Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance

Salim Lahmiri

Physica A: Statistical Mechanics and its Applications, 2016, vol. 456, issue C, 235-243

Abstract: The main purpose of this work is to explore the usefulness of fractal descriptors estimated in multi-resolution domains to characterize biomedical digital image texture. In this regard, three multi-resolution techniques are considered: the well-known discrete wavelet transform (DWT) and the empirical mode decomposition (EMD), and; the newly introduced; variational mode decomposition mode (VMD). The original image is decomposed by the DWT, EMD, and VMD into different scales. Then, Fourier spectrum based fractal descriptors is estimated at specific scales and directions to characterize the image. The support vector machine (SVM) was used to perform supervised classification. The empirical study was applied to the problem of distinguishing between normal and abnormal brain magnetic resonance images (MRI) affected with Alzheimer disease (AD). Our results demonstrate that fractal descriptors estimated in VMD domain outperform those estimated in DWT and EMD domains; and also those directly estimated from the original image.

Keywords: Brain magnetic resonance image; Discrete wavelet transform; Empirical mode decomposition; Variational mode decomposition; Fractal descriptors; Support vector machine (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:456:y:2016:i:c:p:235-243

DOI: 10.1016/j.physa.2016.03.046

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