25. GANIVADA A, RAY S S, PAL S K. Fuzzy rough sets, and a granular neural network for unsupervised feature selection[J]. Neural Netw, 2013, 48:91-108.
[2]
9. JAGANATHAN P, KUPPUCHAMY R. A threshold fuzzy entropy based feature selection for medical database classification[J]. Comput Biol Med, 2013, 43(12):2222-2229.
[3]
10. DAS S, KAR S. Group decision making in medical system:An intuitionistic fuzzy soft set approach[J]. Appl Soft Comput, 2014, 24:196-211.
[4]
11. QIU C Y, XIAO J, YU L, et al. A modified interval type-2 fuzzy C-means algorithm with application in MR image segmention[J]. Pattern Recognit Lett, 2013, 34(12):1329-1338.
[5]
12. BIGAND A, COLOT O. Fuzzy filter based on interval-valued fuzzy sets for image filtering[J]. Fuzzy Sets and Systems, 2010, 161(1):96-117.
[6]
13. BALASUBRAMANIAM P, ANANTHI V P. Image fusion using intuitionistic fuzzy sets[J]. Information Fusion, 2014, 20:21-30.
[7]
14. CHEN Ting-yu, WANG H P, LU Y Y. A multicriteria group decision-making approach based on interval-valued intuitionistic fuzzy sets:A comparative perspective[J]. Expert Syst Appl, 2011, 38(6):7647-7658.
[8]
15. KRUPKA J, JIRAVA P. Rough-fuzzy classifier modeling using data repository sets[J]. Procedia Comput Sci, 2014, 35:701-709.
[9]
16. BAI Hexiang, GE Yong, WANG Jinfeng, et al. A method for extracting rules from spatial data based on rough fuzzy sets[J]. Knowledge-Based Systems, 2014, 57:28-40.
[10]
17. LI D Z, WANG W, ISMAIL F. An evolving fuzzy neural predictor for multi-dimensional system state forecasting[J]. Neurocomputing, 2014, 145:381-391.
19. PHOPHALIA A, RAJWADE A, MITRA S K. Rough set based image denoising for brain MR images[J]. Signal Processing, 2014, 103:24-35.
[13]
20. NINGLER M, STOCKMANNS G, SCHNEIDER G, et al. Adapted variable precision rough set approach for EEG analysis[J]. Artif Intell Med, 2009, 47(3):239-261.
[14]
21. Pramod P K, VADAKKEPAT P, POH L A. Fuzzy-rough discriminative feature selection and classification algorithm, with application to microarray and image datasets[J]. Appl Soft Comput, 2011, 11(4):3429-3440.
[15]
22. JI Zexuan, SUN Quansen, XIA Yong, et al. Generalized rough fuzzy c-means algorithm for brain MR image segmentation[J]. Comput Methods Programs Biomed, 2012, 108(2):644-655.
[16]
23. YAO Yiyu. The superiority of three-way decisions in probabilistic rough set models[J]. Inf Sci, 2011, 181(6):1080-1096.
[17]
24. SUN Bingzhen, MA Weimin, ZHAO Haiyan. Decision-theoretic rough fuzzy set model and application[J]. Inf Sci, 2014, 283:180-196.
[18]
26. LIN T C. Decision-based fuzzy image restoration for noise reduction based on evidence theory[J]. Expert Syst Appl, 2011, 38(7):8303-8310.
[19]
27. SHOYAIB M, ABDULLAH W M, CHAE O. A skin detection approach based on the dempster-shafer theory of evidence[J]. International Journal of Approximate Reasoning, 2012, 53(4):636-659.
30. CHEN Degang, LI Wanlu, ZHANG Xiao, et al. Evidence-theory-based numerical algorithms of attribute reduction with neighborhood-covering rough sets[J]. International Journal of Approximate Reasoning, 2014, 55(3):908-923.
