Segmentation of Sub-cortical Structures by the Graph-Shifts Algorithm

Abstract. 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 i...

• Type: Conference
• Year: 2007
• Where: IPMI
• Authors: Jason J. Corso, Zhuowen Tu, Alan L. Yuille, Arthur W. Toga
• Keywords: Algorithm, Algorithm Called Graph-shifts, Dynamic Hierarchical Representation, IPMI 2007, Medical Imaging