Two-dimensional Empirical Mode Decomposition Research Articles

Retina images classification based on 2D empirical mode decomposition and multifractal analysis

Diabetic retinopathy is an ocular disease caused by long-term damage to the retina due to high blood sugar levels. Elevated blood sugar can impair the microvasculature in the retina, leading to vascular abnormalities and the formation of abnormal new blood vessels. These changes can manifest in the retina as hemorrhages, leaks, vessel dilation, retinal edema, and retinal detachment. The retinas of individuals with diabetes exhibit different morphologies compared to those without the condition. Most histological images cannot be accurately described using traditional geometric shapes or methods. Therefore, this study aims to evaluate and classify the morphology of retinas with varying degrees of severity using multifractal geometry. In the initial experiments, two-dimensional empirical mode decomposition was employed to extract high-frequency detailed features, and the classification process was based on the most relevant features in the multifractal spectrum associated with disease factors. To eliminate less significant features, the random forest algorithm was utilized. The proposed method achieved an accuracy of 96%, sensitivity of 96%, and specificity of 95%.

A novel approach for breast ultrasound classification using two-dimensional empirical mode decomposition and multiple features

A novel approach for breast ultrasound classification using two-dimensional empirical mode decomposition and multiple features

3D mineral exploration Cu-Zn targeting with multi-source geoscience datasets in the Weilasituo-bairendaba district, Inner Mongolia, China

The Weilasituo-bairendaba district is located at the eastern end of the Central Asian Orogenic Belt, which is an important component of the Cu-Pb-Zn polymetallic metallogenic belt on the Western slope of the Greater Xing’an Range in Inner Mongolia, China. The known Cu-Zn deposits such as the Weilasituo Cu-Zn deposit and the Bairendaba Ag-Pb-Zn deposit are the same tectonic-magmatic product. The district’s structure framework consists of the NE-trending regional faults, while the secondary faults provide channels and space for mineralization. The ore-bearing rocks are either Baoyintu Group gneisses or quartz diorites. The typical Cu-Zn deposits exhibit obvious Cu, Pb, Zn geochemical anomaly as well as obvious magnetic anomaly. The district-scale two-dimensional (2D) mineral prospectivity modeling has been reported. Nowadays, three-dimensional (3D) mineral prospectivity modeling is necessary and urgent. Integrated deposit geology and accumulated exploration data, the above four exploration criteria (regional fault, secondary fault, geochemical anomaly and magnetic susceptibility) are used for 3D mineral prospectivity modeling. Filtering (upward continuation, low pass filtering, two-dimensional empirical mode decomposition), magnetic inversion and 3D modeling techniques were used to construct geological models. Excellent machine learning algorithms such as random forest (RF) and XGBoost are applied. The two machine learning methods confirm each other to improve the accuracy of 3D mineral prospectivity modeling. In this paper, repeated random sampling and Bayesian optimization are combined to construct and tune models. This joint method can avoid the contingency caused by random sampling of negative samples, and can also realize automatic optimization of hyperparameters. The optimal models (RF28 and XGBoost11) were selected among thirty repeated training models for mineral prospectivity modeling. The obtained areas under the ROC curves of RF28 and XGBoost11 were 0.987 and 0.986, respectively. The prediction-area (P-A) plot and C-A fractal were used to delineate targets and grade targets. The targets were divided into Ⅰ-level targets and Ⅱ-level targets. The I- and II-targets are not only highly consistent with the known Cu-Zn deposits, but also exhibit obvious ore-forming geological features. The 3D targets are beneficial for Cu-Zn exploration in the Weilasituo-bairendaba district.

