TY - GEN
T1 - Detection and segmentation of pathological structures by the extended graph-shifts algorithm
AU - Corso, Jason J.
AU - Yuille, Alan
AU - Sicotte, Nancy L.
AU - Toga, Arthur
PY - 2007
Y1 - 2007
N2 - We propose an extended graph-shifts algorithm for image segmentation and labeling. This algorithm performs energy minimization by manipulating a dynamic hierarchical representation of the image. It consists of a set of moves occurring at different levels of the hierarchy where the types of move, and the level of the hierarchy, are chosen automatically so as to maximally decrease the energy. Extended graph-shifts can be applied to a broad range of problems in medical imaging. In this paper, we apply extended graph-shifts to the detection of pathological brain structures: (i) segmentation of brain tumors, and (ii) detection of multiple sclerosis lesions. The energy terms in these tasks are learned from training data by statistical learning algorithms. We demonstrate accurate results, precision and recall in the order of 93%, and also show that the algorithm is computationally efficient, segmenting a full 3D volume in about one minute.
AB - We propose an extended graph-shifts algorithm for image segmentation and labeling. This algorithm performs energy minimization by manipulating a dynamic hierarchical representation of the image. It consists of a set of moves occurring at different levels of the hierarchy where the types of move, and the level of the hierarchy, are chosen automatically so as to maximally decrease the energy. Extended graph-shifts can be applied to a broad range of problems in medical imaging. In this paper, we apply extended graph-shifts to the detection of pathological brain structures: (i) segmentation of brain tumors, and (ii) detection of multiple sclerosis lesions. The energy terms in these tasks are learned from training data by statistical learning algorithms. We demonstrate accurate results, precision and recall in the order of 93%, and also show that the algorithm is computationally efficient, segmenting a full 3D volume in about one minute.
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U2 - 10.1007/978-3-540-75757-3_119
DO - 10.1007/978-3-540-75757-3_119
M3 - Conference contribution
C2 - 18051154
AN - SCOPUS:84883839777
SN - 9783540757566
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 985
EP - 993
BT - Medical Image Computing and Computer-Assisted Intervention - 10th International Conference, Proceedings
T2 - 10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007
Y2 - 29 October 2007 through 2 November 2007
ER -