Application of Novel Improved Firefly Algorithm for Image Fusion To Detect Brain Tumor
Article Sidebar
Main Article Content
Abstract
Image fusion is a method for merging vital elements from two images for higher visual appeal and a better understanding of the data contained in source images. Application areas for image fusion include the military, space exploration, and healthcare. In this study, an attempt is made to use the successful fusion of two images to diagnose brain diseases such as neoplastic tumors, cerebrovascular tumors, Alzheimer's tumors, fatal tumors, and sarcoma tumors. In this case, two images are fused after being segmented with the optimal thresholds obtained using a novel improved Firefly (pFA) algorithm with a maximization problem employing fuzzy entropy as the objective function. These segmented images are then applied to Scale Invariant Feature Transform (SIFT) for additional deep feature extraction. Improved features for better fusion can be obtained by this cascaded feature extraction. Finally, fusion rules for the source images are created using interval type-2 fuzzy (IT2FL). On various benchmark image fusion data sets, the uniqueness of the proposed work is tested, and it is found to perform better in terms of Peak Signal Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Edge strength-based Similarity Measure (QABF ), and Mutual information (MI).
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Y. Liu, X. Chen, Z. Wang, Z. J. Wang, R. K. Ward and X. Wang, “Deep learning for pixellevel image fusion: Recent advances and future prospects,” Information Fusion, vol. 42, pp. 158-173, 2018.
B. Rajalingam and R. Priya, “Hybrid multimodality medical image fusion technique for feature enhancement in medical diagnosis,” International Journal of Engineering Science Invention (IJESI), vol. 2, pp. 52-60, 2018.
J. Ma, Y. Ma and C. Li, “Infrared and visible image fusion methods and applications: A survey,” Information Fusion, vol. 45, pp. 153-178, 2019.
L. Tan, Y. Chen and W. Zhang, “Multi-focus Image Fusion Method based on Wavelet Transform,” in Journal of Physics: Conference Series, vol. 1284, no. 1, p. 012068, August 2019.
R. Singh and A. Khare, “Fusion of multimodal medical images using Daubechies complex wavelet transform–A multiresolution approach,” Information Fusion, vol. 19, pp. 49-60, 2014.
F. Vollnhals, J. N. Audinot, T. Wirtz, M. Mercier-Bonin, I. Fourquaux, B. Schroeppel,U. Kraushaar, V. Lev-Ram, M. H. Ellisman and S. Eswara, “Correlative microscopy combining secondary ion mass spectrometry and electron microscopy: comparison of intensity–hue–saturation and Laplacian pyramid methods for image fusion,” Analytical chemistry, vol. 89, no. 20, pp. 10702-10710, 2017.
K. Amolins, Y. Zhang and P. Dare, “Wavelet based image fusion techniques—An introduction, review and comparison,” ISPRS Journal of photogrammetry and Remote Sensing, vol. 62, no. 4, pp. 249-263, 2007.
J. Chen, X. Li, L. Luo, X. Mei and J. Ma, “Infrared and visible image fusion based on targetenhanced multiscale transform decomposition,” Information Sciences, vol. 508, pp. 64-78, 2020.
L. Wang, B. Li and L. F. Tian, “Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shiftinvariant shearlet coefficients,” Information Fusion, vol. 19, pp. 20-28, 2014.
H. Li, Y. Chai and Z. Li, “Multi-focus image fusion based on nonsubsampled contourlet transform and focused regions detection,” Optik, vol. 124, no.1, pp. 40-51, 2013.
B.Sun,W.Zhu,C.Luo,K.Hu,Y.Huand J. Gao, “Fusion of noisy images based on joint distribution model in dual-tree complex wavelet domain,” International Journal of Imaging Systems and Technology, vol. 29, no. 1, pp. 29-41, 2019.
B. Yang and S. Li, “Pixel-level image fusion with simultaneous orthogonal matching pursuit,” Information fusion, vol. 13, no.1, pp. 10-19, 2012.
J. J. Lewis, R. J. O’Callaghan, S. G. Nikolov, D. R. Bull and N. Canagarajah, “Pixel-and regionbased image fusion with complex wavelets. Information fusion, 8(2), 119-130, 2007.
E. J. Cand`es and D. L. Donoho, “Curvelets and curvilinear integrals,” Journal of Approximation Theory, vol. 113, no. 1, pp. 59-90, 2001.
N. Kaur, M. Bahl and H. Kaur, “Review on: image fusion using wavelet and curvelet transform,” International Journal of Computer Science and Information Technologies (IJCSIT), vol.5., no. 2, pp. 2467-2470, 2014.
H. Li, Y. Chai and Z. Li, “Multi-focus image fusion based on nonsubsampled contourlet transform and focused regions detection,” Optik, vol. 124, no.1, pp. 40-51, 2013.
Q. Zhang and X. Maldague, “An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing, Infrared Physics & Technology, vol. 74, pp. 11-20, 2016.
L. Tang, J. Qian, L. Li, J. Hu and X. Wu, “Multimodal medical image fusion based on discrete T chebichef moments and pulse coupled neural network,” International Journal of Imaging Systems and Technology, vol. 27, no. 1, pp. 57-65, 2017.
