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Magnetic Resonance and Computer Tomography Image Fusion using Novel Weight Maps Obtained by using Median and Guided Filters

Srikanth M. V.*, Suneel Kumar A.**, Nagasirisha B.***, Venkata Lakshmi T.****

*-** Usha Rama College of Engineering and Technology, Andhra Pradesh, India.

***-**** Gudlavalleru Engineering College, Andhra Pradesh, India.

Periodicity:April - June'2024
DOI : https://doi.org/10.26634/jip.11.2.20715

Abstract

This paper proposes a technique for medical picture fusion based on the guided image filter. It utilizes guided filtering to smooth images, using texture information as guidance for the filter. Weight maps of the detail images are created through pixel weight computation based on image statistics. Finally, the source images are combined using a weighted average combining approach. The effectiveness of the proposed method is evaluated in terms of multiple quantitative image quality assessment factors and compared with several state-of-the-art image fusion algorithms. Experimental results indicate that the suggested approach for image fusion is effective.

Keywords

Median Filter, Guided Filter, Texture Feature, Magnetic Resonance, Computer Tomography, Image Fusion, Image Statistics.

How to Cite this Article?

Srikanth, M. V., Kumar, A. S., Nagasirisha, B., and Lakshmi, T. V. (2024). Magnetic Resonance and Computer Tomography Image Fusion using Novel Weight Maps Obtained by using Median and Guided Filters. i-manager’s Journal on Image Processing, 11(2), 17-26. https://doi.org/10.26634/jip.11.2.20715

References

[1]. Bavirisetti, D. P., Kollu, V., Gang, X., & Dhuli, R. (2017). Fusion of MRI and CT images using guided image filter and image statistics. International Journal of Imaging Systems and Technology, 27(3), 227-237.

[2]. Burt, P. J. (1984). The pyramid as a structure for efficient computation. In Multiresolution Image Processing and Analysis (pp. 6-35). Berlin, Heidelberg: Springer Berlin Heidelberg.

[3]. Burt, P. J. (1992). A gradient pyramid basis for pattern-selective image fusion. Proc. SID 1992 (pp. 467-470).

[4]. Burt, P. J., & Adelson, E. H. (1987). The Laplacian pyramid as a compact image code. In Readings in Computer Vision (pp. 671-679). Morgan Kaufmann.

[5]. Cao, W., Li, B., & Zhang, Y. (2003, December). A remote sensing image fusion method based on PCA transform and wavelet packet transform. In International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003, 2, 976-981. IEEE.

[6]. Chai, Y., Li, H. F., & Guo, M. Y. (2011). Multifocus image fusion scheme based on features of multiscale products and PCNN in lifting stationary wavelet domain. Optics Communications, 284(5), 1146-1158.

[7]. Civicioglu, P., & Besdok, E. (2022). Contrast stretching based pansharpening by using weighted differential evolution algorithm. Expert Systems with Applications, 208, 118144.

[8]. Daubechies, I. (1992). Ten Lectures on Wavelets. Society for industrial and applied mathematics.

[9]. Eskicioglu, A. M., & Fisher, P. S. (1995). Image quality measures and their performance. IEEE Transactions on Communications, 43(12), 2959-2965.

[10]. He, K., Sun, J., & Tang, X. (2012). Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(6), 1397-1409.

[11]. Jiang, Y., & Wang, M. (2014). Image fusion using multiscale edge-preserving decomposition based on weighted least squares filter. IET Image Processing, 8(3), 183-190.

[12]. Li, S., Kang, X., & Hu, J. (2013). Image fusion with guided filtering. IEEE Transactions on Image Processing, 22(7), 2864-2875.

[13]. Li, S., Kang, X., Fang, L., Hu, J., & Yin, H. (2017). Pixel-level image fusion: A survey of the state of the art. Information Fusion, 33, 100-112.

[14]. Ma, J., Ma, Y., & Li, C. (2019). Infrared and visible image fusion methods and applications: A survey. Information Fusion, 45, 153-178.

[15]. Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693.

[16]. Meher, B., Agrawal, S., Panda, R., & Abraham, A. (2019). A survey on region based image fusion methods. Information Fusion, 48, 119-132.

[17]. Meyer, Y. (1992). Wavelets and Operators. Cambridge university press.

[18]. Olkkonen, H., & Pesola, P. (1996). Gaussian pyramid wavelet transform for multiresolution analysis of images. Graphical Models and Image Processing, 58(4), 394-398.

[19]. Pajares, G., & De La Cruz, J. M. (2004). A wavelet-based image fusion tutorial. Pattern Recognition, 37(9), 1855-1872.

[20]. Patil, S. M. (2016). Image fusion using wavelet transform. International Journal of Engineering and Advanced Technology, 5(4), 66-69.

[21]. Rockinger, O. (1997, October). Image sequence fusion using a shift-invariant wavelet transform. In Proceedings of International Conference on Image Processing, 3, 288-291. IEEE.

[22]. Rockinger, O., & Fechner, T. (1998, July). Pixel-level image fusion: the case of image sequences. In Signal Processing, Sensor Fusion, and Target Recognition VII, 3374, 378-388. SPIE.

[23]. Schiller, P. H. (1991). The color-opponent and broad-band channels of the primate visual system. In from Pigments to Perception (pp. 127–132).

[24]. Schiller, P. H. (1992). The ON and OFF channels of the visual system. Trends in Neurosciences, 15(3), 86-92.

[25]. Srikanth, M. V., Prasad, V. V. K. D. V., & Prasad, K. S. (2023a). Brain tumor detection through modified optimization algorithm by region-based image fusion. ECTI Transactions on Computer and Information Technology (ECTI-CIT), 17(1), 117-127.

[26]. Srikanth, M. V., Prasad, V. V. K. D. V., & Prasad, K. S. (2023b). Application of novel improved firefly algorithm for image fusion to detect brain tumor. ECTI Transactions on Computer and Information Technology (ECTI-CIT), 17(2), 292-307.

[27]. Tang, L., Yuan, J., & Ma, J. (2022). Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network. Information Fusion, 82, 28-42.

[28]. Wang, L., Li, B., & Tian, L. F. (2014). Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients. Information Fusion, 19, 20-28.

[29]. Waxman, A. M., Aguilar, M., Fay, D. A., Ireland, D. B., & Racamato, J. P. (1998). Solid-state color night vision: Fusion of low-light visible and thermal infrared imagery. Lincoln Laboratory Journal, 11(1), 41-60.

[30]. Waxman, A. M., Seibert, M. C., Gove, A., Fay, D. A., Bernardon, A. M., Lazott, C., & Cunningham, R. K. (1995). Neural processing of targets in visible, multispectral IR and SAR imagery. Neural Networks, 8(7-8), 1029-1051.

[31]. Yan, X., Qin, H., Li, J., Zhou, H., & Yang, T. (2016). Multi-focus image fusion using a guided-filter-based difference image. Applied Optics, 55(9), 2230-2239.

[32]. Zhang, H., Xu, H., Tian, X., Jiang, J., & Ma, J. (2021). Image fusion meets deep learning: A survey and perspective. Information Fusion, 76, 323-336.

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