Bidimensional Ensemble Empirical Mode Decomposition Research Articles

Comparing BEMD and BEEMD for identifying geochemical anomalies associated with Cu polymetallic mineralization in the southwestern Fujian province (China)

In this paper, Bi-dimensional empirical mode decomposition (BEMD) and bi-dimensional ensemble empirical mode decomposition (BEEMD) are compared to identify geochemical anomalies using a case study from Cu polymetallic mineralization district in southwestern Fujian province (China). The results showed that (1) Both BEMD and BEEMD were useful for decomposing non-linear and non-stationary geochemical data into a set of simple Bi-intrinsic functions (BIMFs), however, there is a drawback of 2D mode mixing in BEMD, and the serious mode mixing can lead to incorrect geological interpretation; (2) BEEMD can give more robust and reliable results than BEMD, making the data easier to interpret; and (3) the BIMFs and the residue obtained by BEEMD can be combined into new components, which can represent geochemical anomalies, unwanted noise and the background of the study area.

A pixel-level entropy-weighted image fusion algorithm based on bidimensional ensemble empirical mode decomposition

The bidimensional empirical mode decomposition algorithm is more suitable to handle image fusion than the traditional multi-scale decomposition methods in the image fusion area. Nevertheless, there...

Analysis of fMRI images with bi-dimensional empirical mode decomposition based-on Green's functions

Abstract We present a new method for decomposing two-dimensional data arrays with empirical mode decomposition (EMD). It performs envelope surface interpolation based on Green's functions in tension (GiT) to extract bi-dimensional intrinsic mode functions (BIMFs). The new method is called GiT-BEMD and outperforms existing bi-dimensional ensemble EMD (BEEMD) variants in terms of computational costs and quality of extracted intrinsic modes. More specifically, it is easy to implement, much faster than BEEMD, very robust and free from processing artifacts. GiT-BEMD is applied to fMRI data recorded during a contour integration task. Features extracted from resulting volume intrinsic mode functions (VIMFs) achieve higher classification accuracy compared to the canonical BEEMD. The new method thus provides a valuable alternative to existing mode decomposition methods for analyzing images.

Distance regularized level set evolution in magnetic resonance image segmention based on bi-dimensional ensemble empirical mode decomposition

Original image is directly processed by the existing image segmentation algorithms, which is easily affected by noise. A bi-dimensional ensemble empirical mode decomposition (BEEMD) method is introduced to improve the accuracy of MR image segmentation by distance regularized level set (DRLSE) method. The BEEMD method is the extension of one-dimensional noise assisted data analysis from ensemble empirical mode decomposition (EEMD). The key points of BEEMD are as follows. four-neighborhood optimization is used to find extermum; three-spline interpolation is used to obtain the envelope; amplitude standard of added white noise is restricted; a certain time of integration is used to avoid modality aliasing problem. The main steps of the proposed method are as follows. Firstly, the MR image is decomposed into a number of two-dimensional intrinsic mode functions (BIMF) by BEEMD method; different weighting coefficients are endued to BIMF for image reconstruction to enhance the segmentation target. Secondly, part of BIMF components are added into edge indicator function of DRLSE to recover the blurring boundary caused by Gauss smooth operation. Then DRLSE is used to segment the reconstructed MR image. High accuracy and robustness of proposed algorithm are obtained in both simulations and clinical MR images. However, compared with DRLSE, the proposed method is complex and time consuming because using BEEMD for preprocessing the segmentation image.

Nano-level position resolution for particle tracking in digital in-line holographic microscopy.

Three-dimensional particle tracking in biological systems is a quickly growing field, many techniques have been developed providing tracking characters. Digital in-line holographic microscopy is a valuable technique for particle tracking. However, the speckle noise, out-of-focus signals and twin image influenced the particle tracking. Here an adaptive noise reduction method based on bidimensional ensemble empirical mode decomposition is introduced into digital in-line holographic microscopy. It can eliminate the speckle noise and background of the hologram adaptively. Combined with the three-dimensional deconvolution approach in the reconstruction, the particle feature would be identified effectively. Tracking the fixed beads on the cover-glass with piezoelectric stage through multiple holographic images demonstrate the tracking resolution, which approaches 2 nm in axial direction and 1 nm in transverse direction. This would facilitate the development and use in the biological area such as living cells and single-molecule approaches.

