Multilevel segmentation and integrated bayesian model classification with an application to brain tumor segmentation.
We present a new method for automatic segmentation of heterogeneous image data, which is very common in medical image analysis. The main contribution of the paper is a mathematical formulation for incorporating soft model assignments into the calculation of affinities, which are traditionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm. We apply the technique to the task of detecting and segmenting brain tumor and edema in multimodal MR volumes. Our results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of brain tumor.
Corso
Jason J
JJ
Medical Imaging Informatics, University of California, Los Angeles, CA, USA. jcorso@mii.ucla.edu
Sharon
Eitan
E
Yuille
Alan
A
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001932:Brain Neoplasms / Q000473:pathology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity