UCLA STATS 238 - Detection and Segmentation of Pathological Structures (10 pages)

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Detection and Segmentation of Pathological Structures



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Detection and Segmentation of Pathological Structures

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Lecture Notes


Pages:
10
School:
University of California, Los Angeles
Course:
Stats 238 - Vision as Bayesian Inference

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Detection and Segmentation of Pathological Structures by the Extended Graph Shifts Algorithm Jason J Corso Center for Computational Biology Laboratory of Neuroimaging University of California at Los Angeles Los Angeles CA 90095 Alan Yuille Department of Statistics University of California at Los Angeles Los Angeles CA 90095 yuille stat ucla edu Nancy L Sicotte Department of Neurology University of California at Los Angeles Los Angeles CA 90095 Arthur Toga Center for Computational Biology Laboratory of Neuroimaging University of California at Los Angeles Los Angeles CA 90095 To appear in Proceedings of Medical Image Computing and Computer Aided Intervention MICCAI 29 Oct 2 Nov 2007 1 Detection and Segmentation of Pathological Structures by the Extended Graph Shifts Algorithm Jason J Corso1 Alan Yuille2 Nancy L Sicotte3 and Arthur Toga1 1 Center for Computational Biology Laboratory of Neuro Imaging 2 Department of Statistics 3 Department of Neurology Division of Brain Mapping University of California Los Angeles USA jcorso ucla edu Abstract 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 1 Introduction



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