Medical image fusion based on DTNP systems and Laplacian pyramid
Tóm tắt
Từ khóa
Tài liệu tham khảo
Ionescu, M., Pǎun, G., & Yokomori, T. (2006). Spiking neural P systems. Fundamenta Informaticae, 71, 279–308.
Pǎun, G., Rozenberg, G., & Salomaa, A. (2010). The Oxford Handbook of Membrane Computing. New York: Oxford University Press.
Pǎun, Gh. (2007). Spiking neural P systems with astrocyte-like control. Journal of Universal Computer Science, 13(11), 1707–1721.
Pan, L., & Pǎun, G. (2009). Spiking neural P systems with anti-spikes. International Journal of Computers Communications & Control, 4(3), 273–282.
Peng, H., Yang, J., Wang, J., Wang, T., Sun, Z., Song, X., Lou, X., & Huang, X. (2017). Spiking neural P systems with multiple channels. Neural Networks, 95, 66–71.
Wu, T., Pǎun, A., Zhang, Z., & Pan, L. (2017). piking neural P systems with polarizations. IEEE Transactions on Neural Networks and Learning Systems, 29(8), 3349–3360.
Cabarle, F. G. C., Adorna, H. N., Pérez-Jiménez, M. J., & Song, T. (2015). Spiking neural P systems with structural plasticity. Neural Computing and Applications, 26(8), 1905–1917.
Song, X., Valencia-Cabrera, L., Peng, H., Wang, J., & Pérez-Jiménez, M. J. (2021). Spiking neural P systems with delay on synapses. International Journal of Neural Systems, 31(1), 1–19.
Peng, H., & Wang, J. (2018). Coupled neural P systems. IEEE Transactions on Neural Networks and Learning Systems, 30(6), 1672–1682.
Peng, H., Li, B., Wang, J., Song, X., Wang, T., Valencia-Cabrera, L., Pérez-Hurtado, I., Riscos-Núñez, A., & Pérez-Jiménez, M. J. (2020). Spiking neural P systems with inhibitory rules. Knowledge-Based Systems, 188, 1–10.
Peng, H., Bao, T., Luo, X., Wang, J., Song, X., Riscos-Núñez, A., & Pérez-Jiménez, M.J. (2020). Dendrite P systems. Neural Networks, 127, 110–120.
Peng, H., Lv, Z., Li, B., Luo, X., Wang, J., Song, X., Wang, T., Pérez-Jiménez, M.J., & Riscos-Núñez, A. (2020). Nonlinear spiking neural P systems. International Journal of Neural Systems, 30(10): 1–17.
Díaz-Pernil, D., Gutiérrez-Naranjo, M. A., & Peng, H. (2019). Membrane computing and image processing: a short survey. Journal of Membrane Computing, 1(1), 58–73.
Singh, R., & Khare, A. (2014). Fusion of multimodal medical images using Daubechies complex wavelet transform—a multiresolution approach. Information Fusion, 19, 49–60.
Manchanda, M., & Sharma, R. (2016). A novel method of multimodal medical image fusion using fuzzy transform. Journal of Visual Communication and Image Representation, 40, 197–217.
Manchanda, M., & Sharma, R. (2018). An improved multimodal medical image fusion algorithm based on fuzzy transform. Journal of Visual Communication and Image Representation, 51, 76–94.
Singh, S., & Anand, R. S. (2018). Ripplet domain fusion approach for CT and MR medical image information. Biomedical Signal Processing and Control, 46, 281–292.
Padmavathi, K., Asha, C. S., & Karki, M. V. (2020). A novel medical image fusion by combining TV-L1 decomposed textures based on adaptive weighting scheme. Engineering Science and Technology, 23(1), 225–239.
Yang, L., Guo, B., & Ni, W. (2008). Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform. Neurocomputing, 72(1–3), 203–211.
Zhu, Z., Yin, H., Chai, Y., Li, Y., & Qi, G. (2018). A novel multi-modality image method based on image decomposition and sparse representation. Information Science, 432, 516–529.
Zhang, Q., Shi, T., Wang, F., Blum, R. S., & Han, J. (2018). Robust sparse repesentation based multi-focus image fusion with dictionary construction and local spitial consistency. Pattern Recognition, 83, 299–313.
