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Research on Image Fusion Algorithm Based on NSST Frequency Division and Improved LSCN

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Abstract

Single modal medical images provide limited information and cannot reflect all the details of the relevant tissues, which may lead to misdiagnosis in clinical medicine. Therefore, a medical image fusion algorithm based on non-down-sampling shear wave transform (NSST) is proposed. This algorithm fuses multi-modal medical images, enriches the information of fused images, improves the image quality, and provides a basis for clinical diagnosis. Firstly, the low-frequency sub-band and several high-frequency directional sub-bands are obtained by NSST transformation of the source image, and the structural similarity between sub-bands is evaluated. Then, according to the characteristics of low-frequency sub-band images, for sub-images with high similarity, the regional features are obtained by region energy and variance, and the fusion method is based on region feature weighting. For sub-images with low similarity, two images to be fused are input into the LSCN model respectively by fusing the connection items of the improved LSCN model. The improved L-term replaces the ignition frequency in the traditional PCNN as the output. According to the characteristics of high frequency sub-images, the fusion rule of combining visual sensitivity coefficient and regional energy is adopted for sub-images with high similarity. For sub-images with low similarity, an improved guided filter is used to fuse the sub-images in order to maintain the clear edges of the images. Finally, the image is reconstructed by inverse NSST transform. The experimental results show that the proposed algorithm can obtain better fusion effects in both objective and subjective evaluation. The obtained fusion image has rich information, excellent edge retention characteristics, subjectively clear texture and high contrast and good visual effect.

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References

  1. Zhao W, Lu H (2017) Medical image fusion and Denoising with alternating sequential filter and adaptive fractional order Total variation[J]. IEEE Trans Instrum Meas 66(9):2283–2294

    Article  Google Scholar 

  2. Du J, Li W, Xiao B (2017) Anatomical-functional image fusion by information of interest in local Laplacian filtering domain.[J]. IEEE Trans Image Process A Publication of the IEEE Signal Process Soc 12(60):5855–5866

    MathSciNet  Google Scholar 

  3. Manchanda M, Sharma R (2018) An improved multimodal medical image fusion algorithm based on fuzzy transform[J]. J Vis Commun Image Represent 51:76–94

    Article  Google Scholar 

  4. Villadsen J, Hansen HD, Jørgensen LM, Keller SH, Andersen FL, Petersen IN, Knudsen GM, Svarer C (2018) Automatic delineation of brain regions on MRI and PET images from the pig.[J]. J Neurosci Methods 294:51–58

    Article  Google Scholar 

  5. Soundrapandiyan R (2017) An efficient DWT and intuitionistic fuzzy based multimodality medical image fusion[J]. Int J Imaging Syst Technol 27(2):118–132

  6. Huang Z, Ding M, Zhang X (2017) Medical image fusion based on non-subsampled Shearlet transform and spiking cortical model[J]. J Med Imaging Health Inform 7(1):229–234

    Article  Google Scholar 

  7. Jun-feng L, Xiao-li J, Wen-zhan D (2014) Medical image fusion based on lifting wavelet transform[J]. J Image Graph 19(11):1639–1648 (in Chinese)

    Google Scholar 

  8. Vishwakarma A, Bhuyan MK, Iwahori Y (2018) Non-subsampled shearlet transform-based image fusion using modified weighted saliency and local difference[J]. Multimed Tools Appl 77(21):1–28

    Google Scholar 

  9. Huang Y, Bi D, Wu D (2018) Infrared and visible image fusion based on different constraints in the non-subsampled Shearlet transform domain.[J]. Sensors 18(4):1169–1181

    Article  Google Scholar 

  10. 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[J]. IET Image Process 12(10):1873–1880

    Article  Google Scholar 

  11. Bhateja V, Moin A, Srivastava A et al (2016) Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer’s disease[J]. Rev Sci Instrum 87(7):1–7

    Article  Google Scholar 

  12. Ganasala P, Kumar V (2016) Feature-motivated simplified adaptive PCNN-based medical image fusion algorithm in NSST domain[J]. J Digit Imaging 29(1):73–85

    Article  Google Scholar 

  13. Cheng B, Jin L, Li G (2018) A novel fusion framework of visible light and infrared images based on singular value decomposition and adaptive DUAL-PCNN in NSST domain[J]. Infrared Phys Technol 16(9):37–45

    Google Scholar 

  14. ZHAO D, DAI WZ, LI J F. (2018) Medical image fusion based on NSST and improved PCNN[J]. J Optoelectron Laser 29(1):95–104

    Google Scholar 

  15. He K, Sun J, Tang X (2013) Guided image filtering[J]. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409

    Article  Google Scholar 

  16. Li S, Kang X, Hu J (2013) Image fusion with guided filtering[J]. IEEE Trans Image Process 22(7):2864–2875

    Article  Google Scholar 

  17. Zhu D, Xu L, Wang FB et al (2018) Multi-focus image fusion algorithm based on fast finite Shearlet Transformand guided filter[J]. Laser & Optoelectron Prog 55(1):196–203 (in Chinese)

    Google Scholar 

  18. Wang RR, Yang YD (2018) Image fusion algorithm based on complex Shearlet transform coupled with improved guided filtering[J]. J Electron Meas Instrum 5:126–133

    Article  Google Scholar 

  19. Sun G, Li J, Dai J et al (2018) Feature selection for IoT based on maximal information coefficient[J]. Futur Gen Comput Syst The Int J Esci 89:606–616

    Article  Google Scholar 

  20. Sun G, Chen T, Su Y et al (2018) Internet traffic classification based on incremental support vector machines[J]. Futur Gen Comput Syst The Int J Esci 23(4): 789–796

  21. Bavirisetti DP, Kollu V, Gang X, Dhuli R (2017) Fusion of MRI and CT images using guided image filter and image statistics[J]. Int J Imaging Syst Technol 27(3):227–237

    Article  Google Scholar 

  22. ZHAN K, SHI JH, TENG JC et al (2017) Linking synaptic computation for image enhancement [J]. Neurocomputing 238:1–12

    Article  Google Scholar 

  23. Wang ZB, Ma YD, Gu J (2010) Multi- focus image fusion using PCNN[J]. Pattern Recogn 43(6):2003–2016

    Article  Google Scholar 

  24. Deng XY, Ma YD, Dong M (2016) A new adaptive filtering method for removing salt and pepper noise based on multilayered PCNN[J]. Pattern Recogn Lett 79:8–17

    Article  Google Scholar 

  25. Wang Z, Shuai W, Ying Z (2017) Multi-focus image fusion based on the improved PCNN and guided filter[J]. Neural Process Lett 45(1):75–94

    Article  Google Scholar 

  26. He F, Guo Y, Chao G (2017) Human segmentation of infrared image for mobile robot search[J]. Multimed Tools Appl 12:1–14

    Google Scholar 

  27. Cheng B, Jin L, Li G (2018) Adaptive fusion framework of infrared and visual image using saliency detection and improved dual-channel PCNN in the LNSST domain[J]. Infrared Phys Technol 92:30–43

    Article  Google Scholar 

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Correspondence to Hongna Zhang or Wei Yan.

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Zhang, H., Yan, W., Zhang, C. et al. Research on Image Fusion Algorithm Based on NSST Frequency Division and Improved LSCN. Mobile Netw Appl 26, 1960–1970 (2021). https://doi.org/10.1007/s11036-020-01728-8

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