By Moo K. Chung
"The titanic quantity of nonstandard high-dimensional mind imaging facts being generated is frequently tricky to research utilizing present suggestions. This problem in mind photograph research calls for new computational methods and ideas. yet not one of the examine papers or books within the box describe the quantitative innovations with distinctive illustrations of exact imaging info and machine codes. utilizing MATLAB® and case Read more...
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Extra info for Statistical and Computational Methods in Brain Image Analysis
Sample text
The other major novelty of the SurfStat package is the inclusion of mixedeffects models that can explicitly model the within-subject correlation of image scans of the same subject. SurfStat package is therefore better suited for longitudinally collected study designs than other packages. 2 Public Image Database Other than the data sets we provided in the book, a large longitudinal image dataset is also available to the public. edu/ADNI) are two widely distributed data sets. org). ca/brainweb). The SBD contains a set of realistic MRI data volumes produced by an MRI simulator.
2, the first four columns of binary images are lesions of stroke patients with dysphagia who got better (n1 = 58). The next four columns are lesions of stroke patients with dysphagia who did not get better (n2 = 23). We are interested in determining the localized regions of lesions and if there is any pixel showing group differences. This can be done by testing on the significance of proportions. 1 One Sample Test For a collection of binary images, each image has regions of activation. The activated and nonactivated regions are encoded as 1 and 0 respectively, which can be modeled as a Bernoulli distribution.
To guarantee that the limit converges to the true maximum likelihood estimator, additional conditions are needed [47, 397]. The difficulty of implementing the EM-algorithm is at the E-step where we need to compute the conditional expectation Q(Θ|Θj−1 , y). 3 1 k k ln f (Y, Y m |Θ). j=1 Two-Components Gaussian Mixture As an illustration, two-components Gaussian mixture model will be fitted using the EM-algorithm. Extension of three-components mixture model is similar. The image intensity will be modeled as a Gaussian mixture of the form f (y) = p1 f1 (y) + p2 f2 (y) 38 Statistical and Computational Methods in Brain Image Analysis where p1 +p2 = 1 and f1 ∼ N (μ1 , σ12 ) and f2 ∼ N (μ2 , σ22 ) are all known.