An adaptive infrared image denoising method based on two-dimensional empirical mode decomposition for distribution network inspection UAV

An adaptive denoising method based on 2D empirical mode decomposition (EMD) is proposed to improve the infrared image quality of inspection UNMANNED aerial vehicles (UAVs) and provide guarantee for improving the inspection level of distribution network. Through rapid adaptive two-dimensional empirical mode decomposition algorithm decomposition of a UAV collected for distribution network inspection original noise of infrared image, get more than the IMF component and the residual amount, a forecast noise dominated the IMF component parameters such as threshold value and the variance of noise, using the estimated parameters in combination with the optimal linear interpolation algorithm of noise threshold function of leading the IMF component implementation of threshold denoising. After the denoised IMF component is obtained, the denoised infrared image is obtained after reconstruction with the signal-dominated IMF component, and the adaptive denoising of the infrared image of the distribution network inspection UAV is realized. The experimental results show that the method in this paper can maintain the details of the image, improve the definition, significantly improve the visual effect, the overall denoising performance is stable and feasible, and ensure the quality inspection UAV to collect infrared images.

Using Temporal and Spatial Scales to Unravel the Effects of Climatic Factors on Vegetation Variations in China

Spatio-temporal variation of climatic factors generally contains spatial and temporal components that have different frequencies, which may significantly affect the overall variance structure of vegetation growth at the original scale. The objective of the study was to explore the temporal- and spatial-scale-specific relationships between vegetation growth and climatic factors based on the data of half-monthly normalized difference vegetation index (NDVI), half-monthly averaged daily mean temperature (DMT), half-monthly averaged daily range of temperature (DRT), and half-monthly accumulated precipitation (AP). The complete ensemble empirical mode decomposition (CEEMD) was used to decompose the temporal series of NDVI and climatic factors, and their temporal-scale-specific relationships were examined based on the original half-month scale. Two-dimensional empirical mode decomposition (2D-EMD) was used to decompose the spatial distributions of temporally averaged NDVI and climatic factors, and their spatial-scale-specific relationships were tested based on the original resolution of 1 km. The dominant temporal scales of NDVI were around 3, 15, and >15 years, while the dominant spatial scales of NDVI were around 2 × 104 and >10 × 104 km2. The temporal-scale-specific effects of climatic factors on NDVI were the strongest under mixed forest and were the weakest under broadleaf forest. On a 15-year time scale, NDVI was positively affected by DMT and AP at the 200–1,000 mm precipitation region and negatively affected by DRT at the 200–600 mm precipitation region. Temporal effects of climatic factors had the greatest effects on NDVI in the precipitation region of 200–600 mm and in Yunnan province, and 98.08% of the study area included multi-temporal scale effects. Relationships between NDVI and climatic factors at the half-month scale and other temporal scales were different under different elevation, latitude, longitude, land types, climatic regions, and precipitation. The spatial-scale-specific effects of climatic factors on NDVI were also differed, and the area with effects of the multi-spatial scale was about 64.38%. This indicated that multi-temporal scale and multi-spatial scale analysis could help to understand the mechanisms of effect of climatic factors on vegetation growth and provide the foundation for future vegetation restoration in fragile ecosystems.

Imaging Features of Artificial Intelligence Algorithm in the Analysis of Cerebral Protective Effect of Craniotomy Hematoma Removal under Propofol Anesthesia in Patients with Chronic Subdural Hematoma

Objective. The aim of this work was to study the cerebral protective effect of craniotomy hematoma removal under propofol anesthesia based on the artificial intelligence algorithm analysis of the changes in imaging characteristics of chronic subdural hematoma (CSDH) patients. Methods. A total of 60 CSDH patients who were treated in hospital were recruited and were randomly rolled into an experimental group and a control group, with 30 people in each group. Patients in the experimental group were treated with propofol anesthesia + craniotomy hematoma removal, while those in the control group were treated with conventional anesthesia + craniotomy hematoma removal. Head CT examinations were performed on the next day, one week, one month, three months, and six months after the operation. A two-dimensional empirical mode decomposition (BEMD) algorithm was used for edge detection and denoising of brain CT images of CSDH patients. Then, the amount of hematoma was calculated, and the Markwalder grading was performed to evaluate the neurological function. The number of recurrence and reoperation cases within six months of follow-up was collected. Results. 1. The quality of CT images was remarkably improved after processing with artificial intelligence algorithms. 2. The amount of hematoma in the experimental group was remarkably lower than that in the control group at January, March, and June after surgery (12.89 ± 2.12 VS 20.32 ± 16.41; 7.55 ± 4.13 VS 15.88 ± 14.22; 3.39 ± 3.79 VS 6.55 ± 3.69, P < 0.05 ). 3. The experimental group was remarkably better than the control group in Markwalder grading three months and six months after the operation ( P < 0.05 ). Conclusion. Artificial intelligence algorithm had good effect on the brain CT image processing of CSDH patients, and craniotomy hematoma removal under propofol anesthesia had an ideal brain protection effect.

