To the best of our knowledge, nobody in the literature associated these regularization terms with the SSD similarity measure and applied them to the nonrigid registration of medical images.
This new numerical method has none of the high order time step stability constraints that are usually associated with curvature regularization.
The latest algorithm is associated with Bayesian regularization in order to reduce over-fitting during the learning phase [ 14].
As α is varied the algorithm trades off the cost associated with the regularization penalty against the cost associated with the data.
Typical regularization terms associated with image registration problems include curvature, diffusion, elasticity, and fluid.
Therefore, mathematical models and large-scale fast algorithms associated with (ell_{1}) regularization can be seen everywhere in compressed sensing, signal/image processing, machine learning, statistics, and many other fields [6 9].
Optimizing the regularization parameter associated with the individual chromophores is an important element to obtaining a good reconstruction.
We have used cross-validation to select the regularization parameter λ associated to the Group Lasso and Trace Norm regularizers, as well as the parameters λ and K in the case of the Matrix Factorization approach (K∈ [ 1,2,3,…,10], λ∈ [ 10−3,10−2,10−1,…,103]): the rows of Y are randomly partitioned into three groups of approximately equal size.
With the proposed regularization technique, problems associated with the flow-induced irregularity of particle distribution in the Lagrangian finite point scheme are circumvented.
Here it is worth noting that the weight of regularization is directly associated with image resolution [ 33].
