Abstract
Smart healthcare is being adopted gradually as information technology advances. The enormous increase in demand for smart medical imaging has resulted in the fusion of a number of important imaging technologies. In smart imaging, many times single modality images are not sufficient to extract the major or minor information from medical images. Therefore in this paper, a new fusion algorithm is introduced for multi-modality medical images to extract maximum information and provide an efficient fused image. In proposed scheme, NSCT is used to get low- and high-frequency components of the medical images. Further, clustering-based fusion technique is used for fusing low-frequency components by analysing cluster features. Similarly, contrast-preserving image fusion on the high-frequency coefficients is accomplished by the use of directed contrast based on cluster-based components. The experimental results and comparison analysis is conducted on the multi-modal medical image dataset. Test results and evaluations of the proposed technique show that it outperforms the leading fusion strategies in terms of contrast and edge preservations.
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Asha CS, Lal S, Gurupur VP, Saxena PP (2019) Multi-modal medical image fusion with adaptive weighted combination of NSST bands using chaotic grey wolf optimization. IEEE Access 7:40782–40796
Benjamin JR, Jayasree T (2019) An efficient MRI-PET medical image fusion using non-subsampled Shearlet transform. In: 2019 IEEE international conference on intelligent techniques in control, optimization and signal processing (INCOS), IEEE, pp 1–5
Bhatnagar G, Wu QJ, Liu Z (2013) Human visual system inspired multi-modal medical image fusion framework. Expert Syst Appl 40(5):1708–1720
Cao Y, Li S, Hu J (2011) ‘Multi-focus image fusion by nonsubsampledshearlet transform’. In: 2011 Sixth International Conference on Image and Graphics, IEEE, pp. 17–21
Chowdhary CL, Patel PV, Kathrotia KJ, Attique M, Perumal K, Ijaz MF (2020) Analytical study of hybrid techniques for image encryption and decryption. Sensors 20(18):5162
Du J, Li W, Lu K, Xiao B (2016) An overview of multi-modal medical image fusion. Neurocomputing 215:3–20
Fu Z, Zhao Y, Xu Y, Xu L, Xu J (2020) Gradient structural similarity based gradient filtering for multi-modal image fusion. Inf Fus 53:251–268
Ganasala P, Kumar V (2014) Multi-modality medical image fusion based on new features in NSST domain. Biomed Eng Lett 4(4):414–424
Ganasala P, Kumar V (2016) Feature-motivated simplified adaptive PCNN-based medical image fusion algorithm in NSST domain. J Digit Imaging 29(1):73–85
Ghimpeţeanu G, Batard T, Bertalmío M, Levine S (2015) A decomposition framework for image denoising algorithms. IEEE Trans Image Process 25(1):388–399
Goyal S, Singh V, Rani A, Yadav N (2020) FPRSGF denoised non-subsampled shearlet transform-based image fusion using sparse representation. SIViP 14(4):719–726
Guorong G, Luping X, Dongzhu F (2013) Multi-focus image fusion based on non-subsampled shearlet transform. IET Image Proc 7(6):633–639
Hou R, Zhou D, Nie R, Liu D, Ruan X (2019) Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model. Med Biol Eng Comput 57(4):887–900
Hu Q, Hu S, Zhang F (2020) Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering. Signal Process Image Commun 83:115758
Ijaz MF, Attique M, Son Y (2020) Data-driven cervical cancer prediction model with outlier detection and over-sampling methods. Sensors 20(10):2809
Kong W, Liu J (2013) Technique for image fusion based on nonsubsampled shearlet transform and improved pulse-coupled neural network. Opt Eng 52(1):017001
Li S, Yin H, Fang L (2012) Group-sparse representation with dictionary learning for medical image denoising and fusion. IEEE Trans Biomed Eng 59(12):3450–3459
Liu S, Shi M, Zhu Z, Zhao J (2017) Image fusion based on complex-shearlet domain with guided filtering. Multidimension Syst Signal Process 28(1):207–224
Liu X, Mei W, Du H (2018) Multi-modality medical image fusion based on image decomposition framework and nonsubsampled shearlet transform. Biomed Signal Process Control 40:343–350
