{"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/rigid-slice-to-volume-medical-image","title":"Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields","arxiv_id":"1608.05562","date":"2016-08-19","proceeding":null,"authors":["Roque Porchetto","Franco Stramana","Nikos Paragios","Enzo Ferrante"],"abstract":"Rigid slice-to-volume registration is a challenging task, which finds\napplication in medical imaging problems like image fusion for image guided\nsurgeries and motion correction for volume reconstruction. It is usually\nformulated as an optimization problem and solved using standard continuous\nmethods. In this paper, we discuss how this task be formulated as a discrete\nlabeling problem on a graph. Inspired by previous works on discrete estimation\nof linear transformations using Markov Random Fields (MRFs), we model it using\na pairwise MRF, where the nodes are associated to the rigid parameters, and the\nedges encode the relation between the variables. We compare the performance of\nthe proposed method to a continuous formulation optimized using simplex, and we\ndiscuss how it can be used to further improve the accuracy of our approach.\nPromising results are obtained using a monomodal dataset composed of magnetic\nresonance images (MRI) of a beating heart.","url_abs":"http://arxiv.org/abs/1608.05562v1","url_pdf":"http://arxiv.org/pdf/1608.05562v1.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":"rigid-slice-to-volume-medical-image","repo_url":"https://gitlab.com/franco.stramana1/slice-to-volume","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}