scholarly journals Implicit Active Contours Driven by Local and Global Image Fitting Energy for Image Segmentation and Target Localization

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaosheng Yu ◽  
Yuanchen Qi ◽  
Ziwei Lu ◽  
Nan Hu
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contours ◽  
Active Contour Model ◽  
Target Localization ◽  
Numerical Accuracy ◽  
Variational Level Set ◽  
Contour Evolution ◽  
Level Set Formulation ◽  
Local Image

We propose a novel active contour model in a variational level set formulation for image segmentation and target localization. We combine a local image fitting term and a global image fitting term to drive the contour evolution. Our model can efficiently segment the images with intensity inhomogeneity with the contour starting anywhere in the image. In its numerical implementation, an efficient numerical schema is used to ensure sufficient numerical accuracy. We validated its effectiveness in numerous synthetic images and real images, and the promising experimental results show its advantages in terms of accuracy, efficiency, and robustness.

scholarly journals A hybrid level set model for image segmentation

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0251914
Author(s):  
Weiqin Chen ◽  
Changjiang Liu ◽  
Anup Basu ◽  
Bin Pan
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Initial Contour ◽  
Edge Information ◽  
Contour Evolution ◽  
Contour Models ◽  
Global And Local ◽  
Local Fitting ◽  
Local Image

Active contour models driven by local binary fitting energy can segment images with inhomogeneous intensity, while being prone to falling into a local minima. However, the segmentation result largely depends on the location of the initial contour. We propose an active contour model with global and local image information. The local information of the model is obtained by bilateral filters, which can also enhance the edge information while smoothing the image. The local fitting centers are calculated before the contour evolution, which can alleviate the iterative process and achieve fast image segmentation. The global information of the model is obtained by simplifying the C-V model, which can assist contour evolution, thereby increasing accuracy. Experimental results show that our algorithm is insensitive to the initial contour position, and has higher precision and speed.

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.

scholarly journals Convex Image Segmentation Model Based on Local and Global Intensity Fitting Energy and Split Bregman Method

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Yunyun Yang ◽  
Boying Wu
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Energy Functional ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Proposed Model ◽  
Variational Level Set ◽  
Convex Image Segmentation ◽  
Level Set Formulation ◽  
Global Fitting

We propose a convex image segmentation model in a variational level set formulation. Both the local information and the global information are taken into consideration to get better segmentation results. We first propose a globally convex energy functional to combine the local and global intensity fitting terms. The proposed energy functional is then modified by adding an edge detector to force the active contour to the boundary more easily. We then apply the split Bregman method to minimize the proposed energy functional efficiently. By using a weight function that varies with location of the image, the proposed model can balance the weights between the local and global fitting terms dynamically. We have applied the proposed model to synthetic and real images with desirable results. Comparison with other models also demonstrates the accuracy and superiority of the proposed model.

scholarly journals Localized Patch-Based Fuzzy Active Contours for Image Segmentation

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Jiangxiong Fang ◽  
Hesheng Liu ◽  
Huaxiang Liu ◽  
Liting Zhang ◽  
Jun Liu
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contours ◽  
Active Contour Model ◽  
Direct Method ◽  
Lagrange Equation ◽  
Energy Functional ◽  
Local Patch ◽  
A Direct Method ◽  
Fuzzy Region

This paper presents a novel fuzzy region-based active contour model for image segmentation. By incorporating local patch-energy functional along each pixel of the evolving curve into the fuzziness of the energy, we construct a patch-based energy function without the regurgitation term. Its purpose is not only to make the active contour evolve very stably without the periodical initialization during the evolution but also to reduce the effect of noise. In particular, in order to reject local minimal of the energy functional, we utilize a direct method to calculate the energy alterations instead of solving the Euler-Lagrange equation of the underlying problem. Compared with other fuzzy active contour models, experimental results on synthetic and real images show the advantages of the proposed method in terms of computational efficiency and accuracy.

