Selective variational image segmentation combined with registration: models and algorithms

In this paper, I present some new and joint work on local and selective segmentation models and algorithms which have potential applications in medical imaging. First I review a familiar segmentation model of global energy minimization framework in two dimensions (three dimensions may be presented similarly). Then I discuss selective segmentation models and several refined models where pre-defined geometric constraints guide local segmentation. Such 2D models can be generalized to 3D and some brief experiments are given to demonstrate the ideas of the paper. Finally I discuss the use of image registration methods to obtain geometric constraints or equivalent initial contours towards an automatic segmentation framework. As mentioned, the work discussed here represents a small portion of results obtained in the Liverpool's Centre for Mathematical Imaging Techniques (CMIT) and is jointly carried out with collaborators; for this paper, these include Noor Badshah (Peshawar, Pakistan), Jian-ping Zhang and Bo Yu (Dalian, China), Lavdie Rada (Liverpool), Noppadol Chumchob (Silpakorn, Thailand), Carlos Brito (Yucatan, Mexico), and Derek A. Gould (Royal Liverpool University Hospital, Liverpool).