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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한의용생체공학회 Biomedical Engineering Letters (BMEL) Biomedical Engineering Letters (BMEL) Vol.5 No.4
- 발행연도
- 2015.1
- 수록면
- 311 - 320 (10page)
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Purpose Automated classification of brain magnetic resonance(MR) images has been an extensively researched topic inbiomedical image processing. In this work, we propose anew approach for classifying normal and abnormal brain MRimages using bi-dimensional empirical mode decomposition(BEMD) and autoregressive (AR) model. Methods In our approach, brain MR image is decomposedinto four intrinsic mode functions (IMFs) using BEMD andAR coefficients from multiple IMFs are concatenated toform a feature vector. Finally a binary classifier, least-squaressupport vector machine (LS-SVM), is employed to discriminatebetween normal and abnormal brain MR images. Results The proposed technique achieves 100% classificationaccuracy using second-order AR model with linear andradial basis function (RBF) as kernels in LS-SVM. Conclusions Experimental results confirm that the performanceof the proposed method is quite comparable with the existingresults. Specifically, the presented approach outperformsone-dimensional empirical mode decomposition (1D-EMD)based classification of brain MR images.
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참고문헌 (61)
참고문헌 신청
Kumar EP / 2013 / Abnormality detection in brain MRI/CT using segmentation algorithm and 3D visualization / Conf Proc Int Conf Adv Comput 1:56~62
Nandpuru HB / 2014 / MRI brain cancer classification using support vector machine / Conf Proc IEEE Std Conf Electr Electron Comput Sci 1:1~6
Lahmiri S / 2013 / Automatic detection of Alzheimer disease in brain magnetic resonance images using fractal features / Conf Proc IEEE/EMBS Neural Eng 1:1505~1508
Blonda P / 1998 / Segmentation of multiple sclerosis lesions in MRI by fuzzy neural networks:FLVQ and FOSART / Conf Proc Conf North American Fuzzy Information Process Soc 1:39~43
Chaplot S / 2006 / Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network / Biomed Signal Process Control 1(1):86~92
Nandpuru HB / 2014 / MRI brain cancer classification using support vector machine / Conf Proc IEEE Std Conf Electr Electron Comput Sci 1:1~6
Lahmiri S / 2013 / Automatic detection of Alzheimer disease in brain magnetic resonance images using fractal features / Conf Proc IEEE/EMBS Neural Eng 1:1505~1508
Blonda P / 1998 / Segmentation of multiple sclerosis lesions in MRI by fuzzy neural networks:FLVQ and FOSART / Conf Proc Conf North American Fuzzy Information Process Soc 1:39~43
Chaplot S / 2006 / Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network / Biomed Signal Process Control 1(1):86~92
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