Accurate Foreground Extraction Using Graph Cut with Trimap Estimation

2006 ◽  
pp. 1185-1194 ◽  
Author(s):  
Jung-Ho Ahn ◽  
Hyeran Byun
Keyword(s):  
Graph Cut ◽  
Foreground Extraction

scholarly journals Foreground Extraction Combining Graph Cut and Histogram Shape Analysis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 176248-176256
Author(s):  
Kun He ◽  
Dan Wang ◽  
Bin Wang ◽  
Ben Feng ◽  
Chenyu Li
Keyword(s):  
Shape Analysis ◽  
Graph Cut ◽  
Foreground Extraction ◽  
Histogram Shape

scholarly journals Multi-scale foreground extraction on graph cut

2019 ◽  
Vol 277 ◽  
pp. 02031
Author(s):  
Jiayi Liu ◽  
Kun He
Keyword(s):  
Shape Analysis ◽  
Graph Cut ◽  
Negative Effects ◽  
Foreground Extraction ◽  
Multi Scale ◽  
Implementation Effect ◽  
Histogram Shape

In order to improve Grab Cut implementation effect for real images, we propose a novel improvement which extends the Grab Cut in three aspects: 1) a series of edge-preserved components are generated via the TV smoothing model; 2) the number of sub-regions is estimated by histogram shape analysis to remove the negative effects on the unreasonable number of the sub-regions; 3) a segmentation termination condition is constructed by integrating the multi-scale components. The experiment result indicates that this method performs well compared to other methods based on graph cut and is insensitive to sub-regions.

Automatic vertebra segmentation based on graph cut and mean shift algorithm

2011 ◽  
Vol 31 (3) ◽  
pp. 760-762
Author(s):  
Ji LIU ◽  
Xiao-dong KANG ◽  
Fu-cang JIA
Keyword(s):  
Mean Shift ◽  
Graph Cut ◽  
Mean Shift Algorithm

scholarly journals Semi-automatic liver segmentation based on probabilistic models and anatomical constraints

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Doan Cong Le ◽  
Krisana Chinnasarn ◽  
Jirapa Chansangrat ◽  
Nattawut Keeratibharat ◽  
Paramate Horkaew
Keyword(s):  
Probabilistic Models ◽  
Contextual Information ◽  
Automatic Segmentation ◽  
Anatomical Variations ◽  
Graph Cut ◽  
Computing Methods ◽  
Liver Segmentation ◽  
Seed Point ◽  
Tissue Separation ◽  
Human Interactions

AbstractSegmenting a liver and its peripherals from abdominal computed tomography is a crucial step toward computer aided diagnosis and therapeutic intervention. Despite the recent advances in computing methods, faithfully segmenting the liver has remained a challenging task, due to indefinite boundary, intensity inhomogeneity, and anatomical variations across subjects. In this paper, a semi-automatic segmentation method based on multivariable normal distribution of liver tissues and graph-cut sub-division is presented. Although it is not fully automated, the method minimally involves human interactions. Specifically, it consists of three main stages. Firstly, a subject specific probabilistic model was built from an interior patch, surrounding a seed point specified by the user. Secondly, an iterative assignment of pixel labels was applied to gradually update the probabilistic map of the tissues based on spatio-contextual information. Finally, the graph-cut model was optimized to extract the 3D liver from the image. During post-processing, overly segmented nodal regions due to fuzzy tissue separation were removed, maintaining its correct anatomy by using robust bottleneck detection with adjacent contour constraint. The proposed system was implemented and validated on the MICCAI SLIVER07 dataset. The experimental results were benchmarked against the state-of-the-art methods, based on major clinically relevant metrics. Both visual and numerical assessments reported herein indicated that the proposed system could improve the accuracy and reliability of asymptomatic liver segmentation.

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