Multimodal medical image fusion based on Yager’s intuitionistic fuzzy sets

Document Type: Original Manuscript

Authors

1 JNTUK KAKINADA ECE DEPARTMENT

2 Bapatla Engineering College, Bapatla

3 JNTUK, KAKINADA

Abstract

The objective of image fusion for medical images is to combine multiple images obtained from various sources into a single image suitable for better diagnosis. Most of the state-of-the-art image fusing technique is based on nonfuzzy sets, and the fused image so obtained lags with complementary information. Intuitionistic fuzzy sets (IFS) are determined to be more suitable for civilian, and medical image processing as more uncertainties are considered compared with fuzzy set theory. In this paper, an algorithm for effectively fusing multimodal medical images is presented. In the proposed method, images are initially converted into Yager’s intuitionistic fuzzy complement images (YIFCIs), and a new objective function called intuitionistic fuzzy entropy (IFE) is employed to obtain the optimum value of the parameter in membership and non-membership functions. Next, the YIFCIs are compared using contrast visibility (CV) to construct a decision map (DM). DM is refined with consistency verification to create a fused image. Simulations on several pairs of multimodal medical images are performed and compared with the existing fusion methods, such as simple average, discrete cosine transform (DCT), redundant wavelet transform (RWT), intuitionistic fuzzy set, fuzzy transform and interval-valued intuitionistic fuzzy set (IVIFS). The superiority of the proposed method is presented and is justified. Fused image quality is also verified with various quality metrics, such as spatial frequency (SF), average gradient (AG), fusion symmetry (FS), edge information preservation (QAB/F), entropy (E) and computation time (CoT). 

Keywords


[1] B. S. Abdul, M. Arfanjaffar, A. Hussain, M. M. Anwar, Block based pixel level multi-focus image fusion using particle swarm optimization, International Journal of Innovative Computing, Information and Control, 7(7A)(2011), 35833596.

[2] A. Alizad, D. H. Whaley, J. F. Greenleaf, M. Fatemi, Potential applications of vibro-acoustography in breastimaging, Technology in Cancer Research and Treatment, 4(2)(2005), 151-157.

[3] K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20(1)(1986), 87-96.

[4] K. Atanassov, G. Gargov, Interval valued intuitionistic fuzzy sets, Fuzzy Sets and Systems, 31(3)(1989), 343-349.

[5] P. Balasubramaniam, V. P. Ananthi, Image fusion using intuitionistic fuzzy sets, Information Fusion, 20(2014), 21-30.

[6] K. G. Baum, K. Raerty, M. Helguera, E. Schmidt, Investigation of PET/MRI image fusion schemes for enhanced breast cancer diagnosis, IEEE Conference on Nuclear Science Symposium(NSS), October28-November3, Honolulu, Hawaii, 5(2007), 3774-3780.

[7] G. Bhatnagar, Q. Wu and Z. Liu, Directive contrast based multimodal medical image fusion in NSCT domain, IEEE Transactions on Multimedia, 15(5)(2013), 1014-1024.

[8] C. Bhuvaneswari, P. Aruna, D. Loganathan, A new fusion model for classification of the lung diseases using genetic algorithm, Egyptian Informatics Journal, 2(2014), 69-77.

[9] P. Burillo, H. Bustince, Entropy on intuitionistic fuzzy set and on interval-valued fuzzy set, Fuzzy Sets and Systems, 78(1996), 305-316.

[10] H. Bustince, J. Kacpryzk, V. Mohedano, Intuitionistic fuzzy generators: application to intuitionistic fuzzy complementation, Fuzzy Sets and Systems, 114(2000), 485-504.

[11] T. Chaira, A novel intuitionistic fuzzy c means clustering algorithm and its application to medical images, Applied Soft Computing, 11(2)(2011), 1711-1717.

[12] S. Das, M. K. Kundu, Ripplet based multimodality medical image fusion using pulse-coupled neural network and modified spatial frequency, IEEE International Conference on Recent Trends in Information Systems, December 21-23, Kolkata, India, (2011), 229-234.

[13] L. A. De, S. Termni, A definition of non-probabilistic entropy in the setting of fuzzy set theory, Information Control, 20(4)(1972), 301-312.

[14] W. Dou, S. Ruan, Y. Chen, D. Bloyet, J. M. Constans, A frame work of fuzzy information fusion for the segmentation of brain tumour tissues on MR images, Image and Vision Computing, 25(2)(2007), 164-171.

[15] E. Z. A. E. G. Fatma, E. Mohammed, A. Ahmed, Current trends in medical image registration and fusion, Egyptian Informatics Journal, 17(1)(2016), 99-124.

