Segmentation of sub-cortical structures by the graph-shifts algorithm.
We propose a novel algorithm called graph-shifts for performing image segmentation and labeling. This algorithm makes use of a dynamic hierarchical representation of the image. This representation allows each iteration of the algorithm to make both small and large changes in the segmentation, similar to PDE and split-and-merge methods, respectively. In particular, at each iteration we are able to rapidly compute and select the optimal change to be performed. We apply graph-shifts to the task of segmenting sub-cortical brain structures. First we formalize this task as energy function minimization where the energy terms are learned from a training set of labeled images. Then we apply the graphshifts algorithm. We show that the labeling results are comparable in quantitative accuracy to other approaches but are obtained considerably faster: by orders of magnitude (roughly one minute). We also quantitatively demonstrate robustness to initialization and avoidance of local minima in which conventional boundary PDE methods fall.
Corso
Jason J
JJ
Center for Computational Biology, Laboratory of Neuro Imaging, University of California, Los Angeles, USA. jcorso@ucla.edu
Tu
Zhuowen
Z
Yuille
Alan
A
Toga
Arthur
A
D000465:Algorithms; D001185:Artificial Intelligence; D002540:Cerebral Cortex / Q000033:anatomy & histology*; 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*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity