Comparison of Hybrid ACO-k-Means Algorithm and Graph Cut for MRI Images Segmentation

2020 ◽  
pp. 73-80
Author(s):  
Samer El-Khatib ◽  
Yuri Skobtsov ◽  
Sergey Rodzin ◽  
Semyon Potryasaev
Keyword(s):  
Graph Cut

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.

scholarly journals Mesh Denoising via Adaptive Consistent Neighborhood

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 412
Author(s):  
Mingqiang Guo ◽  
Zhenzhen Song ◽  
Chengde Han ◽  
Saishang Zhong ◽  
Ruina Lv ◽  
...  
Keyword(s):  
Graph Cut ◽  
Underlying Surface ◽  
Geometric Features ◽  
Two Stage ◽  
Mesh Denoising ◽  
Normal Field ◽  
Stage Scheme ◽  
Complex Shapes ◽  
Patch Shift

In this paper, we propose a novel guided normal filtering followed by vertex updating for mesh denoising. We introduce a two-stage scheme to construct adaptive consistent neighborhoods for guided normal filtering. In the first stage, we newly design a consistency measurement to select a coarse consistent neighborhood for each face in a patch-shift manner. In this step, the selected consistent neighborhoods may still contain some features. Then, a graph-cut based scheme is iteratively performed for constructing different adaptive neighborhoods to match the corresponding local shapes of the mesh. The constructed local neighborhoods in this step, known as the adaptive consistent neighborhoods, can avoid containing any geometric features. By using the constructed adaptive consistent neighborhoods, we compute a more accurate guide normal field to match the underlying surface, which will improve the results of the guide normal filtering. With the help of the adaptive consistent neighborhoods, our guided normal filtering can preserve geometric features well, and is robust against complex shapes of surfaces. Intensive experiments on various meshes show the superiority of our method visually and quantitatively.

Optimal disassembly sequence generation and disposal of parts using stability graph cut-set method for End of Life product

Sadhana ◽  
2021 ◽  
Vol 46 (1) ◽  
Author(s):  
Bala Murali Gunji ◽  
Sai Krishna Pabba ◽  
Inder Raj Singh Rajaram ◽  
Paul Satwik Sorakayala ◽  
Arnav Dubey ◽  
...  
Keyword(s):  
End Of Life ◽  
Graph Cut ◽  
Sequence Generation ◽  
Disassembly Sequence ◽  
Cut Set ◽  
Stability Graph

Graph-Cut-Based Segmentation of Proximal Femur from Computed Tomography Images with Shape Prior

2015 ◽  
Vol 35 (5) ◽  
pp. 594-607 ◽  
Author(s):  
Junbin Huang ◽  
James F. Griffith ◽  
Defeng Wang ◽  
Lin Shi
Keyword(s):  
Computed Tomography ◽  
Proximal Femur ◽  
Graph Cut ◽  
Shape Prior ◽  
Computed Tomography Images
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