Download Medical Imaging and Augmented Reality: 5th International by Hongen Liao, P.J. Eddie Edwards, Xiaochuan Pan, Yong Fan, PDF

By Hongen Liao, P.J. Eddie Edwards, Xiaochuan Pan, Yong Fan, Guang-Zhong Yang

This publication constitutes the refereed complaints of the fifth overseas Workshop on clinical Imaging and Augmented truth, MIAR 2010, held in Beijing, China, in September 2010. The 60 revised complete papers provided have been rigorously reviewed and chosen from 139 submissions. The papers are geared up in topical sections on snapshot segmentation, photograph registration, form modeling and morphometry, snapshot research, diffusion tensor photograph, computing device assisted intervention, scientific snapshot computing, visualization and alertness, segmentation and class, clinical photo realizing, image-guided surgical procedure, and augmented fact.

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Extra resources for Medical Imaging and Augmented Reality: 5th International Workshop, MIAR 2010, Beijing, China, September 19-20, 2010, Proceedings

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Subject Specific Shape Modeling with Incremental Mixture Models 29 Flg. 4. Illustration of the adaptability of the proposed IMM method to arbitrarily complex shapes. Unlike the IPCA which is biased by the normal subjects despite the model update, the proposed IMM is guided towards a solution in the shape space that is more plausible. References 1. : Active shape models. In: British Machine Vision Conference, pp. 266–275 (1992) 2. : Hypertrophic cardiomyopathy in the community: why we should care.

336–342 (2009) 8. : Tubular anisotrophy for 2D vessels segmentation. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 2286–2293 (2009) 9. : Fast extraction of minimal paths in 3D images and applications to virtual endoscopy. Medical Image Analysis 5(4), 281–299 (2001) 10. : Robust 3-D modeling of vasculature imagery using superellipsoids. IEEE Trans. on Medical Imaging 26(2), 223–237 (2006) 11. : A note on two problems in connections with graphs. Numerische Mathematic 1, 269–271 (1959) 12.

The dimensionally-increased eigenvector matrix is in the same form as a real eigenvector matrix, except that it does not balance the new data coefficients. , ⎛ An A′ = ⎜⎜ ⎝0 a r ⎞ PCA ⎟ ⎯⎯ ⎯→ μ ′′,U ′′, λ ′′ ⎟ ⎠ (3) where A′ and An are the appended and current coefficient matrices in the eigenspace, respectively. μ ′′ , U ′′ and λ ′′ are the mean, eigenvectors and eigenvalues of A′ , respectively. , U n+1 = U ′ ⋅ U ′′ and μ n+1 = μ n + U ′μ ′′ , where U n+1 and μn +1 are the rotated eigenvectors and the updated mean, respectively.

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