"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIFT Flow: Dense Correspondence across Different Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
PatchMatch: a randomized correspondence algorithm for structural image editing
ACM SIGGRAPH 2009 papers
Video SnapCut: robust video object cutout using localized classifiers
ACM SIGGRAPH 2009 papers
The generalized patchmatch correspondence algorithm
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Active learning for interactive 3d image segmentation
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Computer Vision and Image Understanding
Human brain labeling using image similarities
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Partial similarity based nonparametric scene parsing in certain environment
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
PATCHMATCHGRAPH: building a graph of dense patch correspondences for label transfer
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Active learning for interactive segmentation with expected confidence change
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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In this paper, we present a novel three dimensional interactive medical image segmentation method based on high level knowledge of training set. Since the interactive system should provide intermediate results to an user quickly, insufficient low level models are used for most of previous methods. To exploit the high level knowledge within a short time, we construct a structured patch model that consists of multiple corresponding patch sets. The structured patch model includes the spatial relationships between neighboring patch sets and the prior knowledge of the corresponding patch set on each local region. The spatial relationships accelerate the search of corresponding patch in test time, while the prior knowledge improves the segmentation accuracy. The proposed framework provides not only fast editing tool, but the incremental learning system through adding the segmentation result to the training set. Experiments demonstrate that the proposed method is useful for fast and accurate segmentation of target objects from the multiple medical images.