title: 3D-3D tubular organs registration based on bifurcations for the CT images.
abstract: The registration of tubular organs (pulmonary tracheobronchial tree or vasculature) of 3D medical images is critical in various clinical applications such as surgical planning and radiotherapy. In this paper, we present a novel method for tubular organs registration based on the automatically detected bifurcation points of the tubular organs. We first perform a 3D tubular organ segmentation method to extract the centerlines of tubular organs and radius estimation in both planning and respiration-correlated CT (RCCT) images. This segmentation method automatically detects the bifurcation points by applying Adaboost algorithm with specially designed filters. We then apply a rigid registration method which minimizes the least square error of the corresponding bifurcation points between the planning CT images and the respiration-correlated CT images. Our method has over 96% success rate for detecting bifurcation points.We present very promising results of our method applied to the registration of the planning and respiration-correlated CT images. On average, the mean distance and the root-mean-square error (RMSE) of the corresponding bifurcation points between the respiration-correlated images and the registered planning images are less than 2.7 mm.
authors.affiliation: Center for Computational Biomedicine Imaging and Modeling (CBIM), the Department of Biomedical Engineering, Rutgers, the State University of New Jersey, USA. jhzhou@eden.rutgers.edu
authors.forename: Jinghao
authors.initials: J
authors.lastname: Zhou
authors.forename: Sukmoon
authors.initials: S
authors.lastname: Chang
authors.forename: Dimitris
authors.initials: D
authors.lastname: Metaxas
authors.forename: Gig
authors.initials: G
authors.lastname: Mageras
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*