[23]
31. XIAO Zhi, YANG Xianglei, NIU Qing, et al. A new evaluation method based on D-S generalized fuzzy soft sets and its application in medical diagnosis problem[J]. Appl Math Model, 2012, 36(10):4592-4604.
[24]
32. SI Lei, WANG Zhongbin, TAN Chao, et al. A novel approach for coal seam terrain prediction through information fusion of improved D-S evidence theory and neural network[J]. Measurement, 2014, 54:140-151.
[25]
33. HEMANTH D J, VIJILA C, SELVAKUMAR A I, et al. Performance improved iteration-free artificial neural networks for abnormal magnetic resonance brain image classification[J]. Neurocomputing, 2014, 130(23):98-107.
2. GUPTA V, KIRI?LI H A, HENDRIKS E A, et al. Cardiac MR perfusion image processing techniques:a survey[J]. Med Image Anal, 2012, 16(4):767-785.
[28]
3. ZHAO Hongwei, ZHOU Baoyu, LIU Pingping, et al. Modulating a local shape descriptor through biologically inspired color feature[J]. J Bionic Eng, 2014, 11(2):311-321.
5. SINGH M, SINGH S, GUPTA S. An information fusion based method for liver classification using texture analysis of ultrasound images[J]. Information Fusion, 2014, 19:91-96.
[31]
6. SZCZYPIИSKI P, KLEPACZKO A, PAZUREK M, et al. Texture and color based image segmentation and pathology detection in capsule endoscopy videos[J]. Comput Methods Programs Biomed, 2014, 113(1):396-411.
[32]
7. GONZáLEZ-RUFINO E, CARRIóN P, CERNADAS E, et al. Exhaustive comparison of colour texture features and classification methods to discriminate cells categories in histological images of fish ovary[J]. Pattern Recognit, 2013, 46(9):2391-2407.
[33]
8. STOECKER W V, WRONKIEWIECZ M, CHOWDHURY R, et al. Detection of granularity in dermoscopy images of malignant melanoma using color and texture features[J]. Comput Med Imaging Graph, 2011, 35(2):144-147.
[34]
34. TORBATI N, AYATOLLAHI A, KERMANI A. An efficient neural network based method for medical image segmentation[J]. Comput Biol Med, 2014, 44:76-87.
[35]
35. BHATTACHARYYA S, PAL P, BHOWMICK S. Binary image denoising using a quantum multilayer self organizing neural network[J]. Appl Soft Comput, 2014, 24:717-729.
[36]
36. JAMES A P, DASARATHY B V. Medical image fusion:A survey of the state of the art[J]. Information Fusion, 2014, 19:4-19.
[37]
37. KAVITHA C T, CHELLAMUTHU C, RAJESH R. Medical image fusion using combined discrete wavelet and ripplet transforms[J]. Procedia Engineering, 2012, 38:813-820.
[38]
38. LIU Zhaodong, YIN Hongpeng, CHAI Yi, et al. A novel approach for multimodal medical image fusion[J]. Expert Syst Appl, 2014, 41(16):7425-7435.
[39]
39. HANSEN T J, ABRAHAMSEN T J, HANSEN L K. Denoising by semi-supervised kernel PCA preimaging[J]. Pattern Recognit Lett, 2014, 49:114-120.
[40]
40. HE Changtao, LIU Quanxi, LI Hong-liang, et al. Multimodal medical image fusion based on IHS and PCA[J]. Procedia Engineering, 2010, 7:280-285.
42. ZHOU Changjun, WANG Lan, ZHANG Qiang, et al. Face recognition based on PCA image reconstruction and LDA[J]. Optik-International Journal for Light and Electron Optics, 2013, 124(22):5599-5603.
[43]
43. GAN Lan, LV Wenya, ZHANG Xu, et al. Improved PCA + LDA applies to gastric cancer image classification process[J]. Phys Procedia, 2012, 24, Part C:1689-1695.