Get Medical Image Computing and Computer-Assisted Intervention – PDF

By Polina Golland, Nobuhiko Hata, Christian Barillot, Joachim Hornegger, Robert Howe (eds.)

ISBN-10: 331910442X

ISBN-13: 9783319104423

ISBN-10: 3319104438

ISBN-13: 9783319104430

The three-volume set LNCS 8673, 8674, and 8675 constitutes the refereed court cases of the seventeenth foreign convention on clinical photograph Computing and Computer-Assisted Intervention, MICCAI 2014, held in Boston, MA, united states, in September 2014. in response to rigorous peer stories, this system committee rigorously chosen 253 revised papers from 862 submissions for presentation in 3 volumes. The fifty three papers integrated within the 3rd quantity were geared up within the following topical sections: form and inhabitants research; mind; diffusion MRI; and laptop learning.

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Additional resources for Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part III

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21(5), 525–537 (2002) 5. : Hybrid Monte Carlo. Physics Letters 195, 216–222 (1987) 6. : Procrustes methods in the statistical analysis of shape. J. Royal Stat. Soc. 53(2), 285–339 (1991) 7. : A generative shape regularization model for robust face alignment. , Zisserman, A. ) ECCV 2008, Part I. LNCS, vol. 5302, pp. 413–426. Springer, Heidelberg (2008) 8. : Comparing distributions and shapes using the kernel distance. In: ACM Symp. Comp. , pp. 47–56 (2011) 9. : Shape manifolds, Procrustean metrics, and complex projective spaces.

In contrast to other deformation based tools for shape analysis, our approach is non-parametric and naturally captures the probability of a shape belonging to a population. Although it does not provide physical measurements of displacement, these can be computed by deformation or a distance transform to the population median. Our method was successfully tested with synthetic data, where we were able to clearly separate groups and localize an artificially induced shape change. In addition, our real data experiment supports previous results on shape differences in the caudate of females with SPD, namely, a right sided shape difference in the body of the caudate.

In Sec. 4, we present experimental results for synthetic and real datasets. Sec. 5 concludes the paper with a summary. 2 Depth-Ordering of Shapes A challenge in shape analysis is that there is no canonical ordering of shapes. Here we leverage the work on ordering of functions from the statistics literature and extend it to shapes [6]. Once defined the ordering can be used to generalize traditional order statistics, such as the median or the inter-quartile range, to shape ensembles. Recently, band-depth has been proposed as one possible way for ordering functions [9].

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Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part III by Polina Golland, Nobuhiko Hata, Christian Barillot, Joachim Hornegger, Robert Howe (eds.)


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