{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:22:02Z","timestamp":1778084522466,"version":"3.51.4"},"reference-count":53,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2018B010109001"],"award-info":[{"award-number":["2018B010109001"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["No. 2020B1111010002"],"award-info":[{"award-number":["No. 2020B1111010002"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2019B020214001"],"award-info":[{"award-number":["2019B020214001"]}]},{"name":"Guangdong Marine Economic Development Project","award":["(GDNRC [2020]018)"],"award-info":[{"award-number":["(GDNRC [2020]018)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Medical image fusion (MIF) has received painstaking attention due to its diverse medical applications in response to accurately diagnosing clinical images. Numerous MIF methods have been proposed to date, but the fused image suffers from poor contrast, non-uniform illumination, noise presence, and improper fusion strategies, resulting in an inadequate sparse representation of significant features. This paper proposes the morphological preprocessing method to address the non-uniform illumination and noise by the bottom-hat\u2013top-hat strategy. Then, grey-principal component analysis (grey-PCA) is used to transform RGB images into gray images that can preserve detailed features. After that, the local shift-invariant shearlet transform (LSIST) method decomposes the images into the low-pass (LP) and high-pass (HP) sub-bands, efficiently restoring all significant characteristics in various scales and directions. The HP sub-bands are fed to two branches of the Siamese convolutional neural network (CNN) by process of feature detection, initial segmentation, and consistency verification to effectively capture smooth edges, and textures. While the LP sub-bands are fused by employing local energy fusion using the averaging and selection mode to restore the energy information. The proposed method is validated by subjective and objective quality assessments. The subjective evaluation is conducted by a user case study in which twelve field specialists verified the superiority of the proposed method based on precise details, image contrast, noise in the fused image, and no loss of information. The supremacy of the proposed method is further justified by obtaining 0.6836 to 0.8794, 0.5234 to 0.6710, and 3.8501 to 8.7937 gain for QFAB, CRR, and AG and noise reduction from 0.3397 to 0.1209 over other methods for objective parameters.<\/jats:p>","DOI":"10.3390\/e24030393","type":"journal-article","created":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T12:58:36Z","timestamp":1647003516000},"page":"393","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["CT and MRI Medical Image Fusion Using Noise-Removal and Contrast Enhancement Scheme with Convolutional Neural Network"],"prefix":"10.3390","volume":"24","author":[{"given":"Jameel Ahmed","family":"Bhutto","sequence":"first","affiliation":[{"name":"School of Automation Science and Engineering, South China University and Technology, Guangzhou 510640, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lianfang","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University and Technology, Guangzhou 510640, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory Zhuhai, Zhuhai 519000, China"},{"name":"Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiliang","family":"Du","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University and Technology, Guangzhou 510640, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory Zhuhai, Zhuhai 519000, China"},{"name":"Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9293-5625","authenticated-orcid":false,"given":"Zhengzheng","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University and Technology, Guangzhou 510640, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lubin","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University and Technology, Guangzhou 510640, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9138-3323","authenticated-orcid":false,"given":"Muhammad Faizan","family":"Tahir","sequence":"additional","affiliation":[{"name":"School of Electric Power, South China University and Technology, Guangzhou 510640, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"key":"ref_1","first-page":"73","article-title":"Multi-sensor image fusion: A survey of the state of the art","volume":"9","author":"Li","year":"2021","journal-title":"J. Comput. Commun."},{"key":"ref_2","first-page":"436","article-title":"Infrared and visible image fusion based on semantic segmentation","volume":"58","author":"Zhou","year":"2021","journal-title":"J. Comput. Res. Dev."},{"key":"ref_3","first-page":"21","article-title":"Medical image fusion method by deep learning","volume":"2","author":"Li","year":"2021","journal-title":"Int. J. Cogn. Comput. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"157005","DOI":"10.1109\/ACCESS.2020.3018264","article-title":"An enhanced image fusion algorithm by combined histogram equalization and fast gray level grouping using multi-scale decomposition and gray-PCA","volume":"8","author":"Bhutto","year":"2020","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100172","DOI":"10.1016\/j.iot.2020.100172","article-title":"A novel medical image fusion method based on Rolling Guidance Filtering","volume":"14","author":"Chen","year":"2021","journal-title":"Internet Things"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.inffus.2019.06.021","article-title":"A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks","volume":"53","author":"Muzammal","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4235","DOI":"10.1109\/TII.2019.2902878","article-title":"Artificial Intelligence-driven mechanism for