<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heinrich, Mattias P.</style></author><author><style face="normal" font="default" size="100%">Jenkinson, Mark</style></author><author><style face="normal" font="default" size="100%">Jenkinson, Mark</style></author><author><style face="normal" font="default" size="100%">Julia A. Schnabel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Globally optimal deformable registration on a minimum spanning tree using dense displacement sampling.</style></title><secondary-title><style face="normal" font="default" size="100%">Medical image computing and computer-assisted intervention : MICCAI 2012 International Conference on Medical Image Computing and Computer-Assisted Intervention</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Med Image Comput Comput Assist Interv</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Imaging, Three-Dimensional</style></keyword><keyword><style  face="normal" font="default" size="100%">Lung</style></keyword><keyword><style  face="normal" font="default" size="100%">Pattern Recognition, Automated</style></keyword><keyword><style  face="normal" font="default" size="100%">Radiographic Image Enhancement</style></keyword><keyword><style  face="normal" font="default" size="100%">Radiographic Image Interpretation, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Radiography, Thoracic</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity and Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Subtraction Technique</style></keyword><keyword><style  face="normal" font="default" size="100%">Tomography, X-Ray Computed</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">115-22</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Deformable image registration poses a highly non-convex optimisation problem. Conventionally, medical image registration techniques rely on continuous optimisation, which is prone to local minima. Recent advances in the mathematics and new programming methods enable these disadvantages to be overcome using discrete optimisation. In this paper, we present a new technique deeds, which employs a discrete dense displacement sampling for the deformable registration of high resolution CT volumes. The image grid is represented as a minimum spanning tree. Given these constraints a global optimum of the cost function can be found efficiently using dynamic programming, which enforces the smoothness of the deformations. Experimental results demonstrate the advantages of deeds: the registration error for the challenging registration of inhale and exhale pulmonary CT scans is significantly lower than for two state-of-the-art registration techniques, especially in the presence of large deformations and sliding motion at lung surfaces.</style></abstract><issue><style face="normal" font="default" size="100%">Pt 3</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/23286121?dopt=Abstract</style></custom1></record></records></xml>