{"version":1,"kind":"Article","sha256":"","slug":"24","location":"","dependencies":[],"doi":"10.54294/nohev6","frontmatter":{"title":"A nonparametric, entropy-minimizing MRI tissue classification algorithm implementation using ITK","abstract":"This paper focuses on the role of open-source software in the development of a novel magnetic resonance image (MRI) tissue classification algorithm. Specifically, we describe the of use existing classes in the Insight Segmentation and Registration Toolkit (ITK) and several new classes that were implemented to perform non-parametric density estimation and entropy minimization. These new classes also provide a general framework for nonparametric density estimation and related applications.","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":["MRI segmentation","ITK","MRI tissue classification","Parzen window"],"authors":[{"name":"Tasdizen, Tolga","email":"tolga@sci.utah.edu","affiliations":["University of Utah"],"corresponding":true},{"name":"Awate, Suyash","email":"suyash@cs.utah.edu","affiliations":[]},{"name":"Whitaker, Ross","affiliations":[]}],"date_submitted":"2005-08-03T16:33:51Z","external_publication_id":24,"revision_cids":["bafkreif7oq2lum4qwfa5ayx5wr4xazpnlt3rjzfo3co5jgfwtw45wiwykm"]},"mdast":{"type":"root"},"downloads":[{"url":"https://ipfs.desci.com/ipfs/bafkreiasdcbuvq4qcvuzo7kjlsvmb4rgix4bjof4u6dpnol63yk3be5teq","title":"root/reviews.md","filename":"reviews.md","extra":{"size_bytes":7187,"type":"file"}},{"url":"https://ipfs.desci.com/ipfs/bafkreifberotq2fbw5r2fyfarjxwk64lugqg7j52ggit2jlar2dbax3h4a","title":"root/insight-journal-metadata.json","filename":"insight-journal-metadata.json","extra":{"size_bytes":14515,"type":"file"}},{"url":"https://dweb.link/ipfs/bafkreiervwid5tf6ts2n5xdtevfl2krcs3ce6ctzfx47ajez5uqe6ls6ta","title":"root/article.pdf","filename":"article.pdf","extra":{"size_bytes":116874,"type":"file"}}],"references":{"cite":{"order":["ref1","ref2","ref3","ref4","ref5","ref6","ref7","ref8","ref9","ref10","ref11","ref12","ref13","ref14"]},"data":{"ref1":{"label":"ref1","enumerator":"1","url":"https://doi.org/10.1016/s1361-8415(96)80008-9","html":"Segmentation of brain tissue from magentic resonance images+” Med. 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