A Hybrid Method of Covid-19 Patient Detection from Modified CT-Scan/Chest-X-Ray Images Combining Deep Convolutional Neural Network And Two- Dimensional Empirical Mode Decomposition

The outbreak of the SARS-CoV-2/Covid-19 virus in 2019-2020 has made the world look for fast and accurate detection methods of the disease. The most commonly used tools for detecting Covid patients are Chest-X-ray or Chest-CT-scans of the patient. However, sometimes it’s hard for the physicians to diagnose the SARS-CoV-2 infection from the raw image. Moreover, sometimes, deep-learning-based techniques, using raw images, fail to detect the infection. Hence, this paper represents a hybrid method employing both traditional signal processing and deep learning technique for quick detection of SARS-CoV-2 patients based on the CT-scan and Chest-X-ray images of a patient. Unlike the other AI-based methods, here, a CT-scan/Chest-X-ray image is decomposed by two-dimensional Empirical Mode Decomposition (2DEMD), and it generates different orders of Intrinsic Mode Functions (IMFs). Next, The decomposed IMF signals are fed into a deep Convolutional Neural Network (CNN) for feature extraction and classification of Covid patients and Non-Covid patients. The proposed method is validated on three publicly available SARS-CoV-2 data sets using two deep CNN architectures. In all the databases, the modified CT-scan/Chest-X-ray image provides a better result than the raw image in terms of classification accuracy of two fundamental CNNs. This paper represents a new viewpoint of extracting preprocessed features from the raw image using 2DEMD.

Prediction of Soil Nutrients Based on Topographic Factors and Remote Sensing Index in a Coal Mining Area, China

(1) Background: Coal mining operations caused severe land subsidence and altered the distributions of soil nutrients that influenced by multiple environmental factors at different scales. However, the prediction performances for soil nutrients based on their scale-specific relationships with influencing factors remains undefined in the coal mining area. The objective of this study was to establish prediction models of soil nutrients based on their scale-specific relationships with influencing factors in a coal mining area. (2) Methods: Soil samples were collected based on a 1 × 1 km regular grid, and contents of soil organic matter, soil available nitrogen, soil available phosphorus, and soil available potassium were measured. The scale components of soil nutrients and the influencing factors collected from remote sensing and topographic factors were decomposed by two-dimensional empirical mode decomposition (2D-EMD), and the predictions for soil nutrients were established using the methods of multiple linear stepwise regression or partial least squares regression based on original samples (MLSROri or PLSROri), partial least squares regression based on bi-dimensional intrinsic mode function (PLSRBIMF), and the combined method of 2D-EMD, PLSR, and MLSR (2D-EMDPM). (3) Results: The correlation types and correlation coefficients between soil nutrients and influencing factors were scale-dependent. The variances of soil nutrients at smaller scale were stochastic and non-significantly correlated with influencing factors, while their variances at the larger scales were stable. The prediction performances in the coal mining area were better than those in the non-coal mining area, and 2D-EMDPM had the most stable performance. (4) Conclusions: The scale-dependent predictions can be used for soil nutrients in the coal mining areas.

