Segmentation-Based Fusion of CT and MR Images

F. Zhao, G. Xu, and W. Zhao, “Ct and mr image fusion based on adaptive structure decomposition,” IEEE Access, vol. 7, pp. 44 002–44 009, 2019.

S. Li, X. Kang, L. Fang, J. Hu, and H. Yin, “Pixel-level image fusion: A survey of the state of the art,” information Fusion, vol. 33, pp. 100–112, 2017.

N. Jain, A. Yadav, Y. Kumar Sariya, and A. Balodi, “Analysis of discrete wavelet transforms variants for the fusion of ct and mri images,” The Open Biomedical Engineering Journal, vol. 15, no. 1, 2021.

H. Li, B. Manjunath, and S. K. Mitra, “Multisensor image fusion using the wavelet transform,” Graphical models and image processing, vol. 57, no. 3, pp. 235–245, 1995.

Article  Google Scholar 

C. Asha, S. Lal, V. P. Gurupur, and P. P. Saxena, “Multi-modal medical image fusion with adaptive weighted combination of nsst bands using chaotic grey wolf optimization,” IEEE Access, vol. 7, pp. 40 782–40 796, 2019.

E. Jabason, M. O. Ahmad, and M. Swamy, “Multimodal neuroimaging fusion in nonsubsampled shearlet domain using location-scale distribution by maximizing the high frequency subband energy,” IEEE Access, vol. 7, pp. 97 865–97 886, 2019.

M. Yin, X. Liu, Y. Liu, and X. Chen, “Medical image fusion with parameter-adaptive pulse coupled neural network in nonsubsampled shearlet transform domain,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 1, pp. 49–64, 2018.

Article  Google Scholar 

R. Srivastava, O. Prakash, and A. Khare, “Local energy-based multimodal medical image fusion in curvelet domain,” IET computer vision, vol. 10, no. 6, pp. 513–527, 2016.

Article  Google Scholar 

H. Zhang, X. Ma, and Y. Tian, “An image fusion method based on curvelet transform and guided filter enhancement,” Mathematical Problems in Engineering, vol. 2020, 2020.

F. E. Ali, I. El-Dokany, A. Saad, and F. Abd El-Samie, “A curvelet transform approach for the fusion of mr and ct images,” Journal of Modern Optics, vol. 57, no. 4, pp. 273–286, 2010.

C. Pei, K. Fan, and W. Wang, “Two-scale multimodal medical image fusion based on guided filtering and sparse representation,” IEEE Access, vol. 8, pp. 140 216–140 233, 2020.

S. Singh and D. Gupta, “Detail enhanced feature-level medical image fusion in decorrelating decomposition domain,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–9, 2020.

Google Scholar 

Y. Yang, S. Cao, S. Huang, and W. Wan, “Multimodal medical image fusion based on weighted local energy matching measurement and improved spatial frequency,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–16, 2020.

Article  Google Scholar 

J. Du, W. Li, and B. Xiao, “Anatomical-functional image fusion by information of interest in local laplacian filtering domain,” IEEE Transactions on Image Processing, vol. 26, no. 12, pp. 5855–5866, 2017.

Article  PubMed  Google Scholar 

Y. Hou, Z. Li, P. Wang, and W. Li, “Skeleton optical spectra-based action recognition using convolutional neural networks,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 3, pp. 807–811, 2016.

Article  Google Scholar 

Y. Li, J. Zhao, Z. Lv, and J. Li, “Medical image fusion method by deep learning,” International Journal of Cognitive Computing in Engineering, vol. 2, pp. 21–29, 2021.

Article  Google Scholar 

D. Ye, J. Y. H. Fuh, Y. Zhang, G. S. Hong, and K. Zhu, “In situ monitoring of selective laser melting using plume and spatter signatures by deep belief networks,” ISA transactions, vol. 81, pp. 96–104, 2018.

Article  PubMed  Google Scholar 

S. Saadat, M. R. Pickering, D. Perriman, J. M. Scarvell, and P. N. Smith, “Fast and robust multi-modal image registration for 3d knee kinematics,” in 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA).   IEEE, 2017, pp. 1–5.

J. Schlemper, J. Caballero, J. V. Hajnal, A. Price, and D. Rueckert, “A deep cascade of convolutional neural networks for mr image reconstruction,” in International conference on information processing in medical imaging.   Springer, 2017, pp. 647–658.