Bidimensional ensemble empirical mode decomposition of functional biomedical images taken during a contour integration task

In cognitive neuroscience, extracting characteristic textures and features from functional imaging modalities which could be useful in identifying particular cognitive states across different conditions is still an important field of study. This paper explores the potential of two-dimensional ensemble empirical mode decomposition (2DEEMD) to extract such textures, so-called bidimensional intrinsic mode functions (BIMFs), of functional biomedical images, especially functional magnetic resonance images (fMRI) taken while performing a contour integration task. To identify most informative textures, i.e. BIMFs, a support vector machine (SVM) as well as a random forest (RF) classifier is trained for two different stimulus/response conditions. Classification performance is used to estimate the discriminative power of extracted BIMFs. The latter are then analyzed according to their spatial distribution of brain activations related with contour integration. Results distinctly show the participation of frontal brain areas in contour integration. Employing features generated from textures represented by BIMFs exhibit superior classification performance when compared with a canonical general linear model (GLM) analysis employing statistical parametric mapping (SPM).

A denoising scheme for DSPI fringes based on fast bi-dimensional ensemble empirical mode decomposition and BIMF energy estimation

Digital speckle pattern interferometry (DSPI) is a new and efficient technique for measuring the difference in out-of-plane displacement. However, DSPI fringes contain low spatial information degraded with random speckle noise and background intensity. A denoising scheme based on fast bi-dimensional ensemble empirical mode decomposition (FBEEMD) and energy estimation of bi-dimensional intrinsic mode function (BIMF) is proposed to reduce speckle noise in this paper. Furthermore, the denoising scheme is compared with other denoising methods, and evaluated quantitatively using computer-simulated and experimental DSPI fringes.

Using Peano–Hilbert space filling curves for fast bidimensional ensemble EMD realization

Empirical mode decomposition (EMD) is a fully unsupervised and data-driven approach to the class of nonlinear and non-stationary signals. A new approach is proposed, namely PHEEMD, to image analysis by using Peano–Hilbert space filling curves to transform 2D data (image) into 1D data, followed by ensemble EMD (EEMD) analysis, i.e., a more robust realization of EMD based on white noise excitation. Tests’ results have shown that PHEEMD exhibits a substantially reduced computational cost compared to other 2D-EMD approaches, preserving, simultaneously, the information lying at the EMD domain; hence, new perspectives for its use in low computational power devices, like portable applications, are feasible.

Capsule endoscopy image analysis using texture information from various colour models

Wireless capsule endoscopy (WCE) is a novel imaging technique that is gradually gaining ground as it enables the non-invasive and efficacious visualization of the digestive track, and especially the entire small bowel including its middle part. However, the task of reviewing the vast amount of images produced by a WCE examination is a burden for the physicians. To tackle this major drawback, an innovative scheme for discriminating endoscopic images related to one of the most common intestinal diseases, ulceration, is presented here. This new approach focuses on colour–texture features in order to investigate how the structure information of healthy and abnormal tissue is distributed on RGB, HSV and CIE Lab colour spaces. The WCE images are pre-processed using bidimensional ensemble empirical mode decomposition so as to facilitate differential lacunarity analysis to extract the texture patterns of normal and ulcerous regions. Experimental results demonstrated promising classification performance (mean accuracy>95%), exhibiting a high potential towards automatic WCE image analysis.

Adaptive noise reduction method for DSPI fringes based on bi-dimensional ensemble empirical mode decomposition

Digital speckle pattern interferometry (DSPI) fringes contain low spatial information degraded with speckle noise and background intensity. The denoising technique proposed recently based on bi-dimensional empirical mode decomposition (BEMD) could implement noise reduction adaptively. However, the major drawback of BEMD, called mode mixing, has affected its practical application. With noise-assisted data analysis (NADA) method, bi-dimensional ensemble empirical mode decomposition (BEEMD) was proposed, which has solved the problem of mode mixing. The denoising approach based on BEEMD will be presented, compared with other classic denoising methods and evaluated both qualitatively and quantitatively using computer-simulated and experimental DSPI fringes.