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Automated analysis of structural imaging such as lung Computed Tomography (CT) plays an increasingly important role in medical imaging applications. Despite significant progress in the development of image registration and segmentation methods, lung registration and segmentation remain a challenging task. In this paper, we present a novel image registration and segmentation approach, for which we develop a new mathematical formulation to jointly segment and register three-dimensional lung CT volumes. The new algorithm is based on a level-set formulation, which merges a classic Chan-Vese segmentation with the active dense displacement field estimation. Combining registration with segmentation has two key advantages: it allows to eliminate the problem of initializing surface based segmentation methods, and to incorporate prior knowledge into the registration in a mathematically justified manner, while remaining computationally attractive. We evaluate our framework on a publicly available lung CT data set to demonstrate the properties of the new formulation. The presented results show the improved accuracy for our joint segmentation and registration algorithm when compared to registration and segmentation performed separately.

Original publication

DOI

10.1016/j.compmedimag.2017.06.003

Type

Journal article

Journal

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Publication Date

04/2018

Volume

65

Pages

58 - 68

Addresses

Institute for Numerical Mathematics, Technische Universität München, Germany. Electronic address: piotr.swierczynski@ma.tum.de.