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

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

doi:10.1049/iet-ipr.2012.0034

Cited by 21 publications

(14 citation statements)

References 16 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 (Bhuiyan et al, 2008; Riffi et al, 2013, 2014; He et al, 2017). Therefore, those methods were just applicable to reduced size images, which resulted in losing some information during their process.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Empirical Mode Decomposition of Resting-State fMRI Signals: Application to Global Signal Regression

2019

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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 non-linear and non-stationary phenomena. For each SIMF, functional connectivity matrices were computed by means of 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 correlation and global efficiency values of the connectivity matrices related to the low spatial maps (SIMF3, SIMF4, and SIMF5), our results suggest that these maps can be considered as spatial global signal masks. Thus, by summing up the first two SIMFs extracted from the fMRI signals, we have automatically excluded the GS which is now 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 in identifying the correct brain network connectivity, our approach demonstrated expected connectivity patterns for the default mode network and task-positive network.

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Section: Methodsmentioning

confidence: 99%

Spatiotemporal Empirical Mode Decomposition of Resting-State fMRI Signals: Application to Global Signal Regression

2019

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“…The second exploits all the raw information contained in the image (without prior segmentation), namely the gray levels. We are talking about iconic methods [32]. 3.…”

Section: Classification Of Registration Methodsmentioning

confidence: 99%

A Case Analysis on Different Registration Methods on Multi-modal Brain Images

Nathawat¹,

Mandot²,

Sharma³

2018

IJMECS

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Abstract-Many applications of artificial vision need to compare or integrate images of the same object but obtained at different moments of time with different devices (cameras), from different positions, under different conditions, etc. These differences in capture give rise to images with important relative geometric differences that prevent these "Fit" with precision over each other.The registry eliminates these geometric differences so that located pixels in the same coordinates correspond to the same point of the object and, therefore, both images can easily be compared or integrated. The registration of images is essential in disciplines such as remote sensing, radiology, robotic vision, etc. ; Fields, all of them, that overlap images to study environmental phenomena, monitor tumours carcinogenic or to reconstruct the observed scene. This paper also study different measures of similarity used to measure their consistency and a novel procedure is proposed to improve the accuracy of the linear record by pieces. Specifically the elements that influence the estimation are analysed experimentally of probability distributions of the intensity levels of the images. These distributions are the basis for calculating measures of similarity based on entropy as mutual information (MI) or the Entropy correlation coefficient (ECC). Therefore, the effectiveness of these measures depends critically on their correct estimation.

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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%

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

Cited by 21 publications

References 16 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

A Case Analysis on Different Registration Methods on Multi-modal Brain Images

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

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