Bidimensional Intrinsic Mode Functions Research Articles

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).

Medical image registration based on fast and adaptive bidimensional empirical mode decomposition

Image registration plays a crucial role in several areas, yet iconic registration methods are more efficient than those in geometrical registration, but they require great execution time. Regarding reduction in the execution time of iconic registration, the authors have proposed a new method based on mutual information while exploiting adaptive multiresolution decomposition, bidimensional empirical mode decomposition (BEMD) in its fast and adaptive version fast and adaptive BEMD (FABEMD). The idea is that instead of registering two images, the authors proceed to registration of the bidimensional intrinsic mode functions (BIMFs) that results from the FABEMD decomposition. The BIMF selected by the authors' algorithm is characterised by preservation of the general form of the image, and it contains a tone of grey levels lower than that of the original image, thus the number of combinations of the grey levels, used while calculating entropy is reduced, which in turn reduces execution time of the registration.

Multivariate Gray Model-Based BEMD for Hyperspectral Image Classification

Bidimensional empirical mode decomposition (BEMD) has been one of the core activities in image processing due to its fully data-driven and self-adaptive nature. Unfortunately, this promising technique is sensitive to boundary effect. In this paper, a new method inspired by the multivariate gray model (MGM), namely ${\rm GM}(1, N)$ , is developed for boundary extension of the BEMD. Specifically, our contribution is threefold. First, focusing on evaluating the model coefficients and convolution integral, which are key elements in reducing the prediction error of the ${\rm GM}(1, N)$ , we replace the existing (composite) trapezoidal rule with (composite) Simpson rule and deduce an alternative MGM, termed as ${\rm S\hbox{-}GM}(1,N)$ . Second, the given image is extended by the GM(1, 3) or S-GM(1, 3) $(N=3)$ , whose characteristic data series and relative data series are, respectively, derived from the pixel values and coordinates of the image. Consequently, the extended image is decomposed into several bidimensional intrinsic mode functions (BIMFs) and a residue whose corresponding parts are extracted as the decomposition results of the original image. Finally, the proposed boundary effect mitigation methods are applied in the hyperspectral image classification. In greater detail, the BIMFs obtained by various BEMD methods are taken as features of the hyperspectral dataset whereas the widely used $k$ -nearest neighbors ( $k$ -NN) as well as the support vector machine, whose optimal parameters are selected by the genetic algorithm, are adopted as classifiers. Extensive experiments and comparisons with other generally acknowledged methods confirm that the proposed methods achieve promising improvement in the classification performance.

Engineering Surface Analysis by Bidimensional Empirical Mode Decomposition

It is well known that an engineering surface is composed of a large number of wavelengths of roughness that are superimposed on each other. Because these multi-scale features are related to different aspects of the processes the surface has undergone and closely related to the friction and wear properties of a surface, the analysis and characterization of these features becomes an important aspect of manufacture. The challenge is how to use them for acquiring knowledge and for aid to analysis. In this paper, a method for surface topography analysis is proposed based on bidimensional empirical mode decomposition (BEMD), which can provide good adaptive separation of surface texture into multiple hierarchical components known as bidimensional intrinsic mode functions (BIMFs). Applications are conducted by using a simulated surfaces to demonstrate the feasibility and applicability of using the bidimensional empirical mode decomposition method in the analysis of engineering surfaces.

Texture Classification based on Bidimensional Empirical Mode Decomposition and Local Binary Pattern

This paper presents a new simple and robust texture analysis feature based on Bidimensional Empirical Mode Decomposition (BEMD) and Local Binary Pattern (LBP). BEMD is a locally adaptive decomposition method and suitable for the analysis of nonlinear or nonstationary signals. Texture images are decomposed to several Bidimensional Intrinsic Mode Functions (BIMFs) by BEMD, which present a new set multi-scale compo- nents of images. In our approach, firstly, saddle points are added as supporting points for interpolation to improve original BEMD, and then images are decomposed by the new BEMD to several components (BIMFs). After then, Local Binary Pattern (LBP) in different sizes is used to detect features from different BIMFs. At last, normalization and BIMFs selection method are adopted for features selection. The proposed feature presents invariant while preserving LBP's simplicity. Our method has also been evaluated in CuRet and KTH-TIPS2a texture image databases. It is experimentally demonstrated that the proposed feature achieves higher classification accuracy than other state-of-the- art texture representation methods, especially in small training samples condition.

