and efficiently improves accuracy, as compared with the traditional gradient descent algorithm. Second, in the process of target matching, a color histogram and scale-invariant feature transform are combined to provide the target model expression, and a full convolution network (FCN) is trained to extract the pedestrian information in the target model, based on an FCN image semantic segmentation algorithm that can remove background noise effectively. Finally, the extensive experiments on a commonly used tracking benchmark show that the proposed method achieves better performance than the other state-of-the-art trackers in various occlusion situations.

An Evaluation of the Information Technology of Gene Expression Profiles Processing Stability for Different Levels of Noise Components

This paper presents the results of research concerning the evaluation of stability of information technology of gene expression profiles processing with the use of gene expression profiles, which contain different levels of noise components. The information technology is presented as a structural block-chart, which contains all stages of the studied data processing. The hybrid model of objective clustering based on the SOTA algorithm and the technology of gene regulatory networks reconstruction have been investigated to evaluate the stability to the level of the noise components. The results of the simulation have shown that the hybrid model of the objective clustering has high level of stability to noise components and vice versa, the technology of gene regulatory networks reconstruction is rather sensitive to the level of noise component. The obtained results indicate the importance of gene expression profiles preprocessing at the early stage of the gene regulatory network reconstruction in order to remove background noise and non-informative genes in terms of the used criteria.

A Practical Algorithm to Improve Tracking Accuracy of Symmetric Body Surface Using Optical Surface Imaging System

The human body surface has a property of right-left symmetry. Some anatomical sites, such as breasts, legs, and arms, also possess rotational symmetry. This geometric symmetry constitutes a difficult technical barrier for 3D scanner-based optical surface imaging systems. Such systems only measure the distance between the interrogation light source and points of interest on the body surface. Because of this, these systems are unable to differentiate those points on the body surface that move along the symmetry direction or rotate about the symmetry axis simultaneously. This technical limitation hinders their applications on symmetric body surfaces. To overcome this technical barrier, we have developed an algorithm which can calculate a surface-specific region of interest (ROI) for accurate surface tracking. Our algorithm consists of 6 steps: 1) remove background noise; 2) create a seed ROI; 3) add a maximal margin to the seed ROI; 4) rectify ROI overexpansion; 5) eliminate flat topography; and 6) maximize asymmetry and height differences of ROI. Briefly, the algorithm starts by cutting off irrelevant surfaces, including floor, couch, head, lower body, and immobilization devices. A square seed ROI is computed and centered on isocenter, assigned an initial size of 80x80 mm in x and y-axis. The algorithm searches for the maximal and minimal height (z-axis) within it. Thereafter, a 200 mm growth margin is added to the seed ROI. The algorithm compares z-values of two neighboring points in the new ROI. If they are significantly different, then there is a change in topographic feature. ROI grows and repeats the same operation along this direction. The algorithm stops if z-values are too close. The algorithm may expand into body lateral side. To avoid it, a z-value thresholding technique is used so that points with a height lower than the set threshold are eliminated. Two additional thresholding techniques are employed to eliminate a surface with very flat topography. Finally, a symmetry break-up loop is used to maximize the asymmetry and height standard deviation of the ROI. To speed up algorithm performance and circumvent unexpected noise features (such as mask and sheet), the idea of contour line is used in the algorithm. With the ROIs generated by our algorithm, it was found that our optimized asymmetric ROIs were more effective and accurate in detecting shifts by symmetric body surfaces than using symmetric ROIs. It is particularly effective and sensitive in detecting rotational shifts by body surfaces with a rotational symmetry. For optical surface imaging systems, poorly defined large ROIs may produce stable, but often wrong tracking results. This problem is particularly pronounced for symmetric surfaces, such as the breast surface. Our novel concept of optimized asymmetric ROI with a limited size is extremely valuable for real-time patient local tracking applications.

Two-dimensional flow visualization and velocity measurement in natural convection near indoor heated surfaces using a thermal image velocimetry method

Indoor velocity measurement techniques are categorized into point-wise and global-wise measurement techniques. Currently, measurements are either intrusive or restricted to the measurement area. This study presents a thermal image velocimetry (TIV)-based flow measurement method that is suitable for visualizing indoor two-dimensional velocity fields near indoor heated surfaces. The proposed technique uses only an infrared camera for mapping the surface temperature fluctuations. Image processing steps that are used to recover the velocity distribution include the decomposition of the video files into individual frames, the application of filtering to remove background noise, cross-calculation to estimate the velocity, and a final velocity correction based on the continuity equation. To investigate the feasibility of this method, natural convection was studied close to a heated vertical surface in a rectangular cavity. Thermal image velocimetry and particle image velocimetry (PIV) were used to visualize the flow field above a heating unit. The results indicate that the airflow field can be visualized by TIV, and the results measured by TIV are shown to be similar to those for the surface of 6 mm away from the heated surface measured by PIV. A linear correlation is established between TIV and PIV.

