title: Bayesian tracking of elongated structures in 3D images.
abstract: Tracking of tubular elongated structures is an important goal in a wide range of biomedical imaging applications. A Bayesian tube tracking algorithm is presented that allows to easily incorporate a priori knowledge. Because probabilistic tube tracking algorithms are computationally complex, steps towards a computational efficient implementation are suggested in this paper. The algorithm is evaluated on 2D and 3D synthetic data with different noise levels and clinical CTA data. The approach shows good performance on data with high levels of Gaussian noise.
authors.affiliation: Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics Erasmus MC - University Medical Center Rotterdam. michiel@erasmusmc.nl
authors.forename: Michiel
authors.initials: M
authors.lastname: Schaap
authors.forename: Ihor
authors.initials: I
authors.lastname: Smal
authors.forename: Coert
authors.initials: C
authors.lastname: Metz
authors.forename: Theo
authors.initials: T
authors.lastname: van Walsum
authors.forename: Wiro
authors.initials: W
authors.lastname: Niessen
mesh_terms: D000465:Algorithms; D000792:Angiography / Q000379:methods*; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008955:Models, Cardiovascular; 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; D014057:Tomography, X-Ray Computed / Q000379:methods*