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Swierczynski, P.* ; Papiez, B.W.* ; Schnabel, J.A.* ; MacDonald, C.J.*

A level-set approach to joint image segmentation and registration with application to CT lung imaging.

Comput. Med. Imaging Graph. 65, 58-68 (2018)
Verlagsversion DOI
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Atlas-based Segmentation ; Joint Image Segmentation And Registration ; Level-set Registration
ISSN (print) / ISBN 0895-6111
e-ISSN 1879-0771
Zeitschrift Computerized Medical Imaging and Graphics
Quellenangaben Band: 65, Heft: , Seiten: 58-68 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Kidlington
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)