Pathological brain detection based on wavelet entropy and Hu moment invariants

Bio-Medical Materials and Engineering

Volumn 26, Issue , 2015, Pages S1283-S1290

  Zhang, Yudong ^a   Wang, Shuihua ^a   Sun, Ping ^b   Phillips, Preetha ^c  

^a NANJING NORMAL UNIVERSITY   (China)

^b CITY UNIVERSITY OF NEW YORK   (United States)

^c SHEPHERD UNIVERSITY   (United States)

Author keywords

computer aided diagnosis; Hu's moment invariant; magnetic resonance imaging; radial basis function; support vector machine; Wavelet entropy

Indexed keywords

CLASSIFICATION (OF INFORMATION); EIGENVALUES AND EIGENFUNCTIONS; ENTROPY; FEATURE EXTRACTION; FUNCTIONS; MAGNETIC RESONANCE IMAGING; RADIAL BASIS FUNCTION NETWORKS; SUPPORT VECTOR MACHINES;

CLASSIFICATION ACCURACY; COMPUTER AIDED DIAGNOSIS(CAD); GENERALIZED EIGENVALUES; MOMENT INVARIANT; PROXIMAL SUPPORT VECTOR MACHINES; RADIAL BASIS FUNCTION(RBF); RADIAL BASIS FUNCTIONS; WAVELET ENTROPIES;

COMPUTER AIDED DIAGNOSIS;

ACCURACY; ARTICLE; BRAIN; CLASSIFICATION; DECOMPOSITION; ENTROPY; IMAGE ANALYSIS; IMAGE PROCESSING; METHODOLOGY; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; PRIORITY JOURNAL; RADIOLOGICAL PARAMETERS; SUPPORT VECTOR MACHINE; WAVELET ANALYSIS; ALGORITHM; AUTOMATED PATTERN RECOGNITION; BRAIN DISEASE; COMPUTER ASSISTED DIAGNOSIS; HUMAN; IMAGE ENHANCEMENT; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; BRAIN; BRAIN DISEASES; ENTROPY; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; WAVELET ANALYSIS;

EID: 84977465820     PISSN: 09592989     EISSN: 18783619     Source Type: Journal    
DOI: 10.3233/BME-151426     Document Type: Article

References (26)

1. S. Goh, et al., Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder: Evidence from brain imaging, JAMA Psychiatry 71 (2014), 665-671.
2. Y. Zhang, et al., Energy preserved sampling for compressed sensing MRI, Computational and Mathematical Methods in Medicine 2014 (2014), 12.
3. Z. Dong, et al., Improving the spectral resolution and spectral fitting of 1H MRSI data from human calf muscle by the SPREAD technique, NMR in Biomedicine 27 (2014), 1325-1332.
4. S. Wang, G. Ji and Z. Dong, An improved quality guided phase unwrapping method and its applications to MRI, Progress in Electromagnetics Research 145 (2014), 273-286.
5. Y. Zhang, et al., Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging, IEEJ Transactions on Electrical and Electronic Engineering 10 (2015), 116-117.
6. S. Chaplot, L.M. Patnaik and N.R. Jagannathan, Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network, Biomedical Signal Processing and Control 1 (2006), 86-92.
7. E.S.A. El-Dahshan, T. Hosny and A.B.M. Salem, Hybrid intelligent techniques for MRI brain images classification, Digital Signal Processing 20 (2010), 433-441.
8. L. Wu and S. Wang, Magnetic resonance brain image classification by an improved artificial bee colony algorithm, Progress in Electromagnetics Research 116 (2011), 65-79.
9. Y. Zhang and L. Wu, An MR brain images classifier via principal component analysis and kernel support vector machine, Progress in Electromagnetics Research 130 (2012), 369-388.
10. M. Saritha, K. Paul Joseph and A.T. Mathew, Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network, Pattern Recognition Letters 34 (2013), 2151-2156.
11. Y. Zhang, et al., Effect of spider-web-plot in MR brain image classification, Pattern Recognition Letters 62 (2015), 14-16.
12. S. Das, M. Chowdhury and M.K. Kundu, Brain MR image classification using multiscale geometric analysis of ripplet, Progress in Electromagnetics Research-Pier 137 (2013), 1-17.
13. Y. Zhang, et al., An MR brain images classifier system via particle swarm optimization and kernel support vector machine, The Scientific World Journal 2013 (2013), 9.
14. H. Kalbkhani, M.G. Shayesteh and B. Zali-Vargahan, Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series, Biomedical Signal Processing and Control 8 (2013), 909-919.
15. E.S.A. El-Dahshan, et al., Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm, Expert Systems with Applications 41 (2014), 5526-5545.
16. S. Wang and Z. Dong, Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree, Progress in Electromagnetics Research 144 (2014), 171-184.
17. X. Zhou, et al., Detection of pathological brain in MRI scanning based on Wavelet-entropy and naive Bayes classifier, in: Bioinformatics and Biomedical Engineering, F. Ortuño and I. Rojas, eds., Springer International Publishing, Granada, Spain, 2015, pp. 201-209.
18. G. Yang, et al., Automated classification of brain images using wavelet-energy and biogeography-based optimization, Multimedia Tools and Applications (2015), 1-17.
19. S. Wang, et al., Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection, International Journal of Imaging Systems and Technology 25 (2015), 153-164.
20. Z. Dong, et al., Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning, Frontiers in Computational Neuroscience 66 (2015), 1-15.
21. R. Harikumar and B.V. Kumar, Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor, International Journal of Imaging Systems and Technology 25 (2015), 33-40.
22. Y. Zhang, et al., Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC, Biomedical Signal Processing and Control 21 (2015), 58-73.
23. L. Fang, L. Wu and Y. Zhang, A novel demodulation system based on continuous wavelet transform, Mathematical Problems in Engineering 2015 (2015), 9.
24. Y. Zhang, et al., Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM), Entropy 17 (2015), 1795-1813.
25. M.-K. Hu, Visual pattern recognition by moment invariants, IRE Transactions on Information Theory 8 (1962), 179-187.
26. O.L. Mangasarian and E.W. Wild, Multisurface proximal support vector machine classification via generalized eigenvalues, IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (2006), 69-74.