Simultaneous Stereo PIV and MPS3 Wall-Shear Stress Measurements in Turbulent Channel Flow

An extended experimental method is presented in which the micro-pillar shear-stress sensor (MPS 3 ) and high-speed stereo particle-image velocimetry measurements are simultaneously performed in turbulent channel flow to conduct concurrent time-resolved measurements of the two-dimensional wall-shear stress (WSS) distribution and the velocity field in the outer flow. The extended experimental setup, which involves a modified MPS 3 measurement setup and data evaluation compared to the standard method, is presented and used to investigate the footprint of the outer, large-scale motions (LSM) onto the near-wall small-scale motions. The measurements were performed in a fully developed, turbulent channel flow at a friction Reynolds number R e τ = 969 . A separation between large and small scales of the velocity fluctuations and the WSS fluctuations was performed by two-dimensional empirical mode decomposition. A subsequent cross-correlation analysis between the large-scale velocity fluctuations and the large-scale WSS fluctuations shows that the streamwise inclination angle between the LSM in the outer layer and the large-scale footprint imposed onto the near-wall dynamics has a mean value of Θ ¯ x = 16.53 ∘ , which is consistent with the literature relying on direct numerical simulations and hot-wire anemometry data. When also considering the spatial shift in the spanwise direction, the mean inclination angle reduces to Θ ¯ x z = 13.92 ∘ .

Unraveling the local and structured variation of soil nutrients using two-dimensional empirical model decomposition in Fen River Watershed, China

ABSTRACT The spatial patterns of soil nutrients at the sampling scales are the interweaving of local and structured variances. In order to unravel the structured variation of soil nutrients from the original data, two-dimensional empirical mode decomposition (2DEMD) was used to decompose their spatial variability into different scales across a typical watershed. The results suggested that the 2DEMD could separate the local variation represented by IMF1 and structured variation represented by IMF2-4 and residue. Both variance percentages of each IMF and geostatistical parameters of structured variances (IMF2-4) indicated that majority of soil nutrient variations occurred within the local (< 3.2 km) and largest scales (60–90 km for SOM, SAP and SAK, and 219 km for STN km). Climatic factors controlled the soil nutrients under natural land use types, and the topographic factors mainly affected the soil nutrients under cultivated land use types at the largest structured variance. Therefore, at the largest scales, management practices considering slope or elevation are needed to improve the level of soil nutrients under cropland or orchard land in mountainous areas.

Bivariate 2D empirical mode decomposition for analyzing instantaneous turbulent velocity field in unsteady flows

We introduce and demonstrate the bivariate two-dimensional empirical mode decomposition (bivariate 2D-EMD) for the decomposition of a turbulent instantaneous velocity field to separate spatial large-scale organized motion from random turbulent fluctuations. To validate this approach, it was applied to an experimental homogeneous and isotropic turbulent flow (HIT), perturbed by a synthetic Lamb–Oseen vortex that mimics the feature of organized motion. Through different test cases, the scale, the amplitude and the position of the synthetic vortex with respect to the turbulent velocity field were changed. By applying an energy criterion on the modes which resulted from the decomposition process, the initial HIT flow was separated from synthetic perturbation. It is important to point out that in this approach the decomposition as well as the distinction of different parts of the flow are free from any prior and objective assumptions and it requires just one instantaneous velocity field of the flow under analysis. The proposed methodology could be used for analyzing 2D velocity fields obtained from experimental measurement or CFD in different configurations (in-cylinder flow, channel flows, etc.).

Adaptive DSPI phase denoising using mutual information and 2D variational mode decomposition

In digital speckle pattern interferometry (DSPI), noise interference leads to a low peak signal-to-noise ratio (PSNR) and measurement errors in the phase map. This paper proposes an adaptive DSPI phase denoising method based on two-dimensional variational mode decomposition (2D-VMD) and mutual information. Firstly, the DSPI phase map is subjected to 2D-VMD in order to obtain a series of band-limited intrinsic mode functions (BLIMFs). Then, on the basis of characteristics of the BLIMFs and in combination with mutual information, a self-adaptive denoising method is proposed to obtain noise-free components containing the primary phase information. The noise-free components are reconstructed to obtain the denoising DSPI phase map. Simulation and experimental results show that the proposed method can effectively reduce noise interference, giving a PSNR that is higher than that of two-dimensional empirical mode decomposition methods.

