Image fusion (IF) attracts the researchers in the areas of the medical industry as valuable information could be afforded through the fusion of images that enable the clinical decisions to remain effective. With the aim to render an effective image fusion, this paper concentrates on the Bayesian fusion approach, which is tuned optimally using the proposed Fractional Bird Swarm Optimization (Fractional-BSA). The medical image fusion is progressed using the MRI brain image taken from the BRATS database, and the source images of multimodalities are fused effectively to present an information-rich fused image. The source images are subjected to the Haar wavelet transform, and the Bayesian fusion is performed using the Bayesian parameter, which is determined optimally using the proposed Fractional-BSA optimization. The proposed optimization is the integration of the fractional theory in the standard Bird Swarm Optimization (BSA), which improves the effectiveness of image fusion. Unlike any other existing methods, the proposed Fractional-BSA-based Bayesian Fusion approach renders a good quality and complex-free fusion experience. The analysis reveals that the method outperformed the existing methods with maximal mutual information, maximal peak signal-to-noise ratio (PSNR), minimal root mean square error (RMSE) of 1.5665, 44.0857 dB, and 5.4840, respectively.
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Santosh Kumar BP, Venkata Ramanaiah K (2019) An efficient hybrid optimization algorithm for image compression. Multimed Res 2(4):1–11
Daga B, Bhute A, Ghatol A (2011) Implementation of parallel image processing using NVIDIA GPU framework. In: Proceedings of the international conference on advances in computing, communication and control. Springer, Berlin, pp 457–464
Daga BS, Ghatol AA, Thakare VM (2017) Silhouette based human fall detection using multimodal classifiers for content based video retrieval systems. Proc Int Conf Intell Comput Instrum Control Technol (ICICICT):1409–1416
L Wang, B Li and L F Tian, (2013) A novel multi-modal medical image fusion method based on shift-invariant shearlet transforms, The Imaging Science Journal. 61(7):529–540
Xu X, Shana D, Wang G, Jiang X (2016) Multimodal medical image fusion using PCNN optimized by the QPSO algorithm. Appl Soft Comput 46:588–595
Bayrakdar ME (2019) Priority based health data monitoring with IEEE 802.11af technology in wireless medical sensor networks. Med Biol Eng Comput 57(12):2757–2769
Ebenezer D, Anithaa J, Kamaleshwaranb KK, Rani I (2017) Optimum spectrum mask based medical image fusion using Gray Wolf Optimization. Biomed Signal Process Control 34:36–43
Li S, Kwok JT, Wang Y (2001) Combination of images with diverse focuses using the spatial frequency. Inform Fusion 2(3):169–176
Zong J-j, Qiua T-s (2017) Medical image fusion based on sparse representation of classified image patches. Biomed Signal Process Control 34:195–205
Majumdar S, Bharadwaj J (2014) Feature level fusion of multimodal images using Haar lifting wavelet transform. World AcadSci Eng Technol Int J Comput Inform Eng 8(6):1023–1027
Wang A, Sun H, Guan Y (2006) The application of wavelet transform to multimodality medical image fusion. Proc Int Conf Networking Sens Control IEEE Syst Man Cybern Soc IEEE 4:270–274
Amolins K, Zhang Y (2007) Wavelet based image fusion techniques — an introduction, review and comparison. ISPRS J Photogramm Remote Sens 62:249–263
Le Pennec E, Mallat S (2005) Sparse geometric image representation with bandelets. IEEE Trans Image Process 14:423–438
Gaurav Bhatnagar QM, Wu J, Liu Z (2013) Directive contrast based multimodal medical image fusion in NSCT domain. IEEE Trans Multimedia 15(5):1014–1024
Toet A (1989) A morphological pyramidal image decomposition. Pattern Recognit Lett 9(4):255–261
Singh S, Anand RS, Gupta D (2018) CT and MR image information fusion scheme using a cascaded framework in ripplet and NSST domain. IET Image Process 12(5):696–707
Li H, Manjunath B, Mitra SK (1995) Multisensor image fusion using the wavelet transform. Graph Models Image Process 57(3):235–245
Das S, Kundu MK (2012) NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency. Med Biol Eng Comput 50(10):1105–1114
Donoho DL (April 2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306
Yang B, Li S (April 2010) Multifocus image fusion and restoration with sparse representation. IEEE Trans Instrum Meas 59(4):884–892
Bhaladhare PR, Jinwala DC (2016) A Clustering Approach Using Fractional Calculus-Bacterial Foraging Optimization Algorithm for k-Anonymization in Privacy Preserving Data Mining, International Journal of Information Security and Privacy (IJISP), IGI Global, 10(1):45–65.
Mengab X-B, Gaoc XZ, Lude L, Liub Y, Zhanga H (2016) A new bio-inspired optimisation algorithm: Bird Swarm Algorithm. J Exp Theor Artif Intell 28(4):673–687
Venkatrao PH, Damodar SS (2018) HWFusion: Holoentropy and SP-Whale optimisation-based fusion model for magnetic resonance imaging multimodal image fusion. IET Image Process 12(4):572–581
Bavirisetti DP, Dhuli R (2016) Fusion of infrared and visible sensor images based on anisotropic diffusion and Karhunen-Loeve transform. IEEE Sensors J 16(1):203–209
Bhateja V, Patel H, Krishn A, Sahu A, Lay-Ekuakille A (2015) Multimodal medical image sensor fusion framework using cascade of wavelet and contourlet transform domains. IEEE Sensors J 15(12):6783–6790
El-Zahraa F, El-Gamal A, Elmogy M, Atwan A (2016) Current trends in medical image registration and fusion. Egypt Informatics J 17(1):99–124
Tedmori S, Al-Najdawi N (2014) Image cryptographic algorithm based on the Haar wavelet transform. Inform Sci 269:21–34
Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, et al. (2015) The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging 34(10):993–2024
Multimodal Brain Tumor Segmentation Challenge database taken from https://www.med.upenn.edu/sbia/brats2018/data.html. Accessed on September 2018
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Bhardwaj, J., Nayak, A. Haar wavelet transform–based optimal Bayesian method for medical image fusion. Med Biol Eng Comput (2020). https://doi.org/10.1007/s11517-020-02209-6
- Haar wavelet
- Fractional theory
- MRI image
- Image fusion