Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine

SpringerPlus

Volumn 4, Issue 1, 2015, Pages 1-16

  Zhang, Yu Dong ^a   Wang, Shui Hua ^a   Yang, Xiao Jun ^b   Dong, Zheng Chao ^c   Liu, Ge ^d   Phillips, Preetha ^e   Yuan, Ti Fei ^a  

^a NANJING NORMAL UNIVERSITY   (China)

^b CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

^c NEW YORK STATE PSYCHIATRIC INSTITUTE   (United States)

^d Columbia University and New York State Psychiatric Institute ^*  (United States)

^e SHEPHERD UNIVERSITY   (United States)

Author keywords

Computer aided diagnosis; Discrete wavelet packet transform; Fuzzy support vector machine; Magnetic resonance imaging; Pathological brain detection (PBD); Pattern recognition; Tsallis entropy

Indexed keywords

EID: 84948461606     PISSN: None     EISSN: 21931801     Source Type: Journal    
DOI: 10.1186/s40064-015-1523-4     Document Type: Article

References (53)

1. Amaral-Silva H, Wichert-Ana L, Murta LO, Romualdo-Suzuki L, Itikawa E, Bussato GF, Azevedo-Marques P (2014) The superiority of Tsallis Entropy over traditional cost functions for brain MRI and SPECT registration. Entropy 16(3):1632–1651. doi:10.3390/e16031632
2. Ashkezari AD, Ma H, Saha TK, Ekanayake C (2013) Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers. IEEE Trans Dielectr Electr Insul 20(3):965–973
3. Cabella BCT, Sturzbecher MJ, de Araujo DB, Neves UPC (2009) Generalized relative entropy in functional magnetic resonance imaging. Phys A 388(1):41–50. doi:10.1016/j.physa.2008.09.029
4. Campos D (2010) Real and spurious contributions for the Shannon, Rényi and Tsallis entropies. Physica A 389(18):3761–3768
5. Chaplot S, Patnaik LM, Jagannathan NR (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. doi:10.1016/j.bspc.2006.05.002
6. Chen JK, Li GQ (2014) Tsallis wavelet entropy and its application in power signal analysis. Entropy 16(6):3009–3025. doi:10.3390/e16063009
7. Damodharan S, Raghavan D (2015) Combining tissue segmentation and neural network for brain tumor detection. Int Arab J Inf Technol 12(1):42–52
8. Das S, Chowdhury M, Kundu MK (2013) Brain MR image classification using multiscale geometric analysis of ripplet. Prog Electromagn Res-Pier 137:1–17. doi:10.2528/pier13010105
9. Diniz PRB, Murta LO, Brum DG, de Araujo DB, Santos AC (2010) Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images. Brazilian J Med Biol Res 43(1):77–84. doi:10.1590/s0100-879x2009007500019
10. Dong Z, Wu L, Wang S, Zhang Y (2011) A hybrid method for MRI brain image classification. Expert Syst Appl 38(8):10049–10053
11. Dong Z, Zhang Y, Liu F, Duan Y, Kangarlu A, Peterson BS (2014) Improving the spectral resolution and spectral fitting of 1H MRSI data from human calf muscle by the SPREAD technique. NMR Biomed 27(11):1325–1332
12. Dong Z, Phillips P, Wang S, Ji G, Yang J, T-f Yuan (2015) Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front Comput Neurosci 66(9):1–15
13. El-Dahshan ESA, Hosny T, Salem ABM (2010) Hybrid intelligent techniques for MRI brain images classification. Digit Signal Proc 20(2):433–441. doi:10.1016/j.dsp.2009.07.002
14. El-Dahshan ESA, Mohsen HM, Revett K, Salem ABM (2014) Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst Appl 41(11):5526–5545. doi:10.1016/j.eswa.2014.01.021
15. Farzan A, Mashohor S, Ramli AR, Mahmud R (2015) Boosting diagnosis accuracy of Alzheimer’s disease using high dimensional recognition of longitudinal brain atrophy patterns. Behav Brain Res 290:124–130. doi:10.1016/j.bbr.2015.04.010