A Novel Active Contour Model for Image Segmentation and Bias Correction Using Guided Image Filtering Regularization Term

2017 ◽  
Vol 31 (07) ◽  
pp. 1754013 ◽  
Author(s):  
Haijun Wang ◽  
Ming Liu
Keyword(s):  
Image Segmentation ◽  
Bias Correction ◽  
Active Contour ◽  
Spatial Information ◽  
Active Contour Model ◽  
Image Filtering ◽  
Distribution Fitting ◽  
Contour Model ◽  
Guided Image Filtering ◽  
Local Image

This paper presents a novel active contour model for image segmentation and bias correction in terms of robustness to initialization and intensity inhomogeneity. In our model, the local image intensities are described by Gaussian distributions with different means and variances. The local Gaussian distribution fitting energy with a new guided image filtering (GIF) regularization is proposed. The new guided image regularization not only considers the spatial information, but also utilizes the local image content. So compared with the traditional algorithms, the proposed model is less sensitive to initialization and converges faster. Comparative experiments show the advantage of the proposed method.

scholarly journals An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF

2018 ◽  
Vol 8 (12) ◽  
pp. 2576 ◽  
Author(s):  
Lin Sun ◽  
Xinchao Meng ◽  
Jiucheng Xu ◽  
Yun Tian
Keyword(s):  
Image Segmentation ◽  
Weight Function ◽  
Active Contour ◽  
Active Contour Model ◽  
Mean Value ◽  
Segmentation Method ◽  
Image Information ◽  
Contour Model ◽  
Global Image Information ◽  
Local Image

Inhomogeneous images cannot be segmented quickly or accurately using local or global image information. To solve this problem, an image segmentation method using a novel active contour model that is based on an improved signed pressure force (SPF) function and a local image fitting (LIF) model is proposed in this paper, which is based on local and global image information. First, a weight function of the global grayscale means of the inside and outside of a contour curve is presented by combining the internal gray mean value with the external gray mean value, based on which a new SPF function is defined. The SPF function can segment blurred images and weak gradient images. Then, the LIF model is introduced by using local image information to segment intensity-inhomogeneous images. Subsequently, a weight function is established based on the local and global image information, and then the weight function is used to adjust the weights between the local information term and the global information term. Thus, a novel active contour model is presented, and an improved SPF- and LIF-based image segmentation (SPFLIF-IS) algorithm is developed based on that model. Experimental results show that the proposed method not only exhibits high robustness to the initial contour and noise but also effectively segments multiobjective images and images with intensity inhomogeneity and can analyze real images well.

scholarly journals Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
I. Cruz-Aceves ◽  
J. G. Avina-Cervantes ◽  
J. M. Lopez-Hernandez ◽  
H. Rostro-Gonzalez ◽  
C. H. Garcia-Capulin ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
Active Contour ◽  
Medical Image ◽  
Active Contours ◽  
Active Contour Model ◽  
Magnetic Resonance Images ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
Contour Model

This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation.

scholarly journals New Region-Scalable Discriminant and Fitting Energy Functional for Driving Geometric Active Contours in Medical Image Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Xuchu Wang ◽  
Yanmin Niu ◽  
Liwen Tan ◽  
Shao-Xiang Zhang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Active Contour ◽  
Medical Image ◽  
Active Contours ◽  
Active Contour Model ◽  
Energy Functional ◽  
Medical Image Segmentation ◽  
Intensity Inhomogeneity ◽  
Boundary Problems

We propose a novel region-based geometric active contour model that uses region-scalable discriminant and fitting energy functional for handling the intensity inhomogeneity and weak boundary problems in medical image segmentation. The region-scalable discriminant and fitting energy functional is defined to capture the image intensity characteristics in local and global regions for driving the evolution of active contour. The discriminant term in the model aims at separating background and foreground in scalable regions while the fitting term tends to fit the intensity in these regions. This model is then transformed into a variational level set formulation with a level set regularization term for accurate computation. The new model utilizes intensity information in the local and global regions as much as possible; so it not only handles better intensity inhomogeneity, but also allows more robustness to noise and more flexible initialization in comparison to the original global region and regional-scalable based models. Experimental results for synthetic and real medical image segmentation show the advantages of the proposed method in terms of accuracy and robustness.

scholarly journals Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
I. Cruz-Aceves ◽  
J. G. Avina-Cervantes ◽  
J. M. Lopez-Hernandez ◽  
M. G. Garcia-Hernandez ◽  
M. Torres-Cisneros ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Marginal Distribution ◽  
Active Contours ◽  
Active Contour Model ◽  
Magnetic Resonance Images ◽  
Distribution Model ◽  
Shape Priors ◽  
Segmentation Method ◽  
Reference Shape

This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.

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