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.

Adaptive enhancement of optical fringe patterns by selective reconstruction using FABEMD algorithm and Hilbert spiral transform

Presented method for fringe pattern enhancement has been designed for processing and analyzing low quality fringe patterns. It uses a modified fast and adaptive bidimensional empirical mode decomposition (FABEMD) for the extraction of bidimensional intrinsic mode functions (BIMFs) from an interferogram. Fringe pattern is then selectively reconstructed (SR) taking the regions of selected BIMFs with high modulation values only. Amplitude demodulation and normalization of the reconstructed image is conducted using the spiral phase Hilbert transform (HS). It has been tested using computer generated interferograms and real data. The performance of the presented SR-FABEMD-HS method is compared with other normalization techniques. Its superiority, potential and robustness to high fringe density variations and the presence of noise, modulation and background illumination defects in analyzed fringe patterns has been corroborated.

Color image trend adjustment using a color bidimensional empirical mode decomposition method

Bidimensional empirical mode decomposition (BEMD) technique decomposes an image into several bidimensional intrinsic mode functions (BIMFs) and a bidimensional residue (BR). Classical BEMD methods require some form of surface interpolation to estimate envelope surfaces, which causes various problems. On the other hand, existing surface interpolation-based BEMD methods are proposed and/or are suitable for gray-scale images only. This paper presents a novel BEMD approach for color images known as color BEMD (CBEMD), which employs order-statistics filter (OSF)-based envelope estimation to avoid some of the difficulties, otherwise encountered in classical BEMD approaches. The CBEMD can decompose a color image into several color BIMFs and a color BR based on hierarchical local spatial variation of image intensity and color. Since the color BR represents the trend of a color image in terms of global intensity and color distribution, it is utilized for trend adjustment of color images. Both formulation of the CBEMD and finding and adjusting the color image trend are known to be the first approaches in corresponding issues. Experimental results with several real images demonstrate the potential of the proposed CBEMD method for color image processing, which include trend adjustment of color images.

Motion estimation using the fast and adaptive bidimensional empirical mode decomposition

Motion estimation is a basic step that can be used to serve several processes in computer vision. This motion is currently approximated by the visual displacement field called optical flow. Currently, several methods are used to estimate it, but a good compromise between computational cost and accuracy is hard to achieve. This paper tackles the problem by proposing a new technique based on the FABEMD (fast and adaptive bidimensional empirical mode decomposition) with the aim of improving the well-known pyramidal algorithm of Lucas and Kanade (LK) which, in principle, utilizes two consecutive frames extracted from video sequence to determine a dense optical flow. The proposed algorithm uses the FABEMD method to decompose each of the two considered frames into several BIMFs (bidimensional intrinsic mode functions) that are matched in number and proprieties. Thus, to compute the optical flow, the LK algorithm is applied to each of the two matching BIMFs which belong to the same mode of the decomposition. Although the implementation does not use an iterative refinement, the results show that the proposed approach is less sensitive to noise and provides improved motion estimation with a reduction of computing time compared to iterative methods.