A Novel Contrast Enhancement Technique Based on Combination of Local and Global Statistical Data on Malaria Images

Malaria appears to be one of the main reasons for detrimental health issue at the global scale that is responsible for approximately half a million deaths every year. As the cases of malaria seem to escalate at an annual rate, it is vital to provide a rapid and accurate diagnosis through manual microscopic assessment in the attempt to control the spread of malaria. Nevertheless, varied staining steps and noise disruptions can cause inaccurate diagnosis due to wrong interpretation. Hence, to address such issues, this study investigated the performance upon removing background noise and the method of correcting illumination that has an impact upon segmentation for a computer-assisted diagnostic system. The findings display that the technique of based on Otsu threshold and statistic data used to enhance the contrast image as to determine cells infected by the malaria parasite, in comparison to other methods. In fact, this method was tested on 450 malaria images, which consisted of P. Vivax, P. Falciparum, and P. Knowlesi species at the stages of trophozoite, schizont, and gametocyte. As a result, the HSE approach yielded 1.31 for Global Contrast Factor (GCF), while 10.56 for Signal Noise Ratio (SNR).

De-noising research on terahertz holographic reconstructed image based on weighted nuclear norm minimization method

Terahertz imaging is one of the forefront topics of imaging technology today. Denoising process is the key for improving the resolution of the terahertz holographic reconstructed image. Based on the fact that the weighted nuclear norm minimization (WNNM) method preserves the details of the reconstructed image well and the nonlocal mean (NLM) algorithm performs better in the removal of background noise, this paper proposes a new method in which the NLM algorithm is used to improve the WNNM method. The experimental observation and quantitative analysis of the denoising results prove that the new method has better denoising effect for the terahertz holographic reconstructed image.

Abstract 435: Accurate measurement of tumor mutation burden through tumor-only sequencing using a 500-gene panel

Abstract Introduction: Recently, it has been shown by various studies that tumor mutation burden (TMB) can correlate with a cancer patient's responsiveness to checkpoint inhibitor immunotherapy. These findings have stimulated a widespread interest in the development of cost-effective assays that accurately measure TMB. Here we assess whether TMB measured through next-generation sequencing (NGS) of a 2-megabase 500-gene panel correlates with that determined through whole exome sequencing (WES). We further investigate whether sequencing tumor samples alone combined with a germline filtering and background noise removal algorithm can measure TMB accurately compared with sequencing of tumor-normal pairs. Experimental Method: WES data of tumor and subject-matched normal DNA were either downloaded from The Cancer Genome Atlas (TCGA) or generated in-house. A 500-gene targeted DNA sequencing panel, which includes various cancer-relevant genes and covers two megabases, was also developed in-house. TMB was calculated as mutations (single nucleotide variants, insertions, and deletions) per megabase. Data Summary: Using downloaded TCGA data from 2,385 subjects across colon, lung and melanoma cancer types, TMB was measured using tumor-normal WES or the in-silico-filtered tumor-only 500-gene panel and the two approaches correlated linearly (R2 = 0.96). Furthermore, we demonstrated that the targeted panel achieved >90% sensitivity and >85% specificity when classifying TMB-high and low tumor samples at the cutoff of 15 compared to the same TCGA data. TMB as determined from tumor-only WES was also analyzed using computational germline-filtering and background noise-removal to demonstrate excellent concordance with WES of tumor-normal pairs. To further validate these findings, by analyzing WES data and 500-gene panel targeted NGS data of >45 subjects generated in house. Through interrogating the 500-gene NGS data, we found that TMB determined using tumor-only sequencing was also similar from that ascertained through tumor-normal pairs. Of interest, further analysis showed that TMB determined from the 500-gene panel data using tumor-only sequencing along with germline filtering and background noise removing was comparable to that calculated from WES of tumor-normal pairs. Conclusion: Collectively, our data demonstrates that TMB determined from NGS of a two megabase DNA region utilizing a 500 gene panel correlates strongly with TMB measured through WES, with a sensitivity of >90% and a specificity of >85%. In addition, sequencing tumor samples alone combined with bioinformatic germline-filtering and background noise-removal was similar to sequencing subject-matched normal DNA. These findings support the use of a 500 gene panel for accurate measurement of TMB while the addition of bioinformatics enables tumor-only sequencing. Citation Format: Alex Steven So, Shannon Kaplan, Chen Zhao, Shile Zhang, Li Liu, Phillip Le, Raakhee Vijayaraghavan, Tingting Jiang, Sven Bilke, Traci Pawlowski, Karen Gutekunst. Accurate measurement of tumor mutation burden through tumor-only sequencing using a 500-gene panel [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 435.