16. Goh S, Dong Z, Zhang Y, DiMauro S, Peterson BS (2014) Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder: evidence from brain imaging. JAMA psychiatry 71(6):665–671. doi:10.1001/jamapsychiatry.2014.179
17. Harikumar R, Kumar BV (2015) Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor. Int J Imaging Syst Technol 25(1):33–40. doi:10.1002/ima.22118
18. Hussain M (2014) Mammogram enhancement using lifting dyadic wavelet transform and normalized Tsallis entropy. J Comput Sci Technol 29(6):1048–1057. doi:10.1007/s11390-014-1489-7
19. Jayadeva Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905–910. doi:10.1109/tpami.2007.1068
20. Khader M, Ben Hamza A (2011) Nonrigid image registration using an entropic similarity. IEEE Trans Inf Technol Biomed 15(5):681–690. doi:10.1109/titb.2011.2159806
21. LaViolette PS, Daun MK, Paulson ES, Schmainda KM (2014) Effect of contrast leakage on the detection of abnormal brain tumor vasculature in high-grade glioma. J Neurooncol 116(3):543–549. doi:10.1007/s11060-013-1318-9
22. Lee SH, Lee CK, Park JB, Choi YH (2013) Diagnostic method for insulated power cables based on wavelet energy. IEICE Electronics Express 10(12):335–335. doi:10.1587/elex.10.20130335
23. Lin C-F, Wang S-D (2002) Fuzzy support vector machines. Neural Netw IEEE Trans 13(2):464–471. doi:10.1109/72.991432
24. Liu ZG, Hu QL, Cui Y, Zhang QG (2014) A new detection approach of transient disturbances combining wavelet packet and Tsallis entropy. Neurocomputing 142:393–407. doi:10.1016/j.neucom.2014.04.020
25. Munteanu CR, Fernandez-Lozano C, Abad VM, Fernandez SP, Alvarez-Linera J, Hernandez-Tamames JA, Pazos A (2015) Classification of mild cognitive impairment and Alzheimer’s Disease with machine-learning techniques using H-1 magnetic resonance spectroscopy data. Expert Syst Appl 42(15–16):6205–6214. doi:10.1016/j.eswa.2015.03.011
26. Nazir M, Wahid F, Khan SA (2015) A simple and intelligent approach for brain MRI classification. J Intell Fuzzy Syst 28(3):1127–1135. doi:10.3233/ifs-141396
27. Padma A, Sukanesh R (2014) Segmentation and classification of brain CT images using combined wavelet statistical texture features. Arab J Sci Eng 39(2):767–776. doi:10.1007/s13369-013-0649-3
28. Ribbens A, Hermans J, Maes F, Vandermeulen D, Suetens P, Alzheimers Dis N (2014) Unsupervised segmentation, clustering, and groupwise registration of heterogeneous populations of brain MR images. IEEE Trans Med Imaging 33(2):201–224. doi:10.1109/tmi.2013.2270114
29. Saritha M, Joseph KP, Mathew AT (2013) Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recogn Lett 34(16):2151–2156. doi:10.1016/j.patrec.2013.08.017
30. Schwarz D, Kasparek T (2014) Brain morphometry of MR images for automated classification of first-episode schizophrenia. Inf Fusion 19:97–102. doi:10.1016/j.inffus.2013.02.002
31. Sturzbecher MJ, Tedeschi W, Cabella BCT, Baffa O, Neves UPC, De Araujo DB (2009) Non-extensive entropy and the extraction of BOLD spatial information in event-related functional MRI. Phys Med Biol 54(1):161–174. doi:10.1088/0031-9155/54/1/011
32. Tsallis C (2009) Nonadditive entropy: the concept and its use. European Phys J A 40(3):257–266. doi:10.1140/epja/i2009-10799-0
33. Tsallis C (2011) The nonadditive entropy S-q and its applications in physics and elsewhere: some remarks. Entropy 13(10):1765–1804. doi:10.3390/e13101765
