By Arthur E. Burgess, Francine L. Jacobson, Philip F. Judy (auth.), Michael F. Insana, Richard M. Leahy (eds.)
This e-book constitutes the refereed lawsuits of the seventeenth foreign convention on info Processing in scientific Imaging, IPMI 2001, held in Davis, CA, united states, in June 2001.
The fifty four revised papers awarded have been rigorously reviewed and chosen from seventy eight submissions. The papers are prepared in topical sections on target overview of photograph caliber, form modeling, molecular and diffusion tensor imaging, registration and structural research, practical picture research, fMRI/EEG/MEG, deformable registration, form research, and research of mind structure.
Read Online or Download Information Processing in Medical Imaging: 17th International Conference, IPMI 2001 Davis, CA, USA, June 18–22, 2001 Proceedings PDF
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Additional info for Information Processing in Medical Imaging: 17th International Conference, IPMI 2001 Davis, CA, USA, June 18–22, 2001 Proceedings
28 Craig K. Abbey and Miguel P. Eckstein We also use the following basic result from probability theory. Let x and y be two random variables such that x and T (y) have the same distribution. Then the expectation of some function of x, say g (x), is equivalent to an expectation in y according to g (x) x = g (T (y)) y . (11) This result can be found proved in many probability theory texts. Expectation of q Let us consider the expectation of q = (2o − 1) ∆n. The quantity of interest is deﬁned as q = (2o − 1) ∆n = 2 o∆n − ∆n = 2 o∆n .
This facilitates modality comparisons without knowledge of a gold standard. While the σ’s serve as a description of a modality’s reproducibility, the slope and intercept values describe the systematic error (or bias) of the modality. If one is conﬁdent in these estimates they could be employed to adjust and correct systematic error for each modality. Another interesting result of the experiments is the successful estimation of the distribution parameters to ﬁt the form of the true distribution.
G. Christensen , Gee  and others). In the cases of the passive and the active model, this prior distribution has the form: Passive: log p(u) = k1 + Active: log p(u) = k2 + − tC 2 −( − (6) a t ) C( − 2 a ) (7) where k1 and k2 are normalization constants. Note further that the standard multivariate normal distribution (mean=µ, covariance =Σ) has the form (k3 is similarly a normalization constant): log p(u) = k3 + −(u − µ)t Σ −1 (u − µ) 2 (8) By comparing equations (6) and (7) to equation (8), we can see that in both cases the material matrix C plays a similar role to the inverse of the covariance matrix (the stiﬀer the material is, the greater the coupling between the displacements of neighboring points and hence the smaller the eﬀective component of 40 Xenophon Papademetris et al.