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Download Biomedical Image Registration: Third International Workshop, by A. Bardera, M. Feixas, I. Boada, J. Rigau, M. Sbert (auth.), PDF

By A. Bardera, M. Feixas, I. Boada, J. Rigau, M. Sbert (auth.), Josien P. W. Pluim, Boštjan Likar, Frans A. Gerritsen (eds.)

This ebook constitutes the completely refereed post-proceedings of the 3rd foreign Workshop on Biomedical photograph 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 publication. The papers hide all parts of biomedical photograph registration; tools 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 tools, model-based registration, desktop built-in surgical procedure, image-guided remedy and prognosis, remedy making plans, serial experiences, morphometry, biomechanics, snapshot retrieval, snapshot tiling and snapshot fusion, computational and empirical accuracy, comparability experiences, and actual models.

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Additional info for Biomedical Image Registration: Third International Workshop, WBIR 2006, Utrecht, The Netherlands, July 9-11, 2006. Proceedings

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A probing experiment corresponds to the detailed characterization of an objective function with respect to certain transformation parameters. It helps to describe the capture range (the interval over which the objective function does not contain any local optima besides the solution) and accuracy of the objective function. We compared the behavior of our method to that of three others: joint entropy [9], negative mutual information [5, 11] and KL-divergence [1]. The first two of these methods only consider data information and the third one relies on previous registration results.

With the proposed hybrid approach that uses MI for the first levels and CC for the last few levels, two important properties of these similarity measures can be seamlessly combined in a unique manner, namely the multi-modal capabilities of MI with the robustness of CC without increasing the computational complexity of the underlying algorithm. ch). Local Intensity Mapping for Hierarchical Non-rigid Registration 33 References 1. : A survey of medical image registration. Medical Image Analysis 2 (1998) 1–36 2.

Here, we create a simple binary stiffness map: the transformation is constrained to be stiffer over bony structures than over soft tissue structures. Results obtained when using two stiffness values, one for the bones and the other for soft tissue, improve when compared to those obtained with a single value. 3 Results Our approach has been evaluated on two types of images: whole body mouse scans and upper body human scans. 125mm3. 9375x3mm3. Figure 1 shows results obtained with the skeletons 22 X.

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