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KMID : 1144120150050040311
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Biomedical Engineering Letters
2015 Volume.5 No. 4 p.311 ~ p.320
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Classification of magnetic resonance brain images using bi-dimensional empirical mode decomposition and autoregressive model
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Sahu Omkishor
Anand Vijay
Kanhangad Vivek
Pachori Ram Bilas
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Abstract
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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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KEYWORD
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Magnetic resonance imaging (MRI), Bi-dimensional empirical mode decomposition (BEMD), Intrinsic mode function (IMF), Autoregressive (AR) model
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