<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Attila Tanacs</style></author><author><style face="normal" font="default" size="100%">Zoltan Kato</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Yongsheng Ding</style></author><author><style face="normal" font="default" size="100%">Yonghong Peng</style></author><author><style face="normal" font="default" size="100%">Riyi Shi</style></author><author><style face="normal" font="default" size="100%">Kuangrong Hao</style></author><author><style face="normal" font="default" size="100%">Lipo Wang</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Fast linear registration of 3D objects segmented from medical images</style></title><secondary-title><style face="normal" font="default" size="100%">Biomedical Engineering and Informatics (BMEI)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Oct 2011</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Shanghai</style></pub-location><pages><style face="normal" font="default" size="100%">294 - 298</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4244-9351-7 </style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper a linear registration framework is used for medical image registration using segmented binary objects. The method is best suited for problems where the segmentation is available, but we also propose a general bone segmentation approach for CT images. We focus on the case when the objects to be registered differ considerably because of segmentation errors. We check the applicability of the method to bone segmentation of pelvic and thoracic CT images. Comparison is also made against a classical mutual information-based registration method. © 2011 IEEE.&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Conference paper</style></work-type><accession-num><style face="normal" font="default" size="100%">12436502 </style></accession-num><notes><style face="normal" font="default" size="100%">ScopusID: 84855764850doi: 10.1109/BMEI.2011.6098290</style></notes></record></records></xml>