Scale is a widely used notion in medical image analysis that evolved in the form of scale-space theory where the key idea is to represent and analyze an image at various resolutions. Recently, a notion of local morphometric scale referred to as "tensor scale" was introduced using an ellipsoidal model that yields a unified representation of structure size, orientation and anisotropy. In the previous work, tensor scale was described using a 2-D algorithmic approach and a precise analytic definition was missing. Also, with previous framework, 3-D application is not practical due to computational complexity. This book, therefore, establishes an analytic definition of tensor scale in n-dimensional (n-D) images, provides an efficient computational solution for 2- and 3-D images and investigates its role in various medical imaging applications including image interpolation, filtering, and segmentation.