Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach
By Kang Li, Xiaodong Wu, Danny Ziyi Chen, Milan Sonka. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
762 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/6940e5a5b1feb05cfc4712689fd87b9fcc81b276
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Lei Zhang
- Luc Van Gool
- Ming-Hsuan Yang
- Dacheng Tao
- Andrew Zisserman
- Xiaogang Wang
- Jitendra Malik
- Trevor Darrell
Citations in the corpus
- Retinal Imaging and Image Analysis (2010, neutral): “In addition to segmenting the layers in three dimensions, their approach was unique in that the layers could be segmented simultaneously [127].”
- Graph Cuts and Efficient N-D Image Segmentation (2006, methodology): “…extraction of moving or forground objects from video (Li et al., 2005; Wang et al., 2005), simultaneous segmentation of multiple objects (Li et al., 2006), combining segmentation with 3D pose estimation (Bray et al., 2006), computing segmentation uncertainty (Kohli and Torr, 2006), and…”
- A survey of graph theoretical approaches to image segmentation (2013, background): “further proposed to solve the computation problem on massive grid graph [67], to segment multiple interacting surfaces of a single n-dimensional object [112,113], to segment multiple objects and surfaces on layered graph [114] and to extract the multi-surface with some shape prior [115].”
- Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images (2009, positive): “The optimal 3-D graph search approach originally presented in [16], [19] is designed to solve what we will call the “multiple surface segmentation problem.”
- Intraretinal Layer Segmentation of Macular Optical Coherence Tomography Images Using Optimal 3-D Graph Search (2008, methodology): “Each single and multiple surface problem was transformed into that of finding a minimum-cost (nonempty) closed set in a corresponding vertex-weighted geometric graph as described in [13] and [14].”
- Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods (2011, methodology): “Graph cuts can be used to find the optimal solution of this problem [24].”