  Images taken at different times or positions undergo transformations such as
rotation, scaling, skewing, and more. The process of aligning different images
which have undergone transformations can be done via registration. Registration
is desirable when analyzing time-series data for tracking, averaging, or
differential diagnoses of diseases. Efficient registration methods exist for
rigid (including linear or affine) transformations; however, for non-rigid
(also known as non-affine) transformations, current methods are computationally
expensive and time-consuming. In this report, I will explore the ability of a
deep neural network (DNN) and, more specifically, a deep convolutional neural
network (CNN) to efficiently perform non-rigid image registration. The
experimental results show that a CNN can be used for efficient non-rigid image
registration and in significantly less computational time than a conventional
Diffeomorphic Demons or Pyramiding approach.
