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Article type: Research Article
Authors: Sucharitha, G.a; * | sankardass, Veeramalaib | Rani, R.c | Bhat, Nagarajd | Rajaram, A.e
Affiliations: [a] Electronics and Communication Engineering, Institute of Aeronautical Engineering, Hyderabad, India | [b] Department of Computer Science and Engineering, Chennai Institute of Technology, Chennai, India | [c] Department of ECE, Vemu Institute of Technology, Chittoor, AP | [d] Department of ECE, RV College of Engineering, Bengaluru, Karnataka, India | [e] Department of Electronics and Communication Engineering, E.G.S Pillay Engineering College, Nagapattinam, Tamilnadu
Correspondence: [*] Corresponding author. G. Sucharitha, Electronics and Communication Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, Telangana, India. E-mail: sucharitha456@gmail.com.
Abstract: Although difficult, robust and reliable synchronization of multimodal medical pictures has several practical uses. For instance, in MR-TRUS fusing guided prostate treatments, picture registration between the two modalities is essential. However, due to the significant variety in image appearance and correlation, MR-TRUS picture registration remains a challenging issue. In this research, we suggest employing deep convolutional neural networks (CNN) i.e. three dimensional CNN U-NET (3D-Conv-Net) to develop a resemblance measure for MR-TRUS registration. Finally, for the second-order optimal of the taught measure, we apply a composite optimisation method that searches the solution space for an appropriate starting point. We also use a multi-stage process to improve the optimisation metric.
Keywords: Image registration, convolutional neural networks, multimodal image fusion, and prostate cancer
DOI: 10.3233/JIFS-235744
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3395-3409, 2024
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