Cellular automata segmentation of brain tumors on post contrast MR images.
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.
Hamamci
Andac
A
Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey.
Unal
Gozde
G
Kucuk
Nadir
N
Engin
Kayihan
K
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
D003287:Contrast Media