Ma, X., Hu, S., Liu, S., Fang, J., & Xu, S. (2019). Multi-focus image fusion based on joint sparse representation and optimum theory. Signal Processing, 78, 125–134.
Zhang, M., Li, S., Yu, F., & Tian, X. (2020). Image fusion employing adaptive spectral-spatial gradient sparse regularization in UAV remote sensing. Signal Processing, 170(107434), 1–13.
Zhang, Y., Yang, M., Li, N., & Yu, Z. (2020). Analysis-synthesis dictionary pair learning and patch saliency measure for image fusion. Signal Processing, 167(107327), 1–13.
Hu, Q., Hu, S., & Zhang, F. (2020). Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering. Signal Processing, 83(115758), 1–10.
Li, H., Wang, Y., Yang, Z., et al. (2020). Discriminative dictionary learning-based multiple component decomposition for detail-preserving noisy image fusion. IEEE Transactions on Instrumentation and Measurement, 69(4), 1082–1102.
Li, H., & Wu, X. (2019). Densefuse: a fusion approach to infrared and visible images. IEEE Transactions on Image Processing, 28(5), 2614–2623.
Zhang, Y., & Liu, Y. (2020). IFCNN: a general image fusion framework based on convolutional neural network. Information Fusion, 54, 99–118.
Peng, H., Wang, J., Pérez-Jiménez, M. J., & Riscos-Núñez, A. (2019). Dynamic threshold neural P systems. Knowledge-Based Systems, 163, 875–884.
Liu, Y., Liu, S., & Wang, Z. (2015). A general framework for image fusion based on multi-scale transform and sparse representation. Information Fusion, 24(1), 147–164.
Li, S., Kang, X., & Hu, J. (2013). Image fusion with guided filtering. IEEE Transactions on Image Processing, 22(7), 2864–2875.
Zhu, Z., Zheng, M., Qi, G., Wang, D., & Xiang, Y. (2019). A phase congruency and local laplacian energy based multi-modality medical image fusion method in NSCT domain. IEEE Access, 2019(7), 20811–20824.
Li, B., Peng, H., & Wang, J. (2021). A novel fusion method based on dynamic threshold neural P systems and nonsubsampled contourlet transform for multi-modality medical images. Signal Processing, 178, 107793.
Yin, M., Liu, X., Liu, Y., & Chen, X. (2019). Medical image fusion with parameter-adaptive pulse coupled neural network in nonsubsampled shearlet transform domain. IEEE Transactions on Instrumentation and Measurement, 68(1), 49–64.
Tan, W., Tiwari, P., Pandey, H., Moreira, C., & Jaiswal, A. (2020). Multimodal medical image fusion algorithm in the era of big data. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-05173-2.
Liu, Y., Chen, X., Cheng, J., & Peng, H. (2017). A medical image fusion method based on convolutional neural networks. 20th International Conference on Information Fusion, Xi’an, China, pp. 1070–1060.
Xydeas, C. S., & Petrovic, V. (2000). Objective image fusion performance measure. Electronics Letters, 36(4), 308–309.
Hossny, M., Nahavandi, S., & Creighton, D. (2008). Comments on information measure for performance of image fusion. Electronics Letters, 44(18), 1066–1067.
Haghighat, M. B. A., Aghagolzadeh, A., & Seyedarabi, H. (2011). A non-reference image fusion metric based on mutual information of image features. Computer Electronic Engineering, 37(5), 744–756.
Aslantas, V., & Bendes, E. (2015). A new image quality metric for image fusion: the sum of the correlations of differences. International Journal of Electronics and Communications, 69(12), 1890–1896.
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2003). Multiscale structural similarity for image quality assessment. The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003(2), 1398–1402.
Aslantas, V., & Kurban, R. (2010). Fusion of multi-focus images using differential evolution algorithm. Expert Systems with Applications, 37(12), 8861–8870.
Cui, G., Feng, H., Xu, Z., Li, Q., & Chen, Y. (2015). Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition. Optical Communications, 341, 199–209.
Eskicioglu, A. M., & Fisher, P. S. (1995). Image quality measures and their performance. IEEE Transactions on Communications, 43(12), 2959–2965.