edge computing-based industrial applications","volume":"15","author":"Sodhro","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.future.2018.07.042","article-title":"Renovating blockchain with distributed databases: An open source system","volume":"90","author":"Muzammal","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.future.2018.03.052","article-title":"Convergence of IoT and product lifecycle management in medical health care","volume":"86","author":"Sodhro","year":"2018","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"16040","DOI":"10.1109\/ACCESS.2017.2735865","article-title":"From multi-scale decomposition to non-multi-scale decomposition methods: A comprehensive survey of image fusion techniques and its applications","volume":"5","author":"Dogra","year":"2017","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, L., Li, H., Zhu, R., and Du, P. (Multimed. Tools Appl., 2022). An infrared and visible image fusion algorithm based on ResNet-152, Multimed. Tools Appl., in press.","DOI":"10.1007\/s11042-021-11549-w"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yang, Z., and Zeng, S. (2022). TPFusion: Texture preserving fusion of infrared and visible images via dense networks. Entropy, 24.","DOI":"10.3390\/e24020294"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhao, B., and Wu, Z. (2022). Research on color image encryption algorithm based on bit-plane and Chen Chaotic System. Entropy, 24.","DOI":"10.3390\/e24020186"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wei, B., Feng, X., Wang, K., and Gao, B. (2020, January 24\u201326). The multi-focus image fusion method based on CNN and SR. Proceedings of the 3rd International Conference on Algorithms, Computing and Artificial Intelligence, Sanya, China.","DOI":"10.1145\/3446132.3446182"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"149","DOI":"10.14257\/ijbsbt.2014.6.3.18","article-title":"Image fusion based on wavelet transforms","volume":"6","author":"Tawade","year":"2014","journal-title":"Int. J. Bio-Sci. Bio-Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4685","DOI":"10.1007\/s00500-018-3118-9","article-title":"Multi-focus image fusion combining focus-region-level partition and pulse-coupled neural network","volume":"23","author":"He","year":"2019","journal-title":"Soft Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1007\/s10723-020-09527-x","article-title":"Towards Blockchain-enabled security technique for industrial internet of things based decentralized applications","volume":"18","author":"Sodhro","year":"2020","journal-title":"J. Grid Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.inffus.2014.09.004","article-title":"A general framework for image fusion based on multi-scale transform and sparse representation","volume":"24","author":"Liu","year":"2015","journal-title":"Inf. Fusion"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.infrared.2013.07.010","article-title":"Image fusion based on nonsubsampled contourlet transform for infrared and visible light image","volume":"61","author":"Adu","year":"2013","journal-title":"Infrared Phys. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.inffus.2018.02.004","article-title":"Infrared and visible image fusion methods and applications: A survey","volume":"45","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4425","DOI":"10.1007\/s11831-021-09540-7","article-title":"Image fusion techniques: A survey","volume":"28","author":"Kaur","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.inffus.2010.04.001","article-title":"Pixel-level image fusion with simultaneous orthogonal matching pursuit","volume":"13","author":"Yang","year":"2012","journal-title":"Inf. Fusion"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6369","DOI":"10.1007\/s11042-020-08834-5","article-title":"Survey study of multimodality medical image fusion methods","volume":"80","author":"Tawfik","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"15001","DOI":"10.1007\/s11042-019-08579-w","article-title":"Fully convolutional network-based infrared and visible image fusion","volume":"79","author":"Feng","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.inffus.2016.12.001","article-title":"Multi-focus image fusion with a deep convolutional neural network","volume":"36","author":"Liu","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.inffus.2017.10.007","article-title":"Deep learning for pixel-level image fusion: Recent advances and future prospects","volume":"42","author":"Liu","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.ins.2017.09.010","article-title":"A novel multi-modality image fusion method based on image decomposition and sparse representation","volume":"432","author":"Zhu","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.inffus.2015.11.003","article-title":"Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters","volume":"30","author":"Zhou","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_29","first-page":"407","article-title":"Infrared and visible image fusion method based on three stages of discrete wavelet transform","volume":"9","author":"Zhan","year":"2016","journal-title":"Int. J. Hybrid Inf. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"62331T","DOI":"10.1117\/12.662373","article-title":"PCA-based image fusion","volume":"6233","author":"Kumar","year":"2006","journal-title":"Proc. SPIE"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Naji, M.A., and Aghagolzadeh, A. (2015, January 5\u20136). Multi-focus image fusion in DCT domain based on correlation coefficient. Proceedings of the 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), Tehran, Iran.","DOI":"10.1109\/KBEI.2015.7436118"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1016\/j.aeue.2015.02.007","article-title":"Discrete wavelet transform based principal component averaging fusion for medical images","volume":"69","author":"Vijayarajan","year":"2015","journal-title":"AEU-Int. J. Electron. Commun."