Active Contour Model Based on Multi-Population Particle Swarm Optimization

2006 ◽  
Author(s):  
Chun-Chieh Tseng ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Particle Swarm Optimization ◽  
Active Contour ◽  
Active Contour Model ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Model Based ◽  
Contour Model

scholarly journals Multiple Active Contours Driven by Particle Swarm Optimization for Cardiac Medical Image Segmentation

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
I. Cruz-Aceves ◽  
J. G. Aviña-Cervantes ◽  
J. M. López-Hernández ◽  
S. E. González-Reyna
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contours ◽  
Active Contour Model ◽  
Particle Swarm ◽  
Magnetic Resonance Images ◽  
Swarm Optimization ◽  
Contour Model

This paper presents a novel image segmentation method based on multiple active contours driven by particle swarm optimization (MACPSO). The proposed method uses particle swarm optimization over a polar coordinate system to increase the energy-minimizing capability with respect to the traditional active contour model. In the first stage, to evaluate the robustness of the proposed method, a set of synthetic images containing objects with several concavities and Gaussian noise is presented. Subsequently, MACPSO is used to segment the human heart and the human left ventricle from datasets of sequential computed tomography and magnetic resonance images, respectively. Finally, to assess the performance of the medical image segmentations with respect to regions outlined by experts and by the graph cut method objectively and quantifiably, a set of distance and similarity metrics has been adopted. The experimental results demonstrate that MACPSO outperforms the traditional active contour model in terms of segmentation accuracy and stability.

Grey Model Based Particle Swarm Optimization Algorithm for Anticorrosion Reliability Design of Underground Pipelines

2010 ◽  
Vol 118-120 ◽  
pp. 541-545
Author(s):  
Qin Ming Liu ◽  
Ming Dong
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Failure Modes ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Grey Model ◽  
Swarm Optimization ◽  
Reliability Design ◽  
Model Based ◽  
Underground Pipelines

This paper explores the grey model based PSO (particle swarm optimization) algorithm for anti-cauterization reliability design of underground pipelines. First, depending on underground pipelines’ corrosion status, failure modes such as leakage and breakage are studied. Then, a grey GM(1,1) model based PSO algorithm is employed to the reliability design of the pipelines. One important advantage of the proposed algorithm is that only fewer data is used for reliability design. Finally, applications are used to illustrate the effectiveness and efficiency of the proposed approach.

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

2017 ◽  
Vol 24 (6) ◽  
pp. 653-659
Author(s):  
Qiang Zheng ◽  
Honglun Li ◽  
Baode Fan ◽  
Shuanhu Wu ◽  
Jindong Xu
Keyword(s):  
Gaussian Distribution ◽  
Active Contour ◽  
Active Contour Model ◽  
Brain Images ◽  
Model Based ◽  
Contour Model ◽  
Mr Brain Images ◽  
Mr Brain
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