Three-dimensional image registration using distributed parallel computing

Three-dimensional (3D) images have become increasingly popular in practice. They are commonly used in medical imaging applications. In such applications, it is often critical to compare two 3D images, or monitor a sequence of 3D images. To make the image comparison or image monitoring valid, the related 3D images should be geometrically aligned first, which is called image registration (IR). However, IR for 3D images would take much computing time, especially when a flexible method is considered, which does not impose any parametric form on the underlying geometric transformation. Here, the authors explore a fast-computing environment for 3D IR based on the distributed parallel computing. The selected 3D IR method is based on the Taylor's expansion and 3D local kernel smoothing. It is flexible, but involves much computation. The authors demonstrate that this fast-computing environment can effectively handle the computing problem while keeping the good properties of the 3D IR method. The method discussed here is therefore useful for applications involving big data.