By A. Bardera, M. Feixas, I. Boada, J. Rigau, M. Sbert (auth.), Josien P. W. Pluim, Boštjan Likar, Frans A. Gerritsen (eds.)
This booklet constitutes the completely refereed post-proceedings of the 3rd overseas Workshop on Biomedical snapshot Registration, WBIR 2006, held in Utrecht, The Netherlands, in July 2006.
The 20 revised complete papers and 18 revised poster papers provided have been conscientiously reviewed and chosen for inclusion within the e-book. The papers conceal all parts of biomedical photo registration; equipment of registration, biomedical purposes, and validation of registration. themes addressed are measures of similarity, 2D/3D/4D, nonrigid deformation, intra- or inter-modality registration, intra- or inter-subject registration, optimization equipment, model-based registration, laptop built-in surgical procedure, image-guided treatment and analysis, therapy making plans, serial stories, morphometry, biomechanics, photograph retrieval, photo tiling and picture fusion, computational and empirical accuracy, comparability reviews, and actual models.
Read or Download Biomedical Image Registration: Third International Workshop, WBIR 2006, Utrecht, The Netherlands, July 9-11, 2006. Proceedings PDF
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Additional info for Biomedical Image Registration: Third International Workshop, WBIR 2006, Utrecht, The Netherlands, July 9-11, 2006. Proceedings
3. C. G¨ utter, C. Xu, F. Sauer, and J. Hornegger. Learning based non-rigid multimodal image registration using kullback-leibler divergence. In G. Gerig and J. Duncan, editors, MICCAI, volume 2 of LNCS, pages 255–263. Springer, 2005. 4. M. L. Grimson. Multi-modal Volume Registration Using Joint Intensity Distributions. In MICCAI, LNCS, pages 1057–1066. Springer, 1998. 5. F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens. Multimodality image registration by maximization of mutual information.
We draw similarities between the Dirichlet and other encodings of prior information on distribution parameters. Such an analysis facilitates a better understanding of the advantages of the Dirichlet encoding and it creates a tight link with other methods. We start our analysis by showing that the maximum likelihood solution for the multinomial parameters Θ is equivalent to the histogrammed version of the observed intensity pairs drawn from the corresponding input images. Then, using these results, we demonstrate that the MAP estimate of the multinomial parameters (with a Dirichlet prior on them) is the histogram of the pooled data, which is the combination of the currently observed samples and the hypothetical prior counts encoded by the Dirichlet distribution.
We carried out the probing experiments in Multi-modal Image Registration Using Dirichlet-Encoded Prior Information (a) 39 (b) Fig. 1. 2D slices of a corresponding (a) MRI and (b) EPI data set pair the y- (or vertical) direction. This is the parameter along which a strong local optimum occurs in the case of all the previously introduced objective functions. In order to avoid any biases towards the zero solution, we oﬀset the input EPI image by 15 mm along the probing direction. Thus the local optimum is expected to be located at this oﬀset position – and not at zero – on the probing curves.
Biomedical Image Registration: Third International Workshop, WBIR 2006, Utrecht, The Netherlands, July 9-11, 2006. Proceedings by A. Bardera, M. Feixas, I. Boada, J. Rigau, M. Sbert (auth.), Josien P. W. Pluim, Boštjan Likar, Frans A. Gerritsen (eds.)