Abstract
Multimodal image fusion combines the complementary information of multimodality images into a single image that preserves the information of all the source images. This paper proposes a multimodal image fusion method situated on image enhancement, edge detection, multiscale sliding window, and image matting to obtain the detailed region information of the input images. In the proposed system, firstly the multimodality input images are rectified via a contrast enhancement method through which the intensity distribution is refined for clear vision. The spatial gradient edge detection method is utilized for separating the edge information from the contrast-enhanced images. These edges are then used by a multiscale sliding window method to provide global and local activity level maps. These activity maps further generate trimap and decision maps. Finally, by employing the improved decision maps and fusion rule the fused image is acquired.
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Muzammil, S.R., Maqsood, S., Haider, S., Damaševičius, R.: CSID: a novel multimodal image fusion algorithm for enhanced clinical diagnosis. Diagnostics 10(11), 904 (2020)
Maqsood, S., Javed, U., Riaz, M.M., Muzammil, M., Muhammad, F., Kim, S.: Multiscale image matting based multi-focus image fusion technique. Electronics 9(2), 472 (2020)
Grycuk, R., Wojciechowski, A., Wei, W., Siwocha, A.: Detecting visual objects by edge crawling. J. Artif. Intell. Soft Comput. Res. 10(3), 223–237 (2020)
Grycuk, R., Najgebauer, P., Kordos, M., Scherer, M.M., Marchlewska, A.: Fast image index for database management engines. J. Artif. Intell. Soft Comput. Res. 10(2), 113–123 (2020)
Woźniak, M., Wieczorek, M., Siłka, J., Połap, D.: Body pose prediction based on motion sensor data and recurrent neural network. IEEE Trans. Ind. Inform. 17(3), 2101–2111 (2020)
Juočas, L., Raudonis, V., Maskeliūnas, R., Damaševičius, R., Woźniak, M.: Multi-focusing algorithm for microscopy imagery in assembly line using low-cost camera. Int. J. Adv. Manufact. Technol. 102(9), 3217–3227 (2019). https://doi.org/10.1007/s00170-019-03407-9
Guo, Z., Li, X., Huang, H., Guo, N., Li, Q.: Deep learning-based image segmentation on multimodal medical imaging. IEEE Trans. Radiat. Plasma Med. Sci. 3(2), 162–169 (2019)
Ke, Q., Zhang, J., Wei, W., Damaševičius, R., Wozniak, M.: Adaptive Independent Subspace Analysis (AISA) of Brain Magnetic Resonance Imaging (MRI) data. IEEE Access 7(1), 12252–12261 (2019)
Khan, M.A., et al.: Multimodal brain tumor classification using deep learning and robust feature selection: a machine learning application for radiologists. Diagnostics 10(8), 1–19 (2020)
Manchanda, M., Sharma, R.: An improved multimodal medical image fusion algorithm based on fuzzy transform. J. Vis. Commun. Image Represent. 51(2), 76–94 (2018)
Maqsood, S., Javed, U.: Biomedical signal processing and control multi-modal medical image fusion based on two-scale image decomposition and sparse representation. Biomed. Sig. Process. Control 57, 101810 (2020)
Li, H., Qiu, H., Yu, Z., Li, B.: Multifocus image fusion via fixed window technique of multiscale images and non-local means filtering. Sig. Process. 138, 71–85 (2017)
Woźniak, M., Siłka, J., Wieczorek, M.: Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Comput. Appl., 1–16 (2021). https://doi.org/10.1007/s00521-021-05841-x
Wei, W., Zhou, B., Połap, D., Woźniak, M.: A regional adaptive variational PDE model for computed tomography image reconstruction. Pattern Recogn. 92, 64–81 (2019)
Yang, S., Wang, M., Jiao, L., Wu, R., Wang, Z.: Image fusion based on a new contourlet packet. Inf. Fusion 11(2), 78–84 (2010)
Yang, Y.: A novel DWT based multi-focus image fusion method. Procedia Eng. 24(1), 177–181 (2011)
Li, H., Qiu, H., Yu, Z., Zhang, Y.: Infrared and visible image fusion scheme based on NSCT and low-level visual features. Infrared Phys. Technol. 76, 174–184 (2016)
Nencini, F., Garzelli, A., Baronti, S., Alparone, L.: Remote sensing image fusion using the curvelet transform. Inf. Fusion 8(2), 143–156 (2007)
Yang, B., Li, S.: Visual attention guided image fusion with sparse representation. Optik (Stuttg) 125(17), 4881–4888 (2014)
Yan, J., Li, J., Fu, X.: No-reference quality assessment of contrast-distorted images using contrast enhancement. arXiv preprint arXiv:1904.08879 (2019)
Gao, W., Zhang, X., Yang, L., Liu, H.: An improved Sobel edge detection. In: Proceedings of the 3rd International Conference on Computer Science and Information Technology, vol. 9, no. 11, pp. 67–71 (2010)
Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2007)
Hossny, M., Nahavandi, S., Vreighton, D.: Comments on information measure for performance of image fusion. Electron. Lett. 44(18), 1066–1067 (2008)
Liu, Y., Liu, S., Wang, Z.: A general framework for image fusion based on multi-scale transform and sparse representation. Inf. Fusion 24, 147–164 (2015)
Haghighat, M.B.A., Aghagolzadeh, A., Seyedarabi, H.: A non-reference image fusion metric based on mutual information of image features. Comput. Electr. Eng. 37(5), 744–756 (2011)
Petrović, V.S., Xydeas, C.S.: Sensor noise effects on signal-level image fusion performance. Inf. Fusion 4(3), 167–183 (2003)
Han, Y., Cai, Y., Cao, Y., Xu, X.: A new image fusion performance metric based on visual information fidelity. Inf. Fusion 14(2), 127–135 (2013)
Li, S., Kang, X., Hu, J.: Image fusion with guided filtering. IEEE Trans. Image Process. 22, 2864–2875 (2013)
Du, J., Li, W., Xiao, B.: Union Laplacian pyramid with multiple features for medical image fusion. Neurocomputing 194, 326–339 (2016)
Liu, Y., Chen, X., Cheng, J., Peng, H.: A medical image fusion method based on convolutional neural networks. In: Proceedings of the 2017 20th International Conference on Information Fusion (Fusion), pp. 10–13 (2017)
Zhu, Z., Chai, Y., Yin, H., Li, Y., Liu, Z.: A novel dictionary learning approach for multi-modality medical image fusion. Neurocomputing 214, 471–482 (2016)
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Maqsood, S., Damasevicius, R., Siłka, J., Woźniak, M. (2021). Multimodal Image Fusion Method Based on Multiscale Image Matting. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12855. Springer, Cham. https://doi.org/10.1007/978-3-030-87897-9_6
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