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

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  • Lahmiri, Salim

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.

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  • Lahmiri, Salim, 2016. "Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 235-243.
    • Handle: RePEc:eee:phsmap:v:456:y:2016:i:c:p:235-243
    DOI: 10.1016/j.physa.2016.03.046
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    Cited by:

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    2. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2020. "Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine," Resources Policy, Elsevier, vol. 69(C).
    3. Zhang, Hong-Yan & Kang, Ming-Cui & Li, Jing-Qiang & Liu, Hai-Tao, 2017. "R/S analysis of reaction time in Neuron Type Test for human activity in civil aviation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 859-870.
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    5. Lahmiri, Salim, 2017. "Parkinson’s disease detection based on dysphonia measurements," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 98-105.
    6. Salim Lahmiri, 2016. "Features selection, data mining and finacial risk classification: a comparative study," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(4), pages 265-275, October.

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