{"version":1,"kind":"Article","sha256":"","slug":"94","location":"","dependencies":[],"doi":"10.54294/vyt8n2","frontmatter":{"title":"Computational Geometry Computation and KNN Segmentation in ITK","abstract":"This work describes the implementation of computational geometry algorithms developed within the Insight Toolkit (ITK): Distance Transform (DT), Voronoi diagrams, k Nearest Neighbor (kNN) transform, and finally a K Nearest Neighbor classifier for multichannel data, that is used for supervised segmentation. We have tested this algorithm for 2D and 3D medical datasets, and the results are excellent in terms of accuracy and performance. One of the strongest points of the algorithms described here is that they can be used for many other applications, because they are based on the ordered propagation paradigm. This idea consists in actually not raster scan the image but rather in start from the image objects and propagate them until the image is totally filled. This has been demonstrated to be a good approach in many algorithms as for example, computation of Distance Transforms, Voronoi Diagrams, Fast Marching, skeletons computation, etc. We show here that these algorithms have low computational complexity and it provides excellent results for clinical applications as the segmentation of brain MRI.","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":["ordered propagation","KNN Transform","multichannel segmentation","KNN Segmentation","Computational Geometry","Voronoi Diagrams","Distance Transform"],"authors":[{"name":"Cardenes, Ruben","email":"ruben@ctm.ulpgc.es","affiliations":["University of Las Palmas de Gran Canaria"],"corresponding":true},{"name":"Sanchez, Manuel Rene","affiliations":[]},{"name":"Ruiz-Alzola, Juan","affiliations":[]}],"date_submitted":"2006-06-30T11:58:12Z","external_publication_id":94,"revision_cids":["bafkreih37mqw4a3ocbtal6a726z6o7r7uh6zsuy2k6wrokc22rqor7a42e"]},"mdast":{"type":"root"},"downloads":[{"url":"https://ipfs.desci.com/ipfs/bafkreig6hmgwtrqtdlnouufp2nwhudmfds7h3xptnjzofmhgooxioodaje","title":"root/insight-journal-metadata.json","filename":"insight-journal-metadata.json","extra":{"size_bytes":6554,"type":"file"}},{"url":"https://ipfs.desci.com/ipfs/bafkreih26cmau6c2oanfjq2fipgroharbzfesz4qggwkee66wrjxurddke","title":"root/comments.md","filename":"comments.md","extra":{"size_bytes":186,"type":"file"}},{"url":"https://dweb.link/ipfs/bafkreigpraxpslwmonzvanrbxolcbjp5qr2jqothlchgxv5k6zipeypvdq","title":"root/article.pdf","filename":"article.pdf","extra":{"size_bytes":259229,"type":"file"}}],"references":{"cite":{"order":["ref1","ref2","ref3","ref4","ref5","ref6","ref7","ref8","ref9","ref10","ref11"]},"data":{"ref1":{"label":"ref1","enumerator":"1","url":"https://doi.org/10.1016/0730-725x(93)90417-c","html":"Stability of three supervised segmentation techniques+Magnetic Resonance Imaging+11+95+106+1993+L. Clarke+R. Velthuizen+S. Phuphanich+J. Schellenberg+J. Arrington+M. Silbiger"},"ref2":{"label":"ref2","enumerator":"2","html":"Brainweb: online interface to a 3D MRI simulated brain database+In Neuroimage+5+1997+C. Cocosco+V. Kollokian+A. Evans"},"ref3":{"label":"ref3","enumerator":"3","html":"Fast k-nn classification with an optimal k-distance transformation algorithm+Proc. 10th European Signal Processing Conf+2+1368+2000+1+O. Cuisenaire+B. Macq"},"ref4":{"label":"ref4","enumerator":"4","url":"https://doi.org/10.1016/0146-664x(80)90054-4","html":"Euclidean distance mapping+Computer Graphics Image Processor+1+2+248+1980+4+P. Danielsson"},"ref5":{"label":"ref5","enumerator":"5","html":"1973+R. Duda+P. Hart+Scene Analysis"},"ref6":{"label":"ref6","enumerator":"6","html":"second edition+2005+L. Ibanez+W. Schroeder+L. Ng+J. Cates"},"ref7":{"label":"ref7","enumerator":"7","html":"Concepts and Applications of Voronoi Diagrams+1992+A. Okabe+B. Boots+K. Sugihara"},"ref8":{"label":"ref8","enumerator":"8","url":"https://doi.org/10.1016/1049-9660(92)90050-d","html":"Neighborhoods for distance transformations using ordered propagation+CVGIP+56+3+399+409+1992+I. Ragnemalm+Understanding Image"},"ref9":{"label":"ref9","enumerator":"9","url":"https://doi.org/10.1109/visual.1996.567752","html":"The Visualization Toolkit:An Object Oriented Approach to Computer Graphics+2004+W. Schroeder+K. Martin+and W. Lorensen"},"ref10":{"label":"ref10","enumerator":"10","url":"https://doi.org/10.1109/34.19041","html":"An efficient uniform cost algorithm applied to distance transforms+IEEE Transactions on Pattern Analysis an Machine Intelligence+11+4+425+429+1989+B. Verwer+P. Verbeek+S. Dekker"},"ref11":{"label":"ref11","enumerator":"11","url":"https://doi.org/10.1016/0167-8655(96)00036-0","html":"Fast k-nn classification for multichannel image data+Pattern Recognition Letters+1+3+4+721+1996+S. Warfield"}}}}