Extracting magnetic anomalies based on an improved BEMD method: A case study in the Pangxidong Area, South China

In bidimensional empirical mode decomposition, an appropriate stoppage criterion for the sifting process is important. To solve the problem of unstable convergence, a stepwise stop criterion was presented based on the Cauchy-type criterion. In one sifting process, a squared deviation of two successive residual components is first calculated. Then, an absolute value of the difference between two continuous deviations is calculated. The sifting process will stop when the difference value is less than the a priori value. A comparison of correlation coefficients of the final results decomposed from experimental models demonstrated that those intrinsic mode functions obtained by the stepwise stop criterion meet the requirements of orthogonality, and the mode mixing effect among them is depressed effectively. With the help of the proposed criterion, an example is given for extracting magnetic anomalies in the Pangxidong area, Guangdong Province, South China. The residual components of the first and second order of bidimensional intrinsic mode functions revealed a spatial relationship between ore deposits and anomalies.

A Novel Method of Medical Image Fusion Based on Bidimensional Empirical Mode Decomposition

In this paper, a novel method of medical image fusion based on bidimensional empirical mode decomposition (BEMD) is proposed with the aim to improve the quality of fused medical images. BEMD is first used in this paper to decompose medical images into bidimensional intrinsic mode functions (BIMFs) and residue. In this paper m-BIMFs is proposed for the first time, which has larger scale and better feature than BIMFs. Finally, m-PCNN(Pulse Coupled Neural Network), an improved PCNN mode, is used to fuse corresponding m-BIMFs. The experimental results demonstrate that our method is successfully applied to medical image fusion and better than traditional wavelet fusion , pyramid fusion and unimproved m-PCNN fusion.

Multiresolution Information Mining for Pavement Crack Image Analysis

Empirical mode decomposition (EMD) is a multiresolution data analysis method recently developed to cater to the inherent nonstationarity in real-world signals. A two-dimensional (2D) extension of EMD is used in this paper as a pavement distress image analytical tool. The algorithm decomposes an image into a set of narrow band components (called bidimensional intrinsic mode function, or BIMF) that uniquely reflect the variations in the image. Although some components could have good image edge characteristics, others might hold fidelity to the shape and size of objects or trends in the image. Therefore, the complete spatial and frequency characteristic of a desired image feature might also be divided into the different components, indicating that all attributes of a desired feature might not be found in a single component. An optimal solution requires image mining from the different component resolutions to accurately extract those specific attributes without compromising certain spatial and frequency characteristics. Two major contributions to pavement image analysis are achieved. First, the paper explores pavement image denoising or enhancement by combining the EMD and a weighted reconstruction technique as a tool for background standardization of images acquired under different types of illumination effects. Second, using principal component pursuit (PCP), the authors reconstruct a composite image by selecting salient information from coarse and fine resolution BIMFs useful for accurate extraction of linear patterns in a pavement distress image. Compared with conventional image reconstruction or approximation techniques, the methodology used in this paper yields better results. DOI:10.1061/(ASCE)CP.1943-5487.0000178. (C) 2012 American Society of Civil Engineers.

A NEW BIDIMENSIONAL EMPIRICAL MODE DECOMPOSITION BY USING RADON TRANSFORM

Based on a detailed analysis for the one-dimensional intrinsic mode function, a model of the bidimensional intrinsic mode function is found firstly. Then a new bidimensional empirical mode decomposition algorithm is developed by using the Radon transform. The existing methods have to locate the maxima and minima in a bidimensional function, and further, produce bidimensional envelopes. Those are usually hard for a real two-dimensional function. The proposed algorithm successfully avoids these difficulties. Experiments show encouraging results.

A fusion algorithm for remote sensing images based on nonsub-sampled pyramids and bidimensional empirical decomposition

In order to improve the quality of remote sensing image fusion, a new method combining nonsubsampled Laplacian pyramid (NLP) and bidimensional empirical mode decomposition (BEMD) is proposed. First, the high resolution panchromatic image (PAN) is decomposed using NLP until the approximate component and the low resolution multispectral image (MS) contain features with a similar scale. Then, the approximation component and the MS are decomposed by BEMD, resulting in a number of bidimensional intrinsic mode functions (BIMF) and a residue respectively. The instantaneous frequency is computed in 4 directions of the BIMFs. Considering the positive or negative coefficients in the corresponding position, a weighted algorithm is designed for fusing the high frequency details using the instantaneous frequency and the coefficient absolute value of the BIMFs as fusion feature. The fused image is then obtained through inverse BEMD and NLP. Experimental results have illustrated the advantage of this method over the IHS, DWT and a-Trous wavelet in both spectral and spatial detail qualities.