[16] M. B. A. Haghighat, A. Aghagolzadeh, H. Seyedarabi, Multi-focus image fusion for visual sensor networks in DCT domain, Computers and Electrical Engineering, 37(5)(2011), 789-797.

[17] C. He, Q. Liu, H. Li, H. Wang, Multimodal medical image fusion based on IHS and PCA. Procedia Engineering, Symposium on Security Detection and Information Processing, November 12-14, Hefei, China, 7(2010), 280-285.

[18] H. G. Hosseini, A. Alizad, M. Fatemi, Integration of Vibro-Acoustography imaging modality with the traditional mammography, International Journal of Biomedical Imaging, 2007(2007), 1-8.

[19] P. Jagalingam, A. V. Hegde, A Review of quality metrics for fused image, Aquatic Procedia, International Conference on Water Resources, Coastal and Ocean Engineering (ICWRCOE), March 12-14, Mangalore, Karnataka, India, 4(2015), 133-142.

[20] A.P.James, B.V.Dasarathy, Medical image fusion: a survey of the state of the art, InformationFusion, 19(2014), 4-19.

[21] I. Kaplan, E. Kolupka, M. Morrissey, MRI-ultrasound image fusion for 125I prostate implant treatment planning, International Journal of Radiation Oncology Biology Physics, 42(1)(1998), 294.

[22] X. Li, M. He and M. Roux, Multifocus image fusion based on redundant wavelet transform, IET Image Processing, 4(4)(2010), 283-293.

[23] S. Li, X. Kang, L. Fang, Pixel-level image fusion: A survey of the state of theart, Information Fusion, 33(C) (2017), 100-112.

[24] H. Li, B. S. Manjunath, S. K. Mitra, Multisensor image fusion using the wavelet transform, Graph Models Image Process, 57(3)(1995), 235-245.

[25] J. Liang, Y. He, D. Liu and X. Zeng, Image fusion using higher order singular value decomposition, IEEE Transactions on Image Processing 21(5)(2012), 2898-2909.

[26] M. Meenu, S. Rajiv, A novel method of multimodal medical image fusion using fuzzy transform, Journal of Visual Communication and Image Representation, 40(2016), 197-217.

[27] H. O. S. Mishra, S. Bhatnagar, MRI and CT image fusion based on wavelet transform, International Journal of Information and Computation Technology, 4(1)(2014), 47-52.

[28] V. P. S. Naidu, J. R. Raol, Pixel level image fusion using wavelets and principal component analysis, Defense Science Journal, 58(3)(2008), 338-352.

[29] B. K. Shreyamshakumar, Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform, Signal, Image and Video Processing, 7(6)(2013), 1125-1143.

[30] E. Szmidt, J. Kacpryzyk, Distance between intuitionistic fuzzy set, Fuzzy Sets Systems, 114(3)(2000), 505-518.

[31] Z. Tanish, Z. Mukesh, A Novel region based multimodality image fusion method, Journal of Pattern Recognition Research, 6(2)(2011), 140-153.

[32] A. Toet, J. J. Vanruyven, J. M. Valeton, Merging thermal and visual images by a contrast pyramid, Optical Engineering, 28(7)(1989), 789-792.

[33] Y. Wu, C. Wang, S. C. Ng, A. Madabhusi, Y. Zhong, Breast cancer diagnosis using neural-based linear fusion strategies, Processing of Springer, International Conference on Neural Information Processing (ICONIP)., October 3-6, Hong Kong, China, (2006), 165-175.

[34] R. R. Yager, Some aspects of intuitionistic fuzzy sets, Fuzzy Optimization and Decision Making, 8(1) (2009), 67-90.

[35] Y. Yang, D. S. Park, S. Huang, N. Rao, Medical image fusion via an effective wavelet based approach, EURASIP Journal on Advances in Signal Processing, 2010 (2010), 1-13.

[36] Z. L. Yue, A group decision making approach based on aggregating interval data into interval-valued intuitionistic fuzzy information, Applied Mathematical Modelling, 38(2) (2014), 683-698.

[37] C. Yue, A geometric approach for ranking interval-valued intuitionistic fuzzy numbers with an application to group decision-making, Computers & Industrial Engineering, 102(2016), 233-245.

[38] C. Yue, Entropy-based weights on decision makers in group decision-making setting with hybrid preference representations, Applied Soft Computing, 60(2017), 737-749.

[39] L. A. Zadeh, Fuzzy sets, Information Control, 8(3)(1965), 338-353.