By Claire R. Donoghue, Anil Rao, Anthony M. J. Bull, Daniel Rueckert (auth.), Benoît M. Dawant, Gary E. Christensen, J. Michael Fitzpatrick, Daniel Rueckert (eds.)
ISBN-10: 3642313396
ISBN-13: 9783642313394
ISBN-10: 364231340X
ISBN-13: 9783642313400
This ebook constitutes the refereed complaints of the fifth foreign Workshop on Biomedical snapshot Registration, WBIR 2012, held in Nashville, Tennessee, united states, in July 2012. The 20 complete papers and eleven poster papers incorporated during this quantity have been rigorously reviewed and chosen from forty four submitted papers. They complete papers are equipped within the following topical sections: a number of photo units; mind; non-rigid anatomy; and frameworks and similarity measures.
Read Online or Download Biomedical Image Registration: 5th International Workshop, WBIR 2012, Nashville, TN, USA, July 7-8, 2012. Proceedings PDF
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Additional resources for Biomedical Image Registration: 5th International Workshop, WBIR 2012, Nashville, TN, USA, July 7-8, 2012. Proceedings
Example text
2 AQUIRC AQUIRC builds on the idea of registration circuits which was proposed as a consistency measure by Woods et al. [11] and Holden et al. [12]. A registration circuit involves three images A, B, and C and three transformations TAB, TBC, and TCA. As discussed by Fitzpatrick [13], using only one registration circuit can lead to an underestimation of registration error because the error made along one edge in the circuit may correct error introduced from a separate edge in the circuit. In this work, we expand upon the idea of registration circuits to multiple circuits.
Forsberg et al. Channel Coding and Probability Distribution Estimation A well-known issue with mutual information is how to estimate the needed probability distributions, since the straightforward solution of using a standard histogram is insufficient. A common approach is to use some sort of soft histograms where the intensity of each pixel is distributed fractionally over multiple bins. This will give a more stable and often close to continuous result. In this work, channel coding and B-splines have been used to achieve this, which in practice is similar to kernel density estimation introduced by Rosenblatt and Parzen to estimate probability density functions [7,9].
The image on the left is I registered to I , the left middle image is the magnitude of the growth model, the image in the middle right is the image , and on the right is the final error estimation and . As can be seen in Figure 7, the added of the registration between simulation error is apparent in the center of AQUIRC's error estimation figure, although it is difficult to identify the medium and small growth deformations from the background registration error estimation. The results from Experiment 3 are shown in Figure 8.
Biomedical Image Registration: 5th International Workshop, WBIR 2012, Nashville, TN, USA, July 7-8, 2012. Proceedings by Claire R. Donoghue, Anil Rao, Anthony M. J. Bull, Daniel Rueckert (auth.), Benoît M. Dawant, Gary E. Christensen, J. Michael Fitzpatrick, Daniel Rueckert (eds.)
by Edward
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