Level set estimation in medical imaging

Rapid and accurate extraction of level sets and isoconcentration surfaces from noisy medical images is a common problem arising in a variety of contexts, such as estimating regions in which uptake of a pharmaceutical has exceeded some critical value or identifying areas of brain activity in neuroimaging. In general, a level set is the set 5 on which a function f exceeds a critical value (e.g. S = {x : f(x) > gamma}). Boundaries of level sets and isoconcentration surfaces typically constitute manifolds embedded in the high-dimensional observation space. The tree structures underlying our method are constructed by minimizing a complexity regularized data-fitting term over a family of dyadic partitions. Our method specifically aims to minimize an error metric sensitive to both deviations in the location of the level set and the rate of change of the surface intensity or activity level statistic in the vicinity of the level set. Explicit extraction of level sets using multiresolution trees can be implemented in near linear time; simulations demonstrate that explicit level set extraction methods can achieve significantly higher accuracy in neuroimaging applications than more indirect approaches