Luo X, Zhang Z, Zhang B, Wu X (2016) Image fusion with contextual statistical similarity and nonsubsampled shearlet transform. IEEE Sens J 17(6):1760–1771
Maqsood S, Javed U (2020) Multi-modal medical image fusion based on two-scale image decomposition and sparse representation. Biomed Signal Process Control 57:101810
Mehta N, Budhiraja S (2018) Multi-modal medical image fusion using guided filter in NSCT domain. Biomed Pharmacol J 11(4):1937–1946
Moonon AU, Hu J, Li S (2015) Remote sensing image fusion method based on nonsubsampled shearlet transform and sparse representation. Sens Imaging 16(1):23
Ouerghi H, Mourali O, Zagrouba E (2018) Non-subsampled shearlet transform based MRI and PET brain image fusion using simplified pulse coupled neural network and weight local features in YIQ colour space. IET Image Proc 12(10):1873–1880
Panigrahi R, Borah S, Bhoi AK, Ijaz MF, Pramanik M, Kumar Y, Jhaveri RH (2021a) A consolidated decision tree-based intrusion detection system for binary and multiclass imbalanced datasets. Mathematics 9(7):751
Panigrahi R, Borah S, Bhoi AK, Ijaz MF, Pramanik M, Jhaveri RH, Chowdhary CL (2021b) Performance Assessment of supervised classifiers for designing intrusion detection systems: a comprehensive review and recommendations for future research. Mathematics 9(6):690
Ramlal SD, Sachdeva J, Ahuja CK, Khandelwal N (2019) An improved multi-modal medical image fusion scheme based on hybrid combination of nonsubsampled contourlet transform and stationary wavelet transform. Int J Imaging Syst Technol 29(2):146–160
Singh R, Srivastava R, Prakash O, Khare A (2012) Multi-modal medical image fusion in dual tree complex wavelet transform domain using maximum and average fusion rules. J Med Imaging Health Inf 2(2):168–173
Srinivasu PN, SivaSai JG, Ijaz MF, Bhoi AK, Kim W, Kang JJ (2021) Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors 21(8):2852
Tannaz A, Mousa S, Sabalan D, Masoud P (2020) ‘Fusion of multi-modal medical images using nonsubsampled shearlet transform and particle swarm optimization. Multidimens Syst Signal Process 31(1):269–287
Ullah H, Ullah B, Wu L, Abdalla FY, Ren G, Zhao Y (2020) Multi-modality medical images fusion based on local-features fuzzy sets and novel sum-modified-Laplacian in non-subsampled shearlet transform domain. Biomed Signal Process Control 57:101724
Wang L, Li B, Tian LF (2014) Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients. Inf Fus 19:20–28
Xiao-Bo Q, Jing-Wen Y, Hong-Zhi XIAO, Zi-Qian Z (2008) Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain. Acta AutomaticaSinica 34(12):1508–1514
Xu Z (2014) Medical image fusion using multi-level local extrema. Inf Fus 19:38–48
Yin M, Liu W, Zhao X, Yin Y, Guo Y (2014) A novel image fusion algorithm based on nonsubsampledshearlet transform. Optik 125(10):2274–2282
Yin M, Liu X, Liu Y, Chen X (2018) Medical image fusion with parameter-adaptive pulse coupled neural network in nonsubsampled shearlet transform domain. IEEE Trans Instrum Meas 68(1):49–64
Zhang P, Yuan Y, Fei C, Pu T, Wang S (2018) Infrared and visible image fusion using co-occurrence filter. Infrared Phys Technol 93:223–231
Zhao C, Guo Y, Wang Y (2015) A fast fusion scheme for infrared and visible light images in NSCT domain. Infrared Phys Technol 72:266–275
Zhou T, Ruan S, Canu S (2019) A review: deep learning for medical image segmentation using multi-modality fusion. Array 3:100004
Zhu Z, Chai Y, Yin H, Li Y, Liu Z (2016) A novel dictionary learning approach for multi-modality medical image fusion. Neurocomputing 214:471–482
Zhu Z, Yin H, Chai Y, Li Y, Qi G (2018) A novel multi-modality image fusion method based on image decomposition and sparse representation. Inf Sci 432:516–529
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Diwakar, M., Singh, P., Shankar, A. et al. Directive clustering contrast-based multi-modality medical image fusion for smart healthcare system. Netw Model Anal Health Inform Bioinforma 11, 15 (2022). https://doi.org/10.1007/s13721-021-00342-2
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DOI: https://doi.org/10.1007/s13721-021-00342-2