title: Cellular automata segmentation of brain tumors on post contrast MR images.
abstract: In this paper, we re-examine the cellular automata (CA) algorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmentation method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Validation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type.
authors.affiliation: Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey.
authors.forename: Andac
authors.initials: A
authors.lastname: Hamamci
authors.forename: Gozde
authors.initials: G
authors.lastname: Unal
authors.forename: Nadir
authors.initials: N
authors.lastname: Kucuk
authors.forename: Kayihan
authors.initials: K
authors.lastname: Engin
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000473:pathology*; D003287:Contrast Media; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media