},{"key":"ref_33","first-page":"99","article-title":"Image fusion based on stationary wavelet transform","volume":"2","author":"Pradnya","year":"2013","journal-title":"Int. J. Adv. Eng. Res. Stud."},{"key":"ref_34","first-page":"34","article-title":"Multi focus image fusion using combined median and average filter based hybrid stationary wavelet transform and principal component analysis","volume":"9","author":"Lianfang","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_35","first-page":"29","article-title":"Image enhancement by fusion in contourlet transform","volume":"2","author":"Asmare","year":"2010","journal-title":"Int. J. Electr. Eng. Inform."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.infrared.2015.11.002","article-title":"An improved fusion algorithm for infrared and visible images based on multi-scale transform","volume":"74","author":"Li","year":"2016","journal-title":"Infrared Phys. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3089","DOI":"10.1109\/TIP.2006.877507","article-title":"The nonsubsampled contourlet transform: Theory, design, and applications","volume":"15","author":"Zhou","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00006-021-01197-6","article-title":"Multi-modal Medical Image Fusion Based on Geometric Algebra Discrete Cosine Transform","volume":"32","author":"Wang","year":"2022","journal-title":"Adv. Appl. Clifford Algebras"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dogra, A., and Kumar, S. (J. Ambient Intell. Humaniz. Comput., 2022). Multi-modality medical image fusion based on guided filter and image statistics in multidirectional shearlet transform domain, J. Ambient Intell. Humaniz. Comput., in press.","DOI":"10.1007\/s12652-022-03764-6"},{"key":"ref_40","first-page":"761","article-title":"Infrared and visible image fusion combining edge features and adaptive PCNN in NSCT domain","volume":"44","author":"Li","year":"2016","journal-title":"Acta Electron. Sin."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, L., Ma, H., and Jia, Z. (2022). Multiscale geometric analysis fusion-based unsupervised change detection in remote sensing images via FLICM Model. Entropy, 24.","DOI":"10.3390\/e24020291"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.infrared.2014.04.003","article-title":"Novel fusion method for visible light and infrared images based on NSST\u2013SF\u2013PCNN","volume":"65","author":"Kong","year":"2014","journal-title":"Infrared Phys. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, K., Qi, G., Zhu, Z., and Chai, Y. (2017). A novel geometric dictionary construction approach for sparse representation based Image fusion. Entropy, 19.","DOI":"10.3390\/e19070306"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1109\/LSP.2016.2618776","article-title":"Image fusion with convolutional sparse representation","volume":"23","author":"Liu","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Sodhro, A.H., Obaidat, M.S., Pirbhulal, S., Sodhro, G.H., Zahid, N., and Rawat, A. (2019, January 22\u201324). A novel energy optimization approach for artificial intelligence-enabled massive internet of things. Proceedings of the International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), Berlin, Germany.","DOI":"10.23919\/SPECTS.2019.8823317"},{"key":"ref_46","first-page":"5005012","article-title":"Res2Fusion: Infrared and visible image fusion based on dense Res2net and double non-local attention models","volume":"71","author":"Wang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1049\/trit.2018.1045","article-title":"Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation","volume":"4","author":"Qi","year":"2019","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, K., Zheng, M., Wei, H., Qi, G., and Li, Y. (2020). Multi-modality medical image fusion using convolutional neural network and contrast pyramid. Sensors, 20.","DOI":"10.3390\/s20082169"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.inffus.2013.04.005","article-title":"EGGDD: An explicit dependency model for multi-modal medical image fusion in shift-invariant shearlet transform domain","volume":"19","author":"Wang","year":"2014","journal-title":"Inf. Fusion"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.inffus.2015.03.003","article-title":"Joint patch clustering-based dictionary learning for multimodal image fusion","volume":"27","author":"Kim","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, X., Cheng, J., and Peng, H. (2017, January 10\u201313). A medical image fusion method based on convolutional neural networks. Proceedings of the 20th International Conference on Information Fusion (Fusion), Xi\u2019an, China.","DOI":"10.23919\/ICIF.2017.8009769"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"012018","DOI":"10.1088\/1742-6596\/1994\/1\/012018","article-title":"Research on multi-focal image fusion based on wavelet transform","volume":"1994","author":"Kong","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1007\/s11760-013-0556-9","article-title":"Image fusion based on pixel significance using cross bilateral filter","volume":"9","author":"Kumar","year":"2015","journal-title":"Signal Image Video Process."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/3\/393\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:35:07Z","timestamp":1760135707000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/3\/393"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,11]]},"references-count":53,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["e24030393"],"URL":"https:\/\/doi.org\/10.3390\/e24030393","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,11]]}}}