title: Skeleton Graph Matching vs. Maximum Weight Cliques aorta registration techniques.
abstract: Vascular diseases are one of the most challenging health problems in developed countries. Past as well as ongoing research activities often focus on efficient, robust and fast aorta segmentation, and registration techniques. According to this needs our study targets an abdominal aorta registration method. The investigated algorithms make it possible to efficiently segment and register abdominal aorta in pre- and post-operative Computed Tomography (CT) data. In more detail, a registration technique using the Path Similarity Skeleton Graph Matching (PSSGM), as well as Maximum Weight Cliques (MWCs) are employed to realise the matching based on Computed Tomography data. The presented approaches make it possible to match characteristic voxels belonging to the aorta from different Computed Tomography (CT) series. It is particularly useful in the assessment of the abdominal aortic aneurysm treatment by visualising the correspondence between the pre- and post-operative CT data. The registration results have been tested on the database of 18 contrast-enhanced CT series, where the cross-registration analysis has been performed producing 153 matching examples. All the registration results achieved with our system have been verified by an expert. The carried out analysis has highlighted the advantage of the MWCs technique over the PSSGM method. The verification phase proves the efficiency of the MWCs approach and encourages to further develop this methods.
authors.affiliation: Department of Computer Science and Medical Equipment, Faculty of Biomedical Engineering, Silesian University of Technology, ul. Roosevelta 40, 41-800 Zabrze, Poland. Electronic address: jczajkowska@polsl.pl.
authors.forename: Joanna
authors.initials: J
authors.lastname: Czajkowska
authors.affiliation: Research Group for Pattern Recognition, University of Siegen, Hoelderlinstrasse 3, D-57076 Siegen, Germany.
authors.forename: C
authors.initials: C
authors.lastname: Feinen
authors.affiliation: Research Group for Pattern Recognition, University of Siegen, Hoelderlinstrasse 3, D-57076 Siegen, Germany.
authors.forename: M
authors.initials: M
authors.lastname: Grzegorzek
authors.affiliation: University of Applied Sciences Koblenz, Department of Mathematics and Technology, Joseph-Rovan-Allee 2, 53424 Remagen, Germany.
authors.forename: M
authors.initials: M
authors.lastname: Raspe
authors.affiliation: Herz-Jesu Hospital Department of Diagnostic and Interventional Radiology and Nuclear Medicine, Südring 8, 56428 Dernbach, Germany.
authors.forename: R
authors.initials: R
authors.lastname: Wickenhöfer
mesh_terms: D000465:Algorithms; D059925:Anatomic Landmarks / Q000000981:diagnostic imaging; D017544:Aortic Aneurysm, Abdominal / Q000000981:diagnostic imaging*; D001027:Aortography / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012863:Skeleton / Q000000981:diagnostic imaging*; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods
keywords: Hybrid level-set active contour; Image registration; Maximum Weight Cliques; Skeleton Graph Matching; Skeletonisation