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Classification of magnetic resonance brain images using bi-dimensional empirical mode decomposition and autoregressive model

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Abstract

Purpose

Automated classification of brain magnetic resonance (MR) images has been an extensively researched topic in biomedical image processing. In this work, we propose a new approach for classifying normal and abnormal brain MR images using bi-dimensional empirical mode decomposition (BEMD) and autoregressive (AR) model

Methods

In our approach, brain MR image is decomposed into four intrinsic mode functions (IMFs) using BEMD and AR coefficients from multiple IMFs are concatenated to form a feature vector. Finally a binary classifier, least-squares support vector machine (LS-SVM), is employed to discriminate between normal and abnormal brain MR images.

Results

The proposed technique achieves 100% classification accuracy using second-order AR model with linear and radial basis function (RBF) as kernels in LS-SVM.

Conclusions

Experimental results confirm that the performance of the proposed method is quite comparable with the existing results. Specifically, the presented approach outperforms one-dimensional empirical mode decomposition (1D-EMD) based classification of brain MR images.

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Authors and Affiliations

  1. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, 452017, India

    Omkishor Sahu, Vijay Anand, Vivek Kanhangad & Ram Bilas Pachori

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  1. Omkishor Sahu
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  2. Vijay Anand
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  3. Vivek Kanhangad
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  4. Ram Bilas Pachori
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Correspondence to Vivek Kanhangad.

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Sahu, O., Anand, V., Kanhangad, V. et al. Classification of magnetic resonance brain images using bi-dimensional empirical mode decomposition and autoregressive model. Biomed. Eng. Lett. 5, 311–320 (2015). https://doi.org/10.1007/s13534-015-0208-9

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  • DOI: https://doi.org/10.1007/s13534-015-0208-9

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