{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-and-robust-symmetric-image-registration","title":"Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information","arxiv_id":"1807.11599","date":"2018-07-30","proceeding":null,"authors":["Johan Öfverstedt","Joakim Lindblad","Nataša Sladoje"],"abstract":"Intensity-based image registration approaches rely on similarity measures to\nguide the search for geometric correspondences with high affinity between\nimages. The properties of the used measure are vital for the robustness and\naccuracy of the registration. In this study a symmetric, intensity\ninterpolation-free, affine registration framework based on a combination of\nintensity and spatial information is proposed. The excellent performance of the\nframework is demonstrated on a combination of synthetic tests, recovering known\ntransformations in the presence of noise, and real applications in biomedical\nand medical image registration, for both 2D and 3D images. The method exhibits\ngreater robustness and higher accuracy than similarity measures in common use,\nwhen inserted into a standard gradient-based registration framework available\nas part of the open source Insight Segmentation and Registration Toolkit (ITK).\nThe method is also empirically shown to have a low computational cost, making\nit practical for real applications. Source code is available.","url_abs":"http://arxiv.org/abs/1807.11599v2","url_pdf":"http://arxiv.org/pdf/1807.11599v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fast-and-robust-symmetric-image-registration","repo_url":"https://github.com/MIDA-group/py_alpha_amd_release","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"fast-and-robust-symmetric-image-registration","repo_url":"https://github.com/MIDA-group/itkAlphaAMD","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-registration","task_name":"Image Registration"},{"task_slug":"medical-image-registration","task_name":"Medical Image Registration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}