By Sebastien Ourselin, Marc Modat
This ebook constitutes the refereed lawsuits of the sixth overseas Workshop on Biomedical photograph Registration, WBIR 2014, held in London, united kingdom, in July 2014.
The sixteen complete papers and eight poster papers incorporated during this quantity have been rigorously reviewed and chosen from various submitted papers. the total papers are geared up within the following topical sections: computational potency, version dependent regularisation, optimisation, reconstruction, interventional software and alertness particular measures of similarity.
Read Online or Download Biomedical Image Registration: 6th International Workshop, WBIR 2014, London, UK, July 7-8, 2014. Proceedings PDF
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Additional resources for Biomedical Image Registration: 6th International Workshop, WBIR 2014, London, UK, July 7-8, 2014. Proceedings
1st proximal phalanx 10. 2nd proximal phalanx 14. 3rd distal tarsal 11. 2nd distal phalanx Fig. 1. (a) Micro-CT reconstruction of mouse hind limb with bones labelled. (b) Hierarchical representation of bones and their joints in the mouse hind limb. This representation provides the order in which model bones are registered to the sample mesh, which can be modiﬁed with ease for experimental purposes. The topology of the mouse hind limb was represented as a tree (or hierarchy) where nodes and connections correspond to bones and joints, respectively (Figure 1).
In Fig. 4, we illustrate the mean and standard deviation of the target overlap measure for each region. To minimize visual clutter we only include three techniques: a top ranking method from the top ﬁve techniques, SyN ; SymBA: Diﬀeomorphic Registration Based on Gradient Orientations 27 our proposed approach, SymBA; and an alternative method, ROMEO . Note that the performance of our approach tends to tightly match the performance of the top ranking method, and is consistently superior than the poor performing method.
We parametrized our approach with four coarse-to-ﬁne registration stages, where at each consequent registration stage we increase the image resolution as well as the resolution of the velocity ﬁeld being optimized. Registration performance is evaluated across all stages and is summarized in Table 1. Note that the processing time for each stage includes the processing time of the preceding stages. Hence, the complete registration with all four stages takes on average ﬁve minutes. Note that the gain in registration accuracy between stage three and four is relatively small, and that the registration with only the ﬁrst three stages takes on average one and a half minutes.