{"version":1,"kind":"Article","sha256":"","slug":"635","location":"","dependencies":[],"doi":"10.54294/eoxxuh","thumbnail":"https://pub.desci.com/ipfs/bafkreia7nwpha7dq4iovbn56quhtoymdsdbhgc3h3v76hl53pf73gmbajy","frontmatter":{"title":"Construction of Statistical Shape Models from Minimal Deformations","abstract":"Abstract. Statistical shape models (SSM) capture the variation of shape across a population, in order to allow further analysis. Previous work demonstrates that deformation fields contain global transformation components, even if global pre-\r\nregistration is performed. It is crucial to construction of SSMs to remove these global transformation components from the local deformations - thus obtaining minimal deformations - prior to using these as input for SSM construction. In medical image processing, parameterized SSMs based on control points of free-form deformations (FFD) are a popular choice, since they offer several advantages compared to SSMs based on dense deformation fields. In this work, we extend the previous approach by presenting a framework for construction of both, unparameterized and FFD-based SSMs from minimal deformations. The core of the method is computation of minimal deformations by extraction of the linear part from the original dense deformations. For FFD-based SSMs, the FFD-parameterization of the minimal deformations is performed by projection onto the space of FFDs. Both steps are computed by close-form solutions optimally in the least-square sense. The proposed method is evaluated on a data set of 62 MR images of the corpus callosum. The results show a significant improvement achieved by the proposed method for SSMs built on dense fields, as well as on FFD-based SSMs.","license":"You are licensing your work to Kitware Inc. under the\nCreative Commons Attribution License Version 3.0.\n\nKitware Inc. agrees to the following:\n\nKitware is free\n * to copy, distribute, display, and perform the work\n * to make derivative works\n * to make commercial use of the work\n\nUnder the following conditions:\n\\\"by Attribution\\\" - Kitware must attribute the work in the manner specified by the author or licensor.\n\n * For any reuse or distribution, they must make clear to others the license terms of this work.\n * Any of these conditions can be waived if they get permission from the copyright holder.\n\nYour fair use and other rights are in no way affected by the above.\n\nThis is a human-readable summary of the Legal Code (the full license) available at\nhttp://creativecommons.org/licenses/by/3.0/legalcode","keywords":["Shape Models","Statistics"],"authors":[{"name":"Zikic, Darko","email":"zikic@cs.tum.edu","affiliations":["Computer Aided Medical Procedures (CAMP), TUM, Munich, Germany,"],"corresponding":true},{"name":"Glocker, Ben","email":"b.glocker@imperial.ac.uk","affiliations":[]},{"name":"Sass Hansen, Micheal","affiliations":[]},{"name":"Khamene, Ali","affiliations":[]},{"name":"Navab, Nassir","affiliations":[]}],"date_submitted":"2008-09-12 11:52:15","external_publication_id":635,"revision_cids":["bafkreih52hvc725gfsfje552a3nycf33e3ech6h73v2wvi7fbebm2qlhni"],"thumbnail":"https://pub.desci.com/ipfs/bafkreia7nwpha7dq4iovbn56quhtoymdsdbhgc3h3v76hl53pf73gmbajy"},"mdast":{"type":"root"},"downloads":[{"url":"https://ipfs.desci.com/ipfs/bafkreih64d3ldkkwcmkap4zvluw4pd6yn7ee3jxs7wu4yzr7coqgymgwfy","title":"root/insight-journal-metadata.json","filename":"insight-journal-metadata.json","extra":{"size_bytes":7854,"type":"file"}},{"url":"https://dweb.link/ipfs/bafkreigg2dtg5kup3pcordxrn2ph77s5zbad5yxxhk6cvbxqqvxlerzhhi","title":"root/article.pdf","filename":"article.pdf","extra":{"size_bytes":281974,"type":"file"}}],"references":{"cite":{"order":["ref1","ref2","ref3","ref4","ref5","ref6","ref7","ref8","ref9","ref10","ref11","ref12","ref13","ref14","ref15"]},"data":{"ref1":{"label":"ref1","enumerator":"1","url":"https://doi.org/10.1109/tmi.2003.815865","html":"Automatic construction of 3-d statistical deformation models of the brain using nonrigid registration+TMI+22+8+2003+D. 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