Optimal Mass Transport for Registration and Warping

  • Authors:

  • Steven Haker;Lei Zhu;Allen Tannenbaum;Sigurd Angenent

  • Affiliations:

  • Surgical Planning Laboratory Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. haker@bwh.harvard.edu;Departments of Electrical, Computer and Biomedical Engineering, 777 Atlantic Drive, Georgia Institute of Technology, Atlanta, GA 30332-0250, USA. zlzl@ece.gatech.edu;Departments of Electrical, Computer and Biomedical Engineering, 777 Atlantic Drive, Georgia Institute of Technology, Atlanta, GA 30332-0250, USA. tannenba@ece.gatech.edu;Department of Mathematics, University of Wisconsin, Madison, WI 53706, USA. angenent@math.wisc.edu

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2004

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Abstract

Image registration is the process of establishing a common geometric reference frame between two or more image data sets possibly taken at different times. In this paper we present a method for computing elastic registration and warping maps based on the Monge–Kantorovich theory of optimal mass transport. This mass transport method has a number of important characteristics. First, it is parameter free. Moreover, it utilizes all of the grayscale data in both images, places the two images on equal footing and is symmetrical: the optimal mapping from image A to image B being the inverse of the optimal mapping from B to A. The method does not require that landmarks be specified, and the minimizer of the distance functional involved is unique; there are no other local minimizers. Finally, optimal transport naturally takes into account changes in density that result from changes in area or volume. Although the optimal transport method is certainly not appropriate for all registration and warping problems, this mass preservation property makes the Monge–Kantorovich approach quite useful for an interesting class of warping problems, as we show in this paper. Our method for finding the registration mapping is based on a partial differential equation approach to the minimization of the L2 Kantorovich–Wasserstein or “Earth Mover's Distance” under a mass preservation constraint. We show how this approach leads to practical algorithms, and demonstrate our method with a number of examples, including those from the medical field. We also extend this method to take into account changes in intensity, and show that it is well suited for applications such as image morphing.