Paper Info
Title
Airway Segmentation, Skeletonization, And Tree Matching To Improve Registration Of 3d Ct Images With Large Opacities In The Lungs
Abstract
In this work, we address the registration of pulmonary images, representing the same subject, with large opaque regions within the lungs, and with possibly large displacements. We propose a hybrid method combining alignment based on gray levels and landmarks within the same cost function. The landmarks are nodes of the airway tree obtained by specially developed segmentation and skeletonization algorithms. The former uses the random walker approach, whereas the latter exploits the minimum spanning tree constructed by the Dijkstra's algorithm, in order to detect end-points and bifurcations. Airway trees from different images are matched by a modified best-first-search algorithm with a specially designed distance function. The proposed method was evaluated on computed-tomography images of subjects with acute respiratory distress syndrome, acquired at significantly different mechanical ventilation conditions. It achieved better results than registration based only on gray levels, but also better than hybrid registration using a standard airway-segmentation method.
Year
DOI
Venue
2016
10.1007/978-3-319-46418-3_35
COMPUTER VISION AND GRAPHICS, ICCVG 2016
Field
DocType
Volume
Computer vision,Pattern recognition,Computer science,Segmentation,Acute respiratory distress,Metric (mathematics),Skeletonization,Artificial intelligence,Random walker algorithm,Airway,Minimum spanning tree,Dijkstra's algorithm
Conference
9972
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
14
8
Name
Order
Citations
PageRank
Duván Alberto Gómez Betancur100.34
Anna Fabijańska2337.30
Leonardo Florez-Valencia3155.70
Alfredo Morales Pinzón410.72
Eduardo E. Dávila Serrano500.68
Jean-Christophe Richard611.06
Maciej Orkisz731524.14
Marcela Hernández Hoyos822815.91