Polina Golland
MATHEMATICS COLLOQUIUM
Speaker: Polina Golland Abstract. We consider the problem of capturing statistical variability in anatomical images. Unlike the classical approaches that model population variability with unimodal (Gaussian) distributions, we propose a richer set of mixture models better capable of capturing heterogeneous anatomy. Based on the model, we derive an efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. The output of the algorithm is a small number of template images that represent different modes in a population. This is in contrast with traditional, hypothesis-driven computational anatomy approaches that assume a single template to represent a population of images. The experimental results demonstrate that the algorithm can discover interesting sub-populations, suggesting applications in prior-guided segmentation and statistical analysis of anatomical differences in clinical studies. Joint work with Mert Sabuncu and Serdar Balci. |