Non-Gaussian methods in biomedical imaging

Most statistical models for applications rely on the Gaussian assumption. Yet, in many realistic situations, the underlying variation or uncertainty is essentially non-Gaussian. In detection problems, for instance, the Gaussian assumption leads to false alarms in cases where the tail is a fatter one, such as in the case of the Laplace density function. In classification problems, the Gaussian model for variability may be too restrictive, and other models, such as the Generalized Gaussian density function, are more appropriate. We will present examples of such models as applied to applications with multiple images, and show performance in two applications: functional magnetic resonance imaging, and stem cell classification.