TY - GEN
T1 - Segmentation of sub-cortical structures by the graph-shifts algorithm
AU - Corso, Jason J.
AU - Tu, Zhuowen
AU - Yuille, Alan
AU - Toga, Arthur
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-540-73273-0_16
DO - 10.1007/978-3-540-73273-0_16
M3 - Conference contribution
C2 - 17633699
AN - SCOPUS:34548403887
SN - 3540732721
SN - 9783540732723
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 183
EP - 197
BT - Information Processing in Medical lmaging - 20th International Conference, IPMI 2007, Proceedings
T2 - 20th International Conference on Information Processing in Medical lmaging, IPMI 2007
Y2 - 2 July 2007 through 6 July 2007
ER -