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.

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 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 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.

An Image Segmentation Method of Underwater Targets Based on Active Contour Model

2014 ◽  
Vol 511-512 ◽  
pp. 457-461
Author(s):  
Tao Liu ◽  
Lei Wan ◽  
Xing Wei Liang
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Segmentation Method ◽  
Contour Model ◽  
Underwater Targets ◽  
Underwater Target ◽  
Images Processing

The underwater images are disturbed with low signal to noise ratio and edge blur, because there are the light scattering and absorption effects. If the traditional thresholding method is used directly to segment underwater images, it will usually lead to be less effective to process underwater images. An image segmentation method of underwater target based on active contour model was proposed in this paper. Firstly, using Canny edge detection algorithm to detect the edges of the original image to obtain the information of a crude outline, then the algorithm based on C-V active contour model to segment underwater target images was addressed. The images processing results based on threshold segmentation method and C-V model method were compared. Experiments demonstrate the effectiveness of the proposed algorithm for underwater targets images segmentation.

An Intelligent Image Segmentation Algorithm for Medical Ultrasonic Instruments

2009 ◽  
Vol 419-420 ◽  
pp. 701-704
Author(s):  
Guo Chang Gu ◽  
Chang Ming Zhu ◽  
Hai Bo Liu ◽  
Sheng Jing ◽  
Hua Long Yu
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Relevance Vector Machine ◽  
Initial Contour ◽  
Ultrasound Images ◽  
Qualitative And Quantitative Analysis ◽  
Segmentation Method ◽  
Ultrasonic Images ◽  
Image Segmentation Algorithm

In company with medical instruments ' development, the corresponding software plays more and more role in the application. And medical image processing software has become an important component of the medical ultrasonic instruments. Image segmentation plays an important role in both qualitative and quantitative analysis of medical ultrasound images. But state-of-arts methods in the aspect of segmentation can not get satisfactory results. We propose an intelligent image segmentation method for medical ultrasonic images. The algorithm improved active contour model with relevance vector machine, where the advantages of supervised learning classification and the global region distribution information can be exploited to enhance the performance. In order to improve the segmentation speed and get precise initial contour, relevance vector machine also is used to obtain initial contour firstly. A large amount of experimental results have proved that our method outperforms many state-of-arts methods in the aspect of segmentation, and the method can be used in ultrasonic instruments effectively.

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.

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 Hybrid Methodology for Image Segmentation Based on Active Contour Module and Alpha-Shape Theory

2016 ◽  
Vol 10 (11) ◽  
pp. 30
Author(s):  
Mohammed Sabbih Hamoud Al-Tamimi
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Image Data ◽  
Magnetic Resonance Images ◽  
Computational Method ◽  
X Rays ◽  
Alpha Shape ◽  
Contour Model ◽  
Objective Contour

The concept of the active contour model has been extensively utilized in the segmentation and analysis of images. This technology has been effectively employed in identifying the contours in object recognition, computer graphics and vision, biomedical processing of images that is normal images or medical images such as Magnetic Resonance Images (MRI), X-rays, plus Ultrasound imaging. Three colleagues, Kass, Witkin and Terzopoulos developed this energy, lessening “Active Contour Models” (equally identified as Snake) back in 1987. Being curved in nature, snakes are characterized in an image field and are capable of being set in motion by external and internal forces within image data and the curve itself in that order. The present study proposes the use of a hybrid image segmentation technique to acquire precise segmentation outcomes, while engaging “Alpha Shape (α-Shape)” in supposition to derive the original contour, followed by a refining process through engaging a conventional active contour model. Empirical results show high potential in the suggested computational method. Trials indicate that the primary contour is capable of being precisely set next to the objective contour and effectively have these objective contours extracted, devoid of any contour instigation. Some of the benefits associated with the novel hybrid contour include minimized cost of computation, enhanced anti-jamming capability, as well as enlarged utilization array of snake model.

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