34. Tsallis C (2014) An introduction to nonadditive entropies and a thermostatistical approach to inanimate and living matter. Contemp Phys 55(3):179–197. doi:10.1080/00107514.2014.900977
35. Venkatesan AS, Parthiban L (2014) A novel nature inspired fuzzy tsallis entropy segmentation of magnetic resonance images. Neuroquantology 12(2):221–229
36. Wang S, Dong Z, Ji G, Zhang Y (2014) Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Prog Electromagn Res 144:171–184
37. Wang S, Zhang Y, Dong Z, Du S, Ji G, Yan J, Yang J, Wang Q, Feng C, Phillips P (2015a) Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. Int J Imaging Syst Technol 25(2):153–164. doi:10.1002/ima.22132
38. Wang S, Zhang Y, Ji G, Yang J, Wu J, Wei L (2015b) Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic abc and biogeography-based optimization. Entropy 17(8):5711–5728
39. Wu L, Wang S (2011) Magnetic resonance brain image classification by an improved artificial bee colony algorithm. Prog Electromagn Res 116:65–79
40. Xian G-m (2010) An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. Expert Syst Appl 37(10):6737–6741
41. Yang G, Zhang Y, Yang J, Ji G, Dong Z, Wang S, Feng C, Wang Q (2015) Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimedia Tools Appl. doi:10.1007/s11042-015-2649-7
42. Yu D-Z, Zheng C-D, Ai J-L, Shui H-W, Gen L-J, Zheng Z, Ji Q-Y (2015a) MR brain image classification via stationary wavelet transform and generalized eigenvalue proximal SVM. J Med Imaging Health Inform 5(7):1–9
43. Yu D, Shui H, Gen L, Zheng C (2015b) Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging. IEEJ Transact Electr Electron Eng 10(1):116–117. doi:10.1002/tee.22059
44. Yu D, Shui H, Zheng C, Phillip P, Ji G, Yang J (2015c) Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization. Prog Electromagn Res 152:41–58
45. Yu D, Zheng C, Gen L, Shui H (2015d) Effect of spider-web-plot in MR brain image classification. Pattern Recogn Lett 62:14–16. doi:10.1016/j.patrec.2015.04.016
46. Zhang Y, Wu L (2011) Optimal multi-level thresholding based on maximum tsallis entropy via an artificial bee colony approach. Entropy 13(4):841–859
47. Zhang Y, Wu L (2012) An Mr brain images classifier via principal component analysis and kernel support vector machine. Prog Electromagn Res 130:369–388
48. Zhang Y, Wang S, Ji G, Dong Z (2013) An MR brain images classifier system via particle swarm optimization and kernel support vector machine. Sci World J 2013:9. doi:10.1155/2013/130134
49. Zhang Y, Dong Z, Phillips P, Wang S, Ji G, Yang J (2015a) Exponential wavelet iterative shrinkage thresholding algorithm for compressed sensing magnetic resonance imaging. Inf Sci 322:115–132. doi:10.1016/j.ins.2015.06.017
50. Zhang Y, Dong Z, Wang S, Ji G, Yang J (2015b) 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(4):1795–1813
51. Zhang Y, Wang S, Phillips P, Dong Z, Ji G, Yang J (2015c) 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 21:58–73
52. Zhang Y, Wang S, Sun P, Phillips P (2015d) Pathological brain detection based on wavelet entropy and Hu moment invariants. Bio-Med Mater Eng 26(s1):1283–1290
53. Zhou X, Wang S, Xu W, Ji G, Phillips P, Sun P, Zhang Y (2015) 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, vol 9043. Lecture Notes in Computer Science. Springer International Publishing, Granada, pp 201–209. doi:10.1007/978-3-319-16483-0_20