Simultaneous Image Segmentation and Registration and Applications

Image segmentation and registration play active roles in machine vision and image analysis. In particular, image registration helps segmenting images when they have low contrast and/or partial missing information. We explore the joint problem of segmenting and registering a template (e.g. current) image given a reference (e.g. past) image. We solve the joint problem by minimizing a functional that integrates Geodesic Active Contours and Nonlinear Elastic registration. The template image is modeled as a hyper-elastic material (St. Venant-Kirchho model) which undergoes deformations under applied forces. To segment the deforming template, a two-phase level set based energy is introduced together with a weighted total variation term that depends on gradient features of the deforming template. This particular choice allows for fast solution using the dual formulation of the total variation. This allows the segmenting front to accurately track spontaneous changes in the shape of objects embedded in the template image as it deforms. To solve the underlying registration problem we use gradient descent and adopt an implicit-explicit method and use the Fast Fourier Transform. This is a joint work with former PhD student Thomas Atta-Fosu.