Abstract
In this work, the idea of using shift-invariant discrete wavelet transform and sparse fusion-based magnetic resonance and computed tomography image fusion technique is presented. Source images from different modalities are split into different scale components along with high-level components using shift-invariant discrete wavelet transform. Approximation components are fused using sparse fusion. Different computed weights are joined to be used with source images to get the required fused image as output. Metrics-based visual and quantitative results clearly indicate the worth of proposed new approach in comparison with other existing fusion strategies.
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Barra, V., Boire, J.Y.: A general framework for the fusion of anatomical and functional medical images. NeuroImage 13(3), 410–424 (2001)
Polo, A., Cattani, F., Vavassori, A., Origgi, D., Villa, G., Marsiglia, H., Bellomi, M., Tosi, G., De Cobelli, O., Orecchia, R.: MR and CT image fusion for postimplant analysis in permanent prostate seed implants. Int. J. Radiat. Oncol. Biol. Phys. 60(5), 1572–1579 (2004)
Singh, R., Khare, A.: Multiscale medical image fusion in wavelet domain. Sci. World J. 2013, 1–10 (2013). http://doi.org/10.1155/2013/521034
Kavalcova, L., Skaba, R., Kyncl, M., Rouskova, B., Prochazka, A.: The diagnostic value of MRI fistulogram and MRI distal colostogram in patients with anorectal malformations. J. Pediatr. Surg. 48(8), 1806–1809 (2013)
Fattal, R., Agrawala, M., Rusinkiewicz, S.: Multiscale shape and detail enhancement from multi-light image collections. ACM Trans. Graph. 26(3), 51 (2007)
Langari, B., Vaseghi, S., Prochazka, A., Vaziri, B., Aria, F.T.: Edge-guided image gap interpolation using multi-scale transformation. IEEE Trans. Image Process. 25(9), 4394–4405 (2016)
Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583), 607 (1996)
Xu, Z.: Medical image fusion using multi-level local extrema. Inf. Fusion 19, 38–48 (2014)
Hill, P.R., Canagarajah, C.N., Bull, D.R.: Image fusion using complex wavelets. In: BMVC, pp. 1–10 (2002)
Jameel, A., Ghafoor, A., Riaz, M.M.: Improved guided image fusion for magnetic resonance and computed tomography imaging. Sci. World J. 2014, 1–7 (2014). http://doi.org/10.1155/2014/695752
Naidu, V.P.S.: Discrete cosine transform-based image fusion. Defence Sci. J. 60(1), 48–54 (2010)
Shah, P., Srikanth, T.V., Merchant, S.N., Desai, U.B.: Multimodal image/video fusion rule using generalized pixel significance based on statistical properties of the neighborhood. Signal Image Video Process. 8(4), 723–738 (2014)
Shen, R., Cheng, I., Basu, A.: Cross-scale coefficient selection for volumetric medical image fusion. IEEE Trans. Biomed. Eng. 60(4), 1069–1079 (2013)
Gambhir, D., Manchanda, M.: Waveatom transform-based multimodal medical image fusion. Signal Image Video Process. 13(2), 321–329 (2019)
Bhatnagar, G., Wu, Q.J., Liu, Z.: Human visual system inspired multi-modal medical image fusion framework. Expert Syst. Appl. 40(5), 1708–1720 (2013)
Yang, Y., Park, D.S., Huang, S., Rao, N.: Medical image fusion via an effective wavelet-based approach. EURASIP J. Adv. Signal Process. 2010, 44 (2010)
Ramlal, S.D., Sachdeva, J., Ahuja, C.K., Khandelwal, N.: Multimodal medical image fusion using non-subsampled shearlet transform and pulse coupled neural network incorporated with morphological gradient. Signal Image Video Process. 12(8), 1479–1487 (2018)
Wang, L., Li, B., Tian, L.F.: Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients. Inf. Fusion 19, 20–28 (2014)