A NOVEL APPROACH OF EDGE DETECTION VIA A FAST AND ADAPTIVE BIDIMENSIONAL EMPIRICAL MODE DECOMPOSITION METHOD

A novel approach of edge detection is proposed that utilizes a bidimensional empirical mode decomposition (BEMD) method as the primary tool. For this purpose, a recently developed fast and adaptive BEMD (FABEMD) is used to decompose the given image into several bidimensional intrinsic mode functions (BIMFs). In FABEMD, order statistics filters (OSFs) are employed to get the upper and lower envelopes in the decomposition process, instead of surface interpolation, which enables fast decomposition and well-characterized BIMFs. Binarization and morphological operations are applied to the first BIMF obtained from FABEMD to achieve the desired edges. The proposed approach is compared with several other edge detection methodologies, which include a combination of classical BEMD and morphological processing, the Canny and Sobel edge detectors, as well as combinations of BEMD/FABEMD and Canny/Sobel edge detectors. Simulation results with real images demonstrate the efficacy and potential of the proposed edge detection algorithm employing FABEMD.

Medical Image Fusion Algorithm Based on Bidimensional Empirical Mode Decomposition

提出了一种自适应的二维经验模态分解(bidimensional empirical mode decomposition,简称BEMD)医学图像融合算法.待融合的医学图像经过BEMD分解成二维的内蕴模函数(bidimensional intrinsic mode function,简称BIMF)和趋势图像.BIMF图像经过Hilbert-Huang变换提取图像特征,然后,图像分解的各部分数据在区域融合规则下形成综合BEMD表示.最后,综合BEMD表示进行BEMD逆变换得到融合后的医学图像.BEMD分解方法是一种完全自适应的数据分解表达形式,具有比Fourier变化和小波分解更好的特性.该医学图像融合算法不需要预先定义滤波器或小波函数.实验结果表明,该算法与传统融合算法相比性能优越,能够大幅度提高融合图像的质量.;An adaptive medical image fusion algorithm based on the representation of bidimensional empirical mode decomposition (BEMD) is proposed. Source medical images are decomposed into a number of bidimensional intrinsic mode functions (BIMF) as well as a residual image. Image features are extracted through Hilbert-Huang transform on the BIMF. Then the composite BEMD is formed by region-based fusion rules on data representations of BEMD. Finally, the fused image is obtained by inverse BEMD on the composite representation. The BEMD is an adaptive data decomposition representation, and has better performance than Fourier and wavelet transform. The proposed algorithm does not need predetermined filters or wavelet functions. Experimental results show that the proposed algorithm provides superior performance over conventional fusion algorithms in improving the quality of fused images.

Fast and Adaptive Bidimensional Empirical Mode Decomposition Using Order-Statistics Filter Based Envelope Estimation

A novel approach for bidimensional empirical mode decomposition (BEMD) is proposed in this paper. BEMD decomposes an image into multiple hierarchical components known as bidimensional intrinsic mode functions (BIMFs). In each iteration of the process, two-dimensional (2D) interpolation is applied to a set of local maxima (minima) points to form the upper (lower) envelope. But, 2D scattered data interpolation methods cause huge computation time and other artifacts in the decomposition. This paper suggests a simple, but effective, method of envelope estimation that replaces the surface interpolation. In this method, order statistics filters are used to get the upper and lower envelopes, where filter size is derived from the data. Based on the properties of the proposed approach, it is considered as fast and adaptive BEMD (FABEMD). Simulation results demonstrate that FABEMD is not only faster and adaptive, but also outperforms the original BEMD in terms of the quality of the BIMFs.