Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection

Simulation

Volumn 92, Issue 9, 2016, Pages 827-837

  Zhou, Xing Xing ^a   Yang, Jian Fei ^a   Sheng, Hui ^b   Wei, Ling ^b   Yan, Jie ^c   Sun, Ping ^d   Wang, Shui Hua ^a,e  

^a NANJING NORMAL UNIVERSITY   (China)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

^c STANFORD UNIVERSITY   (United States)

^d CITY COLLEGE OF NEW YORK   (United States)

^e Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology   (China)

Author keywords

kernel support vector machine; Magnetic Resonance Imaging; Principal Component Analysis; stationary wavelet transform; support vector machine

Indexed keywords

BRAIN MAPPING; CLASSIFICATION (OF INFORMATION); DIAGNOSIS; FEATURE EXTRACTION; MAGNETIC RESONANCE IMAGING; NEUROIMAGING; NONINVASIVE MEDICAL PROCEDURES; PRINCIPAL COMPONENT ANALYSIS; VECTORS; WAVELET TRANSFORMS;

AUTOMATIC CLASSIFICATION SYSTEMS; BRAIN IMAGING; CROSS VALIDATION; INFORMATION SET; NEW APPROACHES; NON-INVASIVE DIAGNOSTICS; PRE-CLINICAL; STATIONARY WAVELET TRANSFORMS;

SUPPORT VECTOR MACHINES;

EID: 84988719691     PISSN: 00375497     EISSN: 17413133     Source Type: Journal    
DOI: 10.1177/0037549716629227     Document Type: Article

References (65)

