Meztikora Mesh 65 In order to be consistent with the Mesh, cell types have to be con? This approach provides portable multithreading and hides the complexity of differing thread implementations on the many systems supported by ITK. Installation with the toolkit are a helpful resource for learning how to use ITK components but are not essential for the use of the toolkit itself. It is also possible to specify boundary cells explicitly, to indicate different connectivity from the implied neighborhood relationships, or to store information on the boundaries of cells. Only when no new options appear after a con? In particular the Mesh class is parameterized over the PixelType and the dimension of the space.
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Despite this, the fixed initial transform does not contribute to the optimization process. It is only used to access the fixed image from the virtual image space where the metric evaluation happens. Virtual images are a new concept added to the ITKv4 registration framework, which potentially lets us to do the registration process in a physical domain totally different from the fixed and moving image domains. In fact, the region over which metric evaluation is performed is called virtual image domain.
This domain defines the resolution at which the evaluation is performed, as well as the physical coordinate system. The legacy intuitive registration framework can be considered as a special case where the virtual domain is the same as the fixed image domain. As this case practically happens in most of the real life applications, the virtual image is set to be the same as the fixed image by default.
However, the user can define the virtual domain differently than the fixed image domain by calling either SetVirtualDomain or SetVirtualDomainFromImage.
In this example, like the most examples of this chapter, the virtual image is considered the same as the fixed image. Since the registration process happens in the fixed image physical domain, the fixed initial transform maintains its default value of identity and does not need to be set.
In the next section of this chapter, you will get a better understanding from behind the scenes of the registration process when the initial fixed transform is not identity.
Another option is to initialize the output optimizable transform directly. In this approach, a transform object is created, initialized, and then passed to the registration method via SetInitialTransform. At this point the registration method is ready for execution. The optimizer is the component that drives the execution of the registration.
However, the ImageRegistrationMethodv4 class orchestrates the ensemble to make sure that everything is in place before control is passed to the optimizer.
It is usually desirable to fine tune the parameters of the optimizer. Each optimizer has particular parameters that must be interpreted in the context of the optimization strategy it implements.
The optimizer used in this example is a variant of gradient descent that attempts to prevent it from taking steps that are too large. At each iteration, this optimizer will take a step along the direction of the itk::ImageToImageMetricv4 derivative.
Each time the direction of the derivative abruptly changes, the optimizer assumes that a local extrema has been passed and reacts by reducing the step length by a relaxation factor. The reducing factor should have a value between 0 and 1. This factor is set to 0.
Also, the default value for the initial step length is 1, and this value can be changed manually with the method SetLearningRate. In addition to manual settings, the initial step size can also be estimated automatically, either at each iteration or only at the first iteration, by assigning a ScalesEstimator as will be seen in later examples. After several reductions of the step length, the optimizer may be moving in a very restricted area of the transform parameter space. By the method SetMinimumStepLength , the user can define how small the step length should be to consider convergence to have been reached.
This is equivalent to defining the precision with which the final transform should be known. User can also set some other stop criteria manually like maximum number of iterations. The number of iterations involved in computations are defined by the convergence window size via SetConvergenceWindowSize which is shown in later examples of this chapter.
This maximum number is defined with the method SetNumberOfIterations. These criteria need to be defined before registration starts. Otherwise, the default values will be used. In this example, we run a simple registration in one level with no space shrinking or smoothing on the input data.
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