Unravelling scale- and location-specific variations in soil properties using the 2-dimensional empirical mode decomposition

Variations in soil properties are both scale- and location- specific. Empirical mode decomposition (EMD) has been used to separate variations in soil properties at different scales. However, the technique has not been fully employed to characterize the 2-dimensional scale-specific variations in soil properties. Across a 21millionha region in Eastern China, 2863 topsoil samples were collected on a semi-regular grid. Two-dimensional EMD algorithm applied to soil observations was used to extract scale- and location- specific variations in elevation data, soil clay content, soil organic matter (SOM) and pH. It was found that the variations in elevation and pH were mostly within large scales while the variations in clay content and SOM were mostly within intermediate to large scales. The variations were also location-specific due to differences in soil type as a function of elevation, parent materials, and land use. Correlations between the controlling factors (elevation and clay) and soil properties (SOM and pH) were also found to be scale- and location- specific, indicating a complex interrelationship between elevation, parent material, vegetation zonation, microbiological communities, leaching and transport of CaCO3, runoff-erosion and fertilization. The results provided guidance for scale- and location- specific soil and land management practices. It was concluded that this method can be applied elsewhere to improve digital soil mapping of soil properties and better understand the variations and interrelationships in soil properties as a function of changing environmental factors and anthropogenic activities. To fully evaluate the performance of the 2-d EMD algorithm, soil samples that were collected on an irregular grid across large spatial extents should be used in the future.

Three-Dimensional Pavement Crack Detection Algorithm Based on Two-Dimensional Empirical Mode Decomposition

AbstractIn order to detect pavement cracking with high accuracy, a type of three-dimensional (3D) pavement detection approach is proposed based on a two-dimensional (2D) empirical mode decompositio...

Two-dimensional empirical mode decomposition of heavy metal spatial variation in agricultural soils, Southeast China.

The distribution of heavy metals in agricultural soils is affected by various anthropogenic activities and environmental factors occurring at different spatial scales. This paper introduced the two-dimensional empirical mode decomposition (2D-EMD) to separate the spatial variability in soil heavy metals into different scales. Geostatistics and multivariate analysis were also utilized to quantify their spatial structure and identify their potential influencing factors. The study was conducted in an arable land in southeastern China where 260 surface soil samples were collected and measured for total contents of cadmium (Cdtotal), mercury (Hgtotal), and sulfur (TS); pH; and soil organic carbon content (SOC). The results showed that both Cdtotal and Hgtotal had high coefficients of variation. The overall variation in all five soil variables was separated into three intrinsic mode functions (IMFs) and spatial residues. All three IMFs had short-range spatial correlations (1-8km), while the spatial residues had moderate-large spatial ranges (13-39km). IMF1 of Cdtotal was strongly correlated with IMF1 of SOC and TS, which was consistent with the principal component analysis. This indicated that IMF1 of Cdtotal represented local variations which were influenced by agricultural activities. IMFs of Hgtotal showed clustered distributions in the study area, with IMF1 and IMF2 of Hgtotal correlated in one principal component, and IMF3 of Hgtotal and IMF3 of soil pH in another component. This indicated that all three IMFs of Hgtotal might be influenced by different industrial activities or different pathways of the same industrial activities. The residues of Cdtotal and Hgtotal, representing the regional trends, only accounted for 26% of the total variance, whereas IMF1 contributed about half of the total variance. It can be concluded that agricultural activities and industrial activities were the dominant contributors of the overall variations in Cdtotal and Hgtotal in the study area, respectively.

LiDAR Data Filtering and DTM Generation Using Empirical Mode Decomposition

LiDAR technology is advancing. As a result, researchers can benefit from high-resolution height data from Earth’s surface. Digital terrain model (DTM) generation and point classification (filtering) are two important problems for LiDAR data. These are connected problems since solving one helps solving the other. Manual classification of LiDAR point data could be time consuming and prone to errors. Hence, it would not be feasible. Therefore, researchers proposed several methods to solve DTM generation and point classification problems. Although these methods work fairly well in most cases, they may not be effective for all scenarios. To contribute in this research topic, a novel method based on two-dimensional (2-D) empirical mode decomposition (EMD) is proposed in this study. Local, nonlinear, and nonstationary characteristics of EMD allow better DTM generation. The proposed method is tested on two publicly available LiDAR dataset, and promising results are obtained. Besides, the proposed method is compared with other methods in the literature. Comparison results indicate that the proposed method has certain advantages in terms of performance.

