Abstract
Multi-modal medical image fusion plays a significant role in clinical applications like noninvasive diagnosis and image-guided surgery. However, designing an efficient image fusion technique is still a challenging task. In this paper, we propose an improved multi-modal medical image fusion method to enhance the visual quality and contrast of the fused image. To achieve this work, the registered source images are firstly decomposed into low-frequency (LF) and several high-frequency (HF) sub-images via non-subsampled shearlet transform (NSST). Afterward, LF sub-images are combined using the proposed weight local features fusion rule based on local energy and standard deviation, while HF sub-images are fused based on the novel sum-modified-laplacien (NSML) technique. Finally, inversed NSST is applied to reconstruct the fused image. Furthermore, the proposed method is extended to color multi-modal image fusion that effectively restrains color distortion and enhances spatial and spectral resolutions. To evaluate the performance, various experiments conducted on different datasets of gray-scale and color images. Experimental results show that the proposed scheme achieves better performance than other state-of-art proposed algorithms in both visual effects and objective criteria.
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References
Du, F., et al.: An overview of multi-modal medical image fusion. Neurocomputing 215, 3–20 (2016)
Li, S., et al.: Pixel-level image fusion: a survey of the state of the art. Inform. Fusion 33, 100–112 (2017)
Shahdoosti, H.R., Tabatabaei, Z.: MRI and PET/SPECT image fusion at feature level using ant colony based segmentation. Biomed. Sign. Process. Control 47, 63–74 (2019)
Mangai, U.G., et al.: A survey of decision fusion and feature fusion strategies for pattern classification. IETE Tech. Rev. 27(4), 293–307 (2010)
Reena Benjamin, J., Jayasree, T.: Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms. Int. J. Comput. Assist. Radiol. Surg. 13(2), 229–240 (2017). https://doi.org/10.1007/s11548-017-1692-4
He, C., et al.: Multimodal medical image fusion based on IHS and PCA. Procedia Eng. 7, 280–285 (2010)
Wang, W., Chang, F.: A multi-focus image fusion method based on laplacian pyramid. JCP 6(12), 2559–2566 (2011)
Ali, F.E., et al.: Curvelet fusion of MR and CT images. Prog. Electromagn. Res. 3, 215–224 (2008)
Yang, L., Guo, B.L., Ni, W.: Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform. Neurocomputing 72(1–3), 203–211 (2008)
Miao, Q.G., Shi, C., Xu, P.F., Yang, M., Shi, Y.B.: A novel algorithm of image fusion using shearlets. Opt. Commun. 284(6), 1540–1547 (2011)
Easley, G., Labate, D., Lim, W.Q.: Sparse directional image representations using the discrete shearlet transform. Appl. Comput. Harmonic Anal. 25(1), 25–46 (2008)
Manchanda, M., Sharma, R.: An improved multimodal medical image fusion algorithm based on fuzzy transform. J. Vis. Commun. Image Represent. 51, 76–94 (2018)
Ouerghi, H., Mourali, O., Zagrouba, E.: Multimodal medical image fusion using modified PCNN based on linking strength estimation by MSVD transform. Int. J. Comput. Commun. Eng. 6(3), 201–211 (2017)
Ouerghi, H., Mourali, O., Zagrouba, E.: 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 (2018)
Ganasala, P., Kumar, V.: Feature-motivated simplified adaptive PCNN-based medical image fusion algorithm in NSST domain. J. Digit. Imaging 29(1), 73–85 (2016)
Zhang, Y., et al.: IFCNN: A general image fusion framework based on convolutional neural network. Inform. Fusion 54, 99–118 (2020)
Huang, W., Jing, Z.: Evaluation of focus measures in multi-focus image fusion. Pattern Recogn. Lett. 28(4), 493–500 (2007)
Yin, M., et al.: A novel image fusion algorithm based on nonsubsampled shearlet transform. Optik 125(10), 2274–2282 (2014)
Ullah, H., et al.: 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 (2020)
Jagalingam, P., Hegde, A.V.: A review of quality metrics for fused image. Aquatic Procedia 4. Icwrcoe 133–142 (2015)
Mohammed, A., Nisha, A.KL., Sathidevi, P.S.: A novel medical image fusion scheme employing sparse representation and dual PCNN in the NSCT domain. In: IEEE Region 10 Conference (TENCON). pp. 2147–2151. IEEE, Singapore (2016)
Liu, X., Mei, W., Du, H.: Multi-modality medical image fusion based on image decomposition framework and nonsubsampled shearlet transform. Biomed. Signal Process. Control 40, 343–350 (2018)
Du, J., Li, W., Xiao, B.: Anatomical-functional image fusion by information of interest in local Laplacian filtering domain. IEEE Trans. Image Process. 26(12), 5855–5866 (2017)
Lotan, E., et al.: State of the art: Machine learning applications in glioma imaging. Am. J. Roentgenol. 212(1), 26–37 (2019)
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Ouerghi, H., Mourali, O., Zagrouba, E. (2020). Multi-modal Image Fusion Based on Weight Local Features and Novel Sum-Modified-Laplacian in Non-subsampled Shearlet Transform Domain. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_13
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