3D extension of the fast and adaptive bidimensional empirical mode decomposition

Riffi, Jamal; Mahraz, Mohamed Adnane; Abbad, Abdelghafour; Tairi, Hamid

doi:10.1007/s11045-014-0283-6

Cited by 18 publications

(26 citation statements)

References 17 publications

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“…A fast, time efficient, and effective method is essential for processing real images that have a large size. Previous EMD-based methods were limited to small size images as the extrema detection, interpolation at each iteration, and the large number of iterations make their processing time consuming and complicated [19,44,45,46]. Therefore, those methods were just applicable to reduced size images, which resulted in losing some information during their process.…”

Section: Estimation Of the Spatial Imfs (Simfs)mentioning

confidence: 99%

“…Therefore, those methods were just applicable to reduced size images, which resulted in losing some information during their process. Fast and Adaptive Tridimensional (3D) EMD, abbreviated as FATEMD, is a recent extension of the EMD method to three dimensions [19]. The FATEMD method is able to estimate volume components called tridimensional Intrinsic Mode Functions (3D-IMFs) quickly and accurately by limiting the number of iterations per 3D-IMF to one, and changing the process of computing upper and lower envelopes, which reduce the computation time for each iteration [19,44,46].…”

Section: Estimation Of the Spatial Imfs (Simfs)mentioning

confidence: 99%

“…Fast and Adaptive Tridimensional (3D) EMD, abbreviated as FATEMD, is a recent extension of the EMD method to three dimensions [19]. The FATEMD method is able to estimate volume components called tridimensional Intrinsic Mode Functions (3D-IMFs) quickly and accurately by limiting the number of iterations per 3D-IMF to one, and changing the process of computing upper and lower envelopes, which reduce the computation time for each iteration [19,44,46]. In the FATEMD method, the steps of extracting 3D-IMFs are almost the same as the previous EMD based methods, except for the number of iterations and the estimations of the maximum and minimum envelopes.…”

Section: Estimation Of the Spatial Imfs (Simfs)mentioning

confidence: 99%

“…Here, we define an adaptive global signal regression (AGSR), which is voxel-specific, by performing a spatiotemporal decomposition of the fMRI time series. The Spatial and Temporal Intrinsic Mode Functions (SIMF and TIMF, respectively) of fMRI data are acquired by applying FATEMD [19] and ICEEMDAN [20] methods, respectively. These methods are based on the empirical mode decomposition (EMD) method [21,22], and will be used to decompose the fMRI signals adaptatively and in a voxel-specific way.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal empirical mode decomposition of resting-state fMRI signals: application to global signal regression

Moradi

Dousty

Sotero

2019

Preprint

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Resting-state functional connectivity MRI (rs-fcMRI) is a common method for mapping functional brain networks. However, estimation of these networks is affected by the presence of a common global systemic noise, or global signal (GS). Previous studies have shown that the common preprocessing steps of removing the GS may create spurious correlations between brain regions. In this paper, we decompose fMRI signals into 5 spatial and 3 temporal intrinsic mode functions (SIMF and TIMF, respectively) by means of the empirical mode decomposition (EMD), which is an adaptive data-driven method widely used to analyze nonlinear and nonstationary phenomena. For each SIMF, brain connectivity matrices were computed by means of the Pearson correlation between TIMFs of different brain areas. Thus, instead of a single connectivity matrix, we obtained 5 × 3 = 15 functional connectivity matrices. Given the high value obtained for large-scale topological measures such as transitivity, in the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, the spatiotemporal EMD of fMRI signals automatically regressed out the GS, although, interestingly, the removed noisy component was voxel-specific. We compared the performance of our method with the conventional GS regression and to the results when the GS was not removed. While the correlation pattern identified by the other methods suffers from a low level of precision, our approach demonstrated a high level of accuracy in extracting the correct correlation between different brain regions.

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Section: Estimation Of the Spatial Imfs (Simfs)mentioning

confidence: 99%

Section: Estimation Of the Spatial Imfs (Simfs)mentioning

confidence: 99%

Section: Estimation Of the Spatial Imfs (Simfs)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatiotemporal empirical mode decomposition of resting-state fMRI signals: application to global signal regression

Moradi

Dousty

Sotero

2019

Preprint

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“…4 (l) Concerning the FABEMD approach, we tend to select the second or the third BIMF as the region for embedding the watermark. This is motivated by the fact that they correspond to the middle frequency components of an image [1,2,21]. And it has been demonstrated that, if the watermark is embedded in the lower frequency bands, the scheme would be robust to attacks but the watermark may be difficult to hide.…”

Section: Proposed Algorithmsmentioning

confidence: 99%

The efficiency of PDE decomposition in images watermarking

Aherrahrou

Tairi

2015

Multimed Tools Appl

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The Discrete Cosine Transform (DCT) based watermarking scheme is a common and popular technique that has been used for long time for image watermarking. In this work, we aimed to further improve the commonly used DCT based watermarking method by combining the DCT with the PDE (Partial Differential Equation) method or with the FABEMD (Fast and Adaptive Bidimensional Empirical Mode Decomposition). Our experimental results and comparison analysis for the two proposed FABEMD-DCT and PDE-DCT methods demonstrated a better performance in terms of invisibility and the robustness of the watermark compared to some DCT based watermarking schemes. Furthermore, our method based on DCT and PDE model shows highest performance compared to other methods.

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Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification

Liu

2016

ISPRS Journal of Photogrammetry and Remote Sensing

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3D extension of the fast and adaptive bidimensional empirical mode decomposition

Cited by 18 publications

References 17 publications

Spatiotemporal empirical mode decomposition of resting-state fMRI signals: application to global signal regression

Spatiotemporal empirical mode decomposition of resting-state fMRI signals: application to global signal regression

The efficiency of PDE decomposition in images watermarking

Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification

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