Functional Biomedical Images of Alzheimer's Disease. A Green's Function-based Empirical Mode Decomposition Study.

Positron emission tomography (PET) provides a functional imaging modality to detect signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D-EMD) provides means to analyze such images. It decomposes the latter into characteristic modes which represent textures on different spatial scales. These textures provide informative features for subsequent classification purposes. The study proposes a new EMD variant which relies on a Green's function based estimation method including a tension parameter to fast and reliably estimate the envelope hypersurfaces interpolating extremal points of the two-dimensional intensity distrubution of the images. The new method represents a fast and stable bi-dimensional EMD which speeds up computations roughly 100-fold. In combination with proper classifiers these exploratory feature extraction techniques can form a computer aided diagnosis (CAD) system to assist clinicians in identifying various diseases from functional images alone. PET images of subjects suffering from Alzheimer's disease are taken to illustrate this ability.

Fabric pattern recognition using mean shift algorithm in empirical mode domain

Pattern recognition is a key issue in the field of textile computer aided analysis and design. Focusing on the non-stationarity of the fabric texture structure, a hybrid method combining two-dimensional mode decomposition with mean shift segmentation is proposed in this paper. Two-dimensional empirical mode decomposition is an adaptive image analysing technique, which uses intrinsic spatial scales instead of predefined basis functions to decompose a given image into a number of intrinsic modes. The first intrinsic mode preserves multiple patterns for an underlying fabric image, which is subsequently submitted to the mean shift clustering process in this paper. Simulation results have proved that the proposed mean shift algorithm in empirical mode domain is in accordance with human visual effect, and possesses the capability of clustering diverse patterns inherent in the textile in a fully unsupervised way.

Extraction of Ocean Fronts Based on Empirical Mode Decomposition

Ocean front is a narrow transitional zone that the penetration of sea is obviously different between two or more waters there. It is an important feature of geophysical turbulence which plays an important role in ocean dynamics. Ocean fronts become visible on radar images because they are associated with a variable surface current which modulates the sea surface roughness and thus the backscattered radar power. This paper propose a new integrated method to extract ocean fronts based on two-dimensional Empirical Mode Decomposition (EMD), image edge detection and mathematical morphology processing. Experimental results show that this integrated method can be effective in ocean front feature extraction.

Two-dimensional nonlinear geophysical data filtering using the multidimensional EEMD method

A variety of two-dimensional (2D) empirical mode decomposition (EMD) methods have been proposed in the last decade. Furthermore, the multidimensional EMD algorithm and its parallel class, multivariate EMD (MEMD), are available in recent years. From those achievements, it is possible to design an efficient 2D nonlinear filter for geophysical data processing. We introduce a robust 2D nonlinear filter which can be applied to enhance the signal of 2D geophysical data or to highlight the feature component on an image. We did this by replacing the conventionally used smooth interpolation in the ensemble empirical mode decomposition (EEMD) algorithm with a piecewise interpolation method. The one-dimensional (1D) EEMD procedures were consecutively performed in all directions, and then the comparable minimal scale combination technique was applied to the decomposed components. The theoretical derivation, model simulation, and real data applications are demonstrated in this paper. The proposed filtering method is effective in improving the image resolution by suppressing the random noise added in the simulation example and strong low frequency track corrugation noise bands with background noise in the field example. Furthermore, the algorithm can be easily extended to higher dimensions by repeating the same procedure in the succeeding dimension. To evaluate the proposed method, one data set is processed separately by using the enhanced analytic signal method and the multivariate EMD (MEMD) algorithm, and the results from these two methods are compared with that of the proposed method. A general equation for generating three-dimensional (3D) EEMD components based on the comparable minimal scale combination principle is derived for further applications.