Programs
- M. Tech. in Automotive Engineering -
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Publication Type : Conference Paper
Publisher : Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications. AISGSC 2019 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-24051-6_48 Elsevier
Source : Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications. AISGSC 2019 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-24051-6_48 Elsevier
Url : https://link.springer.com/chapter/10.1007/978-3-030-24051-6_48
Campus : Coimbatore
School : School of Computing
Year : 2019
Abstract : Image registration is one of the most significant and useful approaches in diagnosing disease by providing complementary information from different medical images. Image registration is a process of overlaying two or more images into a single integrated image. This process is widely used in medical imaging analysis to overlay images obtained from different devices at different time. Traditional methods to geometrically align images are time-consuming, while deep learning techniques are less time-consuming. In recent years, deep learning is a growing technology and has gained many breakthroughs in various image processing problems such as classification, reconstruction, and registration. In particular, convolutional neural networks (CNNs) is one of the most powerful tools in computer vision task. Recently, deep learning techniques are being developed for medical image registration, and image fusion is clearly evidenced from high-quality research. The intention of this survey is to provide perspective about the recent development of registration techniques using machine learning and deep learning techniques.
Cite this Research Publication : Priya, M.C.S., Jayashree, L.S. (2020). A Survey on Medical Image Registration Using Deep Learning Techniques. In: Kumar, L., Jayashree, L., Manimegalai, R. (eds) Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications. AISGSC 2019 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-24051-6_48 Elsevier