Research Progress of Multi-Model Medical Image Fusion at Feature Level_Journal of Biomedical Engineering

Authors：

ZHANGJunjie ,  ZHOUTao , LUHuiling , WANGHuiqun

School of Science, Ningxia Medical University, Yinchuan 750004, China;

Corresponding author：

ZHOUTao, Email: zhoutaonxmu@126.com

Keywords：

multi-model; feature extraction; feature selection; feature reduction; image fusion; medical image analysis

DOI：

10.7507/1001-5515.20160067

Video：

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Abstract Full text Figures/Tables Video References Cited by

Medical image fusion realizes advantage integration of functional images and anatomical images. This article discusses the research progress of multi-model medical image fusion at feature level. We firstly describe the principle of medical image fusion at feature level. Then we analyze and summarize fuzzy sets, rough sets, D-S evidence theory, artificial neural network, principal component analysis and other fusion methods' applications in medical image fusion and get summery. Lastly, we in this article indicate present problems and the research direction of multi-model medical images in the future.

Citation： ZHANGJunjie, ZHOUTao, LUHuiling, WANGHuiqun. Research Progress of Multi-Model Medical Image Fusion at Feature Level. Journal of Biomedical Engineering, 2016, 33(2): 394-399. doi: 10.7507/1001-5515.20160067 Copy

1.	周涛, 陆惠玲, 陈志强, 等.多模态医学影像融合识别技术研究进展[J].生物医学工程学杂志, 2013, 30(5):1117-1122.
2.	GUPTA V, KIRIŞLI H A, HENDRIKS E A, et al. Cardiac MR perfusion image processing techniques:a survey[J]. Med Image Anal, 2012, 16(4):767-785.
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.
4.	张淑军, 王高峰, 石峰.基于AAM提取几何特征的人脸识别算法[J].系统仿真学报, 2013, 25(10):2374-2380.
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.
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.
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.
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.
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.
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.
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.
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.
13.	BALASUBRAMANIAM P, ANANTHI V P. Image fusion using intuitionistic fuzzy sets[J]. Information Fusion, 2014, 20:21-30.
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.
15.	KRUPKA J, JIRAVA P. Rough-fuzzy classifier modeling using data repository sets[J]. Procedia Comput Sci, 2014, 35:701-709.
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.
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.
18.	王国胤, 姚一豫, 于洪.粗糙集理论与应用研究综述[J].计算机学报, 2009, 32(7):1229-1246.
19.	PHOPHALIA A, RAJWADE A, MITRA S K. Rough set based image denoising for brain MR images[J]. Signal Processing, 2014, 103:24-35.
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.
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.
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.
23.	YAO Yiyu. The superiority of three-way decisions in probabilistic rough set models[J]. Inf Sci, 2011, 181(6):1080-1096.
24.	SUN Bingzhen, MA Weimin, ZHAO Haiyan. Decision-theoretic rough fuzzy set model and application[J]. Inf Sci, 2014, 283:180-196.
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.
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.
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.
28.	毛海岑, 刘爱东.利用证据理论的图像融合方法[J].红外与激光工程, 2013, 42(6):1642-1646.
29.	姚丽莎, 赵海峰, 罗斌, 等.基于证据理论的小波域多特征医学图像融合[J].计算机应用, 2012, 32(6):1544-1547.
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.
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.
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.
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.
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.	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.	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.	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.	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.	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.	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.
41.	曾岳, 冯大政.一种基于加权变形的2DPCA的人脸特征提取方法[J].电子与信息学报, 2011, 33(4):769-774.
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.	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.

1. 周涛, 陆惠玲, 陈志强, 等.多模态医学影像融合识别技术研究进展[J].生物医学工程学杂志, 2013, 30(5):1117-1122.
2. GUPTA V, KIRIŞLI H A, HENDRIKS E A, et al. Cardiac MR perfusion image processing techniques:a survey[J]. Med Image Anal, 2012, 16(4):767-785.
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.
4. 张淑军, 王高峰, 石峰.基于AAM提取几何特征的人脸识别算法[J].系统仿真学报, 2013, 25(10):2374-2380.
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.
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.
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.
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.
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.
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.
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.
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.
13. BALASUBRAMANIAM P, ANANTHI V P. Image fusion using intuitionistic fuzzy sets[J]. Information Fusion, 2014, 20:21-30.
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.
15. KRUPKA J, JIRAVA P. Rough-fuzzy classifier modeling using data repository sets[J]. Procedia Comput Sci, 2014, 35:701-709.
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.
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.
18. 王国胤, 姚一豫, 于洪.粗糙集理论与应用研究综述[J].计算机学报, 2009, 32(7):1229-1246.
19. PHOPHALIA A, RAJWADE A, MITRA S K. Rough set based image denoising for brain MR images[J]. Signal Processing, 2014, 103:24-35.
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.
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.
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.
23. YAO Yiyu. The superiority of three-way decisions in probabilistic rough set models[J]. Inf Sci, 2011, 181(6):1080-1096.
24. SUN Bingzhen, MA Weimin, ZHAO Haiyan. Decision-theoretic rough fuzzy set model and application[J]. Inf Sci, 2014, 283:180-196.
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.
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.
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.
28. 毛海岑, 刘爱东.利用证据理论的图像融合方法[J].红外与激光工程, 2013, 42(6):1642-1646.
29. 姚丽莎, 赵海峰, 罗斌, 等.基于证据理论的小波域多特征医学图像融合[J].计算机应用, 2012, 32(6):1544-1547.
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.
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.
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.
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.
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. 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. 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. 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. 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. 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. 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.
41. 曾岳, 冯大政.一种基于加权变形的2DPCA的人脸特征提取方法[J].电子与信息学报, 2011, 33(4):769-774.
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. 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.

Journal of Biomedical Engineering

Research Progress of Multi-Model Medical Image Fusion at Feature Level

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