title: Modeling of intensity priors for knowledge-based level set algorithm in calvarial tumors segmentation.
abstract: In this paper, an automatic knowledge-based framework for level set segmentation of 3D calvarial tumors from Computed Tomography images is presented. Calvarial tumors can be located in both soft and bone tissue, occupying wide range of image intensities, making automatic segmentation and computational modeling a challenging task. The objective of this study is to analyze and validate different approaches in intensity priors modeling with an attention to multiclass problems. One, two, and three class Gaussian mixture models and a discrete model are evaluated considering probability density modeling accuracy and segmentation outcome. Segmentation results were validated in comparison to manually segmented golden standards, using analysis in ROC (Receiver Operating Curve) space and Dice similarity coefficient.
authors.affiliation: Chair of Medical Engineering, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Germany. popovic@hia.rwth-aachen.de
authors.forename: Aleksandra
authors.initials: A
authors.lastname: Popovic
authors.forename: Ting
authors.initials: T
authors.lastname: Wu
authors.forename: Martin
authors.initials: M
authors.lastname: Engelhardt
authors.forename: Klaus
authors.initials: K
authors.lastname: Radermacher
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; 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; D012888:Skull Neoplasms / Q000000981:diagnostic imaging*; D013382:Subtraction Technique