1. Yu D, Shui H, Gen L, et al. Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging. IEEJ Trans Electr Electron Eng 2015; 10: 116-117.
2. Chen Y, Zhang Y, Yang J, et al. Curve-like structure extraction using minimal path propagation with back-tracing. IEEE Trans Image Process 2015; 99: 1-16.
3. Brahim A, Gorriz JM, Ramirez J, et al., Intensity normalization of DaTSCAN SPECT imaging using a model-based clustering approach. Appl Soft Comput 2015; 37: 234-244.
4. Dong Z, Zhang Y, Liu F, et al., Improving the spectral resolution and spectral fitting of 1H MRSI data from human calf muscle by the SPREAD technique. NMR Biomed 2014; 27: 1325-1332.
5. Mahmoud BSM, Nannapaneni R, Chang S, et al., Improving the safety and quality of raw tuna fillets by X-ray irradiation. Food Control 2016; 60: 569-574.
6. Goh S, Dong Z, Zhang Y, et al. Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder: evidence from brain imaging. JAMA Psychiatr 2014; 71: 665-671.
7. Zhang Y, Wang S, Ji G, et al. An MR brain images classifier system via particle swarm optimization and kernel support vector machine. Sci World J 2013; 2013: 9.
8. Gott AN, Eckley IA, Aston JAD,. Estimating the population local wavelet spectrum with application to non-stationary functional magnetic resonance imaging time series. Stat Med 2015; 34: 3901-3915.
9. Zhang Y, Dong Z, Phillips P, et al. Exponential wavelet iterative shrinkage thresholding algorithm for compressed sensing magnetic resonance imaging. Inform Sci 2015; 322: 115-132.
10. Thacker JM, Li LP, Li W, et al. Renal blood oxygenation level-dependent magnetic resonance imaging a sensitive and objective analysis. Investig Radiol 2015; 50: 821-827.
11. Zhang Y-D, Chen S, Wang S-H, et al. Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine. Int J Imag Syst Technol 2015; 25: 317-327.
12. Chaplot S, Patnaik LM, Jagannathan NR., Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control 2006; 1: 86-92.
13. El-Dahshan ESA, Hosny T, Salem ABM., Hybrid intelligent techniques for MRI brain images classification. Digital Signal Process 2010; 20: 433-441.
14. Wu L, Wang S., Magnetic resonance brain image classification by an improved artificial bee colony algorithm. Progr Electromagn Res 2011; 116: 65-79.
15. Dong Z, Wu L, Wang S, et al. A hybrid method for MRI brain image classification. Expert Syst Appl 2011; 38: 10049-10053.
16. Ramasamy R, Anandhakumar P,. Brain tissue classification of MR images using fast Fourier transform based expectation-maximization Gaussian mixture model. In: Wyld Michal Wozniak, Chaki Nabendu, Meghanathan Natarajan, Nagamalai Dhinaharan, (eds.) Advances in computing and information technology. Berlin, Germany: Springer, 2011, pp. 387-398.
17. Zhang Y, Wu L., An MR brain images classifier via principal component analysis and kernel support vector machine. Progr Electromagn Res 2012; 130: 369-388.
18. Saritha M, Paul Joseph K, Mathew AT., Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recognit Lett 2013; 34: 2151-2156.
19. Zhang Y, Dong Z, Ji G, et al., Effect of spider-web-plot in MR brain image classification. Pattern Recognit Lett 2015; 62: 14-16.
20. Das S, Chowdhury M, Kundu MK., Brain MR image classification using multiscale geometric analysis of ripplet. Progr Electromagn Res Pier 2013; 137: 1-17.
21. Wang S, Dong Z, Ji G, et al. Classification of Alzheimer Disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Progr Electromagn Res 2014; 144: 171-184.
22. Zhou X, Wang S, Xu W, et al. Detection of pathological brain in MRI scanning based on wavelet-entropy and naive Bayes classifier. In: Ortuño F, Rojas I, (eds) Bioinformatics and biomedical engineering. Granada, Spain: Springer International Publishing, 2015, pp. 201-209.
23. Zhang Y, Dong Z, Wang S, 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 2015; 17: 1795-1813.
24. Damodharan S, Raghavan D., Combining tissue segmentation and neural network for brain tumor detection. Int Arab J Inform Technol 2015; 12: 42-52.
25. Yang G, Zhang Y, Yang J, et al. Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimed Tool Appl. Published ahead of print 1 May 2015. DOI: 10.1007/s11042-015-2649-7.
26. Nazir M, Wahid F, Khan SA., A simple and intelligent approach for brain MRI classification. J Intell Fuzzy Syst 2015; 28: 1127-1135.
27. Zhang Y, Dong Z, Phillips P, et al. Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning. Front Comput Neurosci 2015; 66: 1-15.
28. Wang S, Zhang Y, Dong Z, et al. Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. Int J Imag Syst Technol 2015; 25: 153-164.
29. LaViolette PS, Daun MK, Paulson ES, et al. Effect of contrast leakage on the detection of abnormal brain tumor vasculature in high-grade glioma. J Neuro Oncol 2014; 116: 543-549.
30. Zhang Y, Wang S, Phillips P, 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. Biomed Signal Process Control 2015; 21: 58-73.
31. Moffat JJ, Ka MH, Jung EM, et al. Genes and brain malformations associated with abnormal neuron positioning. Mol Brain 2015; 8: 12.
