{"version":1,"kind":"Article","sha256":"","slug":"35","location":"","dependencies":[],"doi":"10.54294/x9118y","frontmatter":{"title":"Knowledge-Based Segmentation of Brain MRI Scans Using the Insight Toolkit","abstract":"An Insight Toolkit (ITK) implementation of our knowledgebased\r\nsegmentation algorithm applied to brain MRI scans is presented\r\nin this paper. Our algorithm is a refinement of the work of Teo, Saprio,\r\nand Wandall. The basic idea is to incorporate prior knowledge into the\r\nsegmentation through Bayesrule. Image noise is removed via an affine\r\ninvariant anisotropic smoothing of the posteriors as in Haker et. al.\r\nWe present the results of this code on two different projects. First, we\r\nshow the effect of applying this code to skull-removed brain MRI scans.\r\nSecond, we show the effect of applying this code to the extraction of the\r\nDLPFC from a user-defined subregion of brain MRI data.We present our\r\nresults on brain MRI scans, comparing the results of the knowledge-based\r\nsegmentation to manual segmentations on datasets of schizophrenic patients.","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":["DLPFC","Knowledge-Based Segmentation","Brain MRI Scans","Bayes' Rule"],"authors":[{"name":"Melonakos, John","email":"jmelonak@ece.gatech.edu","affiliations":["Georgia Tech"],"corresponding":true},{"name":"Melonakos, John","email":"john@arrayfire.com","affiliations":[]},{"name":"Al-Hakim, Ramsey","affiliations":[]},{"name":"Fallon, James","affiliations":[]},{"name":"Tannenbaum, Allen","affiliations":[]}],"date_submitted":"2005-08-05T21:08:11Z","external_publication_id":35,"revision_cids":["bafkreieyq7rs3zagij4fhchx5b22r6sbaoplnd5cwh2f3w4wzlebz7hjn4"]},"mdast":{"type":"root"},"downloads":[{"url":"https://ipfs.desci.com/ipfs/bafkreid26c6ueutk24jaqlci2x4njmb23f44cd3sqrrp2uctozxsm57xqm","title":"root/insight-journal-metadata.json","filename":"insight-journal-metadata.json","extra":{"size_bytes":17707,"type":"file"}},{"url":"https://pub.desci.com/ipfs/bafkreidlauyaz7554nnae4a4lvvqs4k5zkxlnmyaodwyqf4dmyid7ocpea","title":"root/code/CMakeLists.txt","filename":"CMakeLists.txt","extra":{"size_bytes":432,"type":"file"}},{"url":"https://pub.desci.com/ipfs/bafkreibbhit3mbp2c7ntyd7vethzse6yzzwlk3soygfszespeimkk627ki","title":"root/code/KnowledgeBasedSegmentation.cxx","filename":"KnowledgeBasedSegmentation.cxx","extra":{"size_bytes":15521,"type":"file"}},{"url":"https://pub.desci.com/ipfs/bafkreiene3othzhl5cn3fgmwjke4eyczwnxxezwey2nto2kjbvat7h5v2e","title":"root/code/itkHistogramDensityFunction.h","filename":"itkHistogramDensityFunction.h","extra":{"size_bytes":2959,"type":"file"}},{"url":"https://pub.desci.com/ipfs/bafkreihj47flh4jrdnlop6ho6brrmoe6xk7v27k4l4sy3656otnhqtydqm","title":"root/code/itkHistogramDensityFunction.txx","filename":"itkHistogramDensityFunction.txx","extra":{"size_bytes":2655,"type":"file"}},{"url":"https://pub.desci.com/ipfs/bafkreiffasnbr3ejcj2y6zcm4y4ct32jfcpjhxvro3t655fgb5oowb7yva","title":"root/code/itkImageCastVectorIndexSelectionFilter.h","filename":"itkImageCastVectorIndexSelectionFilter.h","extra":{"size_bytes":5297,"type":"file"}},{"url":"https://ipfs.desci.com/ipfs/bafkreidl64oaqvcosajlmc6bq2totxii4a2rcexbtoixuyncov2vae5hzm","title":"root/reviews.md","filename":"reviews.md","extra":{"size_bytes":11507,"type":"file"}},{"url":"https://dweb.link/ipfs/bafkreibbz6n3bfvt2qhnuhp7btmhgitl56pwv3y46vcuddy7rucedweqcm","title":"root/article.pdf","filename":"article.pdf","extra":{"size_bytes":268391,"type":"file"}}],"references":{"cite":{"order":["ref1","ref2","ref3","ref4","ref5","ref6","ref7"]},"data":{"ref1":{"label":"ref1","enumerator":"1","url":"https://doi.org/10.1109/42.650881","html":"Creating connected representations of cortical gray matter for functional MRI visualization+IEEE Trans. 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