Analysis of fMRI images with bi-dimensional empirical mode decomposition based-on Green's functions. (September 2016)

• Record Type: Journal Article
• Authors: Al-Baddai, Saad; Al-Subari, Karema; Tomé, Ana Maria; Ludwig, Bernd; Salas-Gonzales, Diego; Lang, Elmar Wolfgang
• Is Part Of: Biomedical signal processing and control. Volume 30(2016)
• Page Start: 53
• Page End: 63
• Publication Date: 2016-09
• Subjects: Empirical mode decomposition -- Green's function -- FMRI -- SVM -- VIMFs; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.06.019
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Graphical abstract: Abstract : Highlights: A promising technique to analyze fMRI images is presented. The results are significantly improved comparing to previous studies. The proposed approach GiT-BEMD is much more stable than the canonical BEMD. The classification rates are considerably enhanced by employing the extracted textures, generated by GiT-BEMD, combined with an SVM classifier. 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.