[Graph-based interactive three-dimensional segmentation of magnetic resonance images of brain tumors].
We propose a graph-based three-dimensional (3D) algorithm to automatically segment brain tumors from magnetic resonance images (MRI). The algorithm uses minimum s/t cut criteria to obtain a global optimal result of objective function formed according to Markov Random Field Model and Maximum a posteriori (MAP-MRF) theory, and by combining the expectation-maximization (EM) algorithm to estimate the parameters of mixed Gaussian model for normal brain and tumor tissues. 3D segmentation results of brain tumors are fast achieved by our algorithm. The validation of the algorithm was tested and showed good accuracy and adaptation under simple interactions with the physicians.
Li
Wei
W
Key Lab for Medical Image Processing, Southern Medical University, Guangzhou 510515, China.
Chen
Wu-fan
WF
D000465:Algorithms; D001932:Brain Neoplasms / Q000175:diagnosis*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging; D010363:Pattern Recognition, Automated / Q000379:methods