Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is efficient, has very few free parameters to tune, and the authors show how to tune the few remaining parameters. Results show that the method reliably aligns various datasets including two facial datasets and two medical datasets of prostate and brain MRI images and demonstrates efficiency in terms of performance and a reduction of the computational cost.
Hemorrhagic insult is a major source of morbidity and mortality in both adults and newborn babies in the developed countries. The mechanisms underlying the non-traumatic rupture of cerebral vessels are not fully clear, but there is strong evidence that stress, which is associated with an increase in arterial blood pressure, plays a crucial role in the development of acute intracranial hemorrhage (ICH), and alterations in cerebral blood flow (CBF) may contribute to the pathogenesis of ICH. The problem is that there are no effective diagnostic methods that allow for a prognosis of risk to be made for the development of ICH. Therefore, quantitative assessment of CBF may significantly advance the underst