title: Joint segmentation-registration of organs using geometric models.
abstract: In this paper, we present a novel method for the segmentation of the organs found in CT and MR images. The proposed algorithm utilizes the shape model of the target organ to gain robustness in the case where the objective organ is surrounded by other organs or tissue with the similar intensity profile. The algorithm labels the image based on the graph-cuts technique and incorporates the shape prior using a technique based on level-sets. The method requires proper registration of the shape template for an accurate segmentation, and we propose a unified registration-segmentation framework to solve this problem. Furthermore, to reduce the computational cost, the algorithm is designed to run on watershed regions instead of voxels. The accuracy of the algorithm is shown on the medical examples.
authors.affiliation: Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180, USA. ayvaca@cs.rpi.edu
authors.forename: Alper
authors.initials: A
authors.lastname: Ayvaci
authors.forename: Daniel
authors.initials: D
authors.lastname: Freedman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014781:Viscera / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*; D051598:Whole Body Imaging / Q000379:methods