applsci-06-00169.pdf (3.49 MB)
Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection
journal contribution
posted on 2019-08-29, 14:30 authored by S Wang, S Lu, Z Dong, J Yang, M Yang, Y ZhangAbstract: (Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step
for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to
develop an automatic classification system of brain images in magnetic resonance imaging (MRI).
(Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along
axial plane with size of 256 ˆ 256. We utilized an s-level decomposition on the basis of dual-tree
complex wavelet transform (DTCWT), in order to obtain 12s “variance and entropy (VE)” features
from each subband. Afterwards, we used support vector machine (SVM) and its two variants:
the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers.
In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and
DTCWT + VE + TSVM. (Results) The results showed that our “DTCWT + VE + TSVM” obtained an
average accuracy of 99.57%, which was not only better than the two other proposed methods, but also
superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification
accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further,
we used 100 slices from real subjects, and we found our proposed method was superior to human
reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible.
Funding
Natural Science Foundation of Jiangsu Province (BK20150983), Open Fund of Key laboratory of symbolic computation and knowledge engineering of ministry of education, Jilin University (93K172016K17), Open Fund of Key Laboratory of Statistical information technology and data mining, State Statistics Bureau, (SDL201608), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Nanjing Normal University Research Foundation for Talented Scholars (2013119XGQ0061, 2014119XGQ0080), Open Fund of Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology (15-140-30-008K), Open Project Program of the State Key Lab of CAD & CG, Zhejiang University (A1616), Fundamental Research Funds for the Central Universities (LGYB201604).
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Citation
APPLIED SCIENCES-BASEL, 2016, 6 (6), p169Author affiliation
/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of InformaticsVersion
- VoR (Version of Record)
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APPLIED SCIENCES-BASELPublisher
MDPIissn
2076-3417Acceptance date
2016-05-30Copyright date
2016Available date
2019-08-29Publisher DOI
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Science & TechnologyPhysical SciencesTechnologyChemistry, MultidisciplinaryMaterials Science, MultidisciplinaryPhysics, AppliedChemistryMaterials SciencePhysicsmagnetic resonance imagingparameter estimationsupport vector machinedual-tree complex wavelet transformtwin support vector machinevarianceentropyBIOGEOGRAPHY-BASED OPTIMIZATIONFRACTIONAL FOURIER ENTROPYCLASSIFICATIONMRIALGORITHMHYBRIDIZATIONRECOGNITIONDIAGNOSISENERGYMODEL
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