Ch, M.M.I., Riaz, M.M., Iltaf, N., Ghafoor, A., Sadiq, M.A.: Magnetic resonance and computed tomography image fusion using saliency map and cross bilateral filter. Signal Image Video Process. 13, 1157–1164 (2019). http://doi.org/10.1007/s11760-019-01459-8
Yin, M., Liu, X., Liu, Y., Chen, X.: Medical image fusion with parameter-adaptive pulse coupled neural network in nonsubsampled Shearlet transform domain. IEEE Trans. Instrum. Meas. 99, 1–16 (2018)
Bavirisetti, D.P., Kollu, V., Gang, X., Dhuli, R.: Fusion of MRI and CT images using guided image filter and image statistics. Int. J. Imaging Syst. Technol. 27(3), 227–237 (2017)
Zhan, K., Xie, Y., Wang, H., Min, Y.: Fast filtering image fusion. J. Electron. Imaging 26(6), 063004 (2017)
Liu, Y., Chen, X., Cheng, J., Peng, H.: A medical image fusion method based on convolutional neural networks. In: 2017 20th International Conference on Information Fusion (Fusion). IEEE, pp. 1–7 (2017)
Vijayarajan, R., Muttan, S.: Discrete wavelet transform based principal component averaging fusion for medical images. AEU Int. J. Electron. Commun. 69(6), 896–902 (2015)
Li, S., Kang, X., Hu, J.: Image fusion with guided filtering. IEEE Trans. Image process. 22(7), 2864–2875 (2013)
Rockinger, O.: Image sequence fusion using a shift-invariant wavelet transform. In: Proceedings of International Conference on Image Processing, Vol. 3, pp. 288–291. IEEE (1997)
Liu, Y., Liu, S., Wang, Z.: A general framework for image fusion based on multi-scale transform and sparse representation. Inf. Fusion 1(24), 147–64 (2015)
Mallat, S.G., Zhang, Z.: Matching pursuits with time–frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397–415 (1993)
Harvard Image Database. http://www.med.harvard.edu/aanlib. Last Accessed 16 Mar 2021
Yang, C., Zhang, J.Q., Wang, X.R., Liu, X.: A novel similarity based quality metric for image fusion. Inf. Fusion 9(2), 156–160 (2008)
Haghighat, M., Razian, M.A.: Fast-FMI: non-reference image fusion metric. In: 2014 IEEE 8th International Conference on Application of Information and Communication Technologies (AICT), pp. 1–3. IEEE (2014)
He, C., Liu, Q., Li, H., Wang, H.: Multimodal medical image fusion based on IHS and PCA. Procedia Eng. 1(7), 280–285 (2010)
Liu, Z., Yin, H., Chai, Y., Yang, S.X.: A novel approach for multimodal medical image fusion. Expert Syst. Appl. 41(16), 7425–7435 (2014)
Bhateja, V., Patel, H., Krishn, A., Sahu, A., Lay-Ekuakille, A.: Multimodal medical image sensor fusion framework using cascade of wavelet and contourlet transform domains. IEEE Sens. J. 15(12), 6783–6790 (2015)
Manchanda, M., Sharma, R.: A novel method of multimodal medical image fusion using fuzzy transform. J. Vis. Commun. Image Represent. 1(40), 197–217 (2016)
Nambiar, R., Desai, U., Shetty, V.: Medical image fusion analysis using curvelet transform. In: Proceedings of the International Conference on Advances in Computing, Communication and Information Science (ACCIS-14), Kerala, India, pp. 27–29 (2014)
Srivastava, R., Prakash, O., Khare, A.: Local energy-based multimodal medical image fusion in curvelet domain. IET Comput. Vis. 10(6), 513–527 (2016)
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Ch, M.M.I., Riaz, M.M., Iltaf, N. et al. Shift-invariant discrete wavelet transform-based sparse fusion of medical images. SIViP 17, 881–889 (2023). https://doi.org/10.1007/s11760-021-01998-z
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DOI: https://doi.org/10.1007/s11760-021-01998-z