M. M. Mijwil, R. Doshi, K. K. Hiran, O. J. Unogwu, and I. Bala, “Mobilenetv1-based deep learning model for accurate brain tumor classification,” Mesopotamian Journal of Computer Science, vol. 2023, pp. 32–41, 2023.

Article  Google Scholar 

K. Aggarwal, M. M. Mijwil, A.-H. Al-Mistarehi, S. Alomari, M. Gök, A. M. Z. Alaabdin, S. H. Abdulrhman et al., “Has the future started? the current growth of artificial intelligence, machine learning, and deep learning,” Iraqi Journal for Computer Science and Mathematics, vol. 3, no. 1, pp. 115–123, 2022.

Google Scholar 

Y. Liu, X. Chen, Z. Wang, Z. J. Wang, R. K. Ward, and X. Wang, “Deep learning for pixel-level image fusion: Recent advances and future prospects,” Information Fusion, vol. 42, pp. 158–173, 2018.

Article  Google Scholar 

Y. Liu, X. Chen, R. K. Ward, and Z. J. Wang, “Image fusion with convolutional sparse representation,” IEEE signal processing letters, vol. 23, no. 12, pp. 1882–1886, 2016.

Article  Google Scholar 

Y. Liu, X. Chen, R. K Ward, and Z. J. Wang, “Medical image fusion via convolutional sparsity based morphological component analysis,” IEEE Signal Processing Letters, vol. 26, no. 3, pp. 485–489, 2019.

Article  Google Scholar 

B. Wohlberg, “Efficient algorithms for convolutional sparse representations,” IEEE Transactions on Image Processing, vol. 25, no. 1, pp. 301–315, 2015.

Article  PubMed  Google Scholar 

Y. Liu, X. Chen, H. Peng, and Z. Wang, “Multi-focus image fusion with a deep convolutional neural network,” Information Fusion, vol. 36, pp. 191–207, 2017.

Article  Google Scholar 

S. Ma, M. Chen, J. Wu, Y. Wang, B. Jia, and Y. Jiang, “High-voltage circuit breaker fault diagnosis using a hybrid feature transformation approach based on random forest and stacked autoencoder,” IEEE Transactions on Industrial Electronics, vol. 66, no. 12, pp. 9777–9788, 2018.

Article  Google Scholar 

E. P. Ijjina et al., “Classification of human actions using pose-based features and stacked auto encoder,” Pattern Recognition Letters, vol. 83, pp. 268–277, 2016.

Article  Google Scholar 

H. Chen, L. Jiao, M. Liang, F. Liu, S. Yang, and B. Hou, “Fast unsupervised deep fusion network for change detection of multitemporal sar images,” Neurocomputing, vol. 332, pp. 56–70, 2019.

Article  Google Scholar 

A. Ahmad and B. F. Branstetter, “Ct versus mr: Still a tough decision,” Otolaryngologic Clinics of North America, vol. 41, no. 1, pp. 1–22, 2008, topics in ENT Imaging. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0030666507001806

S. Li, X. Kang, and J. Hu, “Image fusion with guided filtering,” IEEE Transactions on Image processing, vol. 22, no. 7, pp. 2864–2875, 2013.

Article  PubMed  Google Scholar 

Z. Zhu, M. Zheng, G. Qi, D. Wang, and Y. Xiang, “A phase congruency and local laplacian energy based multi-modality medical image fusion method in nsct domain,” IEEE Access, vol. 7, pp. 20 811–20 824, 2019.

D. P. Bavirisetti, V. Kollu, X. Gang, and R. Dhuli, “Fusion of mri and ct images using guided image filter and image statistics,” International journal of Imaging systems and Technology, vol. 27, no. 3, pp. 227–237, 2017.

Article  Google Scholar 

P. Ganasala and A. Prasad, “Contrast enhanced multi sensor image fusion based on guided image filter and nsst,” IEEE Sensors Journal, vol. 20, no. 2, pp. 939–946, 2019.

Article  Google Scholar 

B. Shreyamsha Kumar, “Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform,” Signal, Image and Video Processing, vol. 7, no. 6, pp. 1125–1143, 2013.

留言 (0)

沒有登入
gif