32. Zhang Y, Wang S, Dong Z, et al. Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization. Progr Electromagn Res 2015; 152: 41-58.
33. Dong Z, Liu A, Wang S, et al. Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine. J Med Imag Health Inform 2015; 5: 1-9.
34. Farzan A, Mashohor S, Ramli AR, et al. Boosting diagnosis accuracy of Alzheimer's disease using high dimensional recognition of longitudinal brain atrophy patterns. Behav Brain Res 2015; 290: 124-130.
35. Zhang Y, Wang S, Sun P, et al. Pathological brain detection based on wavelet entropy and Hu moment invariants. Bio Med Mater Eng 2015; 26: 1283-1290.
36. Wibmer A, Hricak H, Gondo T, et al. Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol 2015; 25: 2840-2850.
37. Zhang Y, Wang S., Detection of Alzheimer's disease by displacement field and machine learning. PeerJ 2015; 3: e1251. DOI: 10.7717/peerj.
38. Poeppl TB, Eickhoff SB, Fox PT, et al. Connectivity and functional profiling of abnormal brain structures in pedophilia. Hum Brain Mapp 2015; 36: 2374-2386.
39. Wang S, Pan H, Zhang C, et al. RGB-D image-based detection of stairs, pedestrian crosswalks and traffic signs. J Vis Comm Image Represent 2014; 25: 263-272.
40. Zhang Y, Wang S, Phillips P, et al. Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowl Base Syst 2014; 64: 22-31.
41. Durak L., Shift-invariance of short-time Fourier transform in fractional Fourier domains. J Franklin Inst 2009; 346: 136-146.
42. Kayvanrad MH, McLeod AJ, Baxter JSH, et al. Stationary wavelet transform for under-sampled MRI reconstruction. Magn Reson Imag 2014; 32: 1353-1364.
43. Asgari S, Mehrnia A, Moussavi M., Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine. Comput Biol Med 2015; 60: 132-142.
44. Zhang Y-D, Wang S-H, Yang X-J, et al. Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine. SpringerPlus 2015; 4: 716.
45. Wang S, Yang X, Zhang Y, et al. Identification of green, Oolong and black teas in China via wavelet packet entropy and fuzzy support vector machine. Entropy 2015; 17: 6663-6682.
46. Hill PR, Anantrasirichai N, Achim A, et al. Undecimated dual-tree complex wavelet transforms. Signal Process Image Commun 2015; 35: 61-70.
47. Wu L, Wei G., A new classifier for polarimetric SAR images. Progr Electromagn Res 2009; 94: 83-104.
48. Martiskainen P, Järvinen M, Skön J-P, et al. Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Appl Anim Behav Sci 2009; 119: 32-38.
49. Jeyakumar V, Wang JH, Li G., Lagrange multiplier characterizations of robust best approximations under constraint data uncertainty. J Math Anal Appl 2012; 393: 285-297.
50. Cucker F, Smale S., On the mathematical foundations of learning. Bull Am Math Soc 2002; 39: 1-49.
51. Poggio T, Smale S., The mathematics of learning: dealing with data. Not Am Math Soc 2003; 50: 537-544.
52. Wang S, Zhang Y, Ji G, et al. Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization. Entropy 2015; 17: 5711-5728.
53. Thomas G, Simon D, Michelini J., Biogeography-based optimization of a variable camshaft timing system. Eng Appl Artif Intell 2015; 45: 376-387.
54. Zhang Y, Wu L, Wang S., UCAV path planning based on FSCABC. Information 2011; 14: 687-692.
55. Hong PN, Ahn CW., Fast artificial bee colony and its application to stereo correspondence. Expert Syst Appl 2016; 45: 460-470.
56. Zhang Y, Wu L, Wang S., Solving Two-dimensional HP model by firefly algorithm and simplified energy function. Math Prob Eng 2013; 13: 1-9.
57. Zaman MA, Sikder U., Bouc-Wen hysteresis model identification using Modified Firefly Algorithm. J Magn Magn Mater 2015; 395: 229-233.
58. Zhang Y, Wang S, Ji G., A comprehensive survey on particle swarm optimization algorithm and its applications. Math Prob Eng 2015; 2015: 38.
59. Wang S, Lu Z, Wei L, et al. Fitness-scaling adaptive genetic algorithm with local search for solving the Multiple Depot Vehicle Routing Problem. Simulation 2015; 91: 1-16.
60. Shamshirgaran SR, Ameri M, Khalaji M, et al. Design and optimization of a compressed air energy storage (CAES) power plant by implementing genetic algorithm. Mech Ind 2016; 17: 8.
61. Wang S, Chen M, Li Y, et al. Detection of dendritic spines using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks. Comput Math Methods Med 2015; 2015: 12.
62. Azami H, Escudero J., Improved multiscale permutation entropy for biomedical signal analysis: Interpretation and application to electroencephalogram recordings. Biomed Signal Process Control 2016; 23: 28-41.
63. Wang S, Zhang Y, Yang X, et al. Pathological brain detection by a novel image feature-fractional fourier entropy. Entropy 2015; 17: 7877.
64. Rana AQ, Qureshi ARM, Rahman L, et al. Association of restless legs syndrome, pain, and mood disorders in parkinson's disease. Int J Neurosci 2016; 126: 116-120.
65. Wang S, Zhang Y, Liu G, et al. Detection of Alzheimer's Disease by three-dimensional displacement field estimation in structural magnetic resonance imaging. J Alzheimer's Dis 2016; 50: 1-16.