Q. Miao, R. Liu, Y. Wang, J. Song, Y. Quan and Y. Li, “Remote sensing image fusion based on shearlet and genetic algorithm,” International Journal of Bio-Inspired Computation, vol. 9, no.4, pp. 240-250, 2017.
K. P. Upla, M. V. Joshi and P. P. Gajjar, “An Edge Preserving Multiresolution Fusion: Use of Contourlet Transform and MRF Prior,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 6, pp. 3210-3220, June 2015.
L. Wang, B. Li and L. F. Tian, “Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shiftinvariant shearlet coefficients,” Information Fusion, vol. 19, pp. 20-28, 2014.
K.J.Xia,H.S.YinandJ.Q.Wang,“A novel improved deep convolutional neural network model for medical image fusion,” Cluster Computing, vol. 22, no. 1, pp. 1515-1527, 2019.
D.F.Wu,W.He,S.Lin,B.HanandC. S. Zee, “Using real-time fusion imaging constructed from contrast-enhanced ultrasonography and magnetic resonance imaging for highgrade glioma in neurosurgery,” World neurosurgery, vol. 125, pp. e98-e109,2019.
J. Ma, W. Yu, P. Liang, C. Li and J. Jiang, “FusionGAN: A generative adversarial network for infrared and visible image fusion. Information Fusion, vol. 48, pp. 11-26, 2019.
R. Shen, I. Cheng and A. Basu, “Cross-scale coefficient selection for volumetric medical image fusion,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 4, pp. 1069-1079, 2012.
U. N. Dulhare and A. M. Khaleed, “Taj-Shanvi Framework for Image Fusion Using Guided Filters,” Data Management, Analytics and Innovation, Springer, Singapore, pp. 419-427, 2020.
X. S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press, 2008.
J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proceedings of ICNN’95 -International Conference on Neural Networks, Perth, WA, Australia, vol.4, pp. 1942-1948, 1995.
D. T. Pham and M. Castellani, “The Bees Algorithm: Modelling Foraging Behaviour to Solve Continuous Optimization Problems,” Proc. Ime che, vol. 223, no. 12, pp. 2919-2938, 2009.
D. G. Lowe, “Object recognition from local scale-invariant features,” Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, vol.2, pp. 1150-1157, 1999.
Y. He, G. Deng, Y. Wang, L. Wei, J. Yang, X. Li and Y. Zhang, “Optimization of SIFT algorithm for fast-image feature extraction in linescanning ophthalmoscope,” Optik, vol. 152, pp. 21-28, 2018.
L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
S. Pare, A. K. Bhandari, A. Kumar, G. K. Singh, “A new technique for multilevel color image thresholding based on modified fuzzy entropy and L ́evy flight firefly algorithm,” Computers & Electrical Engineering vol. 70, pp. 476–495, 2018.
A. H. Gandomi, X. S. Yang and A. H. Alavi, “Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems,” Engineering with Computers, vol. 29, no. 1, pp. 17–35, 2013.
E. E. Hofmann,, A. E. Haskell,, J. M. Klinck and C. M. Lascara, “Lagrangian modelling studies of Antarctic krill (Euphausia superba) swarm formation,” ICES Journal of Marine Science, vol. 61, no. 4, pp. 617–631, 2004.
H. J. Price, “Swimming behavior of krill in response to algal patches: A mesocosm study,” Limnology and Oceanography, vol. 34, no. 4, pp. 649–659, 1989.
C. Harris and M. Stephens, “A Combined Corner and Edge Detector,” Alvey Vision Conference 15, Plessey Research Roke Manor, United Kingdom, pp. 147-152, 1988.
Qilian Liang and J. M. Mendel, “Interval type-2 fuzzy logic systems: theory and design,” in IEEE Transactions on Fuzzy Systems, vol. 8, no. 5, pp. 535-550, Oct. 2000.
J. M. Mendel and R. I. B. John, “Type-2 fuzzy sets made simple,” in IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 117-127, April 2002.
Y. Yang, Y. Que, S. Huang and P. Lin, “Multimodal Sensor Medical Image Fusion Based on Type-2 Fuzzy Logic in NSCT Domain,” in IEEE Sensors Journal, vol. 16, no. 10, pp. 3735-3745, May 15, 2016.
L. Li and D. Li, “Fuzzy entropy image segmentation based on particle swarm optimization,” Progress in Natural Science, vol. 18, no. 9, pp. 1167–1171, 2008.
M. V. Srikanth, V. V. K. D. V. Prasad and K. S. Prasad, “An Improved Firefly AlgorithmBased 2-D Image Thresholding for Brain Image Fusion,” International Journal of Cognitive Informatics and Natural Intelligence, vol. 14 , no. 3 ,July-September 2020.
M. V. Srikanth, V. V. K. D. V. Prasad and K. S. Prasad, “Optimal Threshold Based Brain Image Fusion for Brain Cancer Detection using Firefly algorithm,” International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 2, pp. 2277-3878, July 2019.