scholarly journals Unsupervised Texture Segmentation Using Active Contour Model and Oscillating Information

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Guodong Wang ◽  
Zhenkuan Pan ◽  
Qian Dong ◽  
Ximei Zhao ◽  
Zhimei Zhang ◽  
...  
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Image Decomposition ◽  
Smooth Approximation ◽  
Split Bregman ◽  
Piecewise Constant ◽  
Decomposition Model ◽  
Contour Model ◽  
Proposed Model ◽  
Data Term

Textures often occur in real-world images and may cause considerable difficulties in image segmentation. In order to segment texture images, we propose a new segmentation model that combines image decomposition model and active contour model. The former model is capable of decomposing structural and oscillating components separately from texture image, and the latter model can be used to provide smooth segmentation contour. In detail, we just replace the data term of piecewise constant/smooth approximation in CCV (convex Chan-Vese) model with that of image decomposition model-VO (Vese-Osher). Therefore, our proposed model can estimate both structural and oscillating components of texture images as well as segment textures simultaneously. In addition, we design fast Split-Bregman algorithm for our proposed model. Finally, the performance of our method is demonstrated by segmenting some synthetic and real texture images.

scholarly journals A Partition-Based Active Contour Model Incorporating Local Information for Image Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Jiao Shi ◽  
Jiaji Wu ◽  
Anand Paul ◽  
Licheng Jiao ◽  
Maoguo Gong
Keyword(s):  
Image Segmentation ◽  
Real World ◽  
Active Contour ◽  
Active Contour Model ◽  
Intensity Inhomogeneity ◽  
Curve Evolution ◽  
Evolution Theory ◽  
Piecewise Constant ◽  
Contour Model ◽  
Proposed Model

Active contour models are always designed on the assumption that images are approximated by regions with piecewise-constant intensities. This assumption, however, cannot be satisfied when describing intensity inhomogeneous images which frequently occur in real world images and induced considerable difficulties in image segmentation. A milder assumption that the image is statistically homogeneous within different local regions may better suit real world images. By taking local image information into consideration, an enhanced active contour model is proposed to overcome difficulties caused by intensity inhomogeneity. In addition, according to curve evolution theory, only the region near contour boundaries is supposed to be evolved in each iteration. We try to detect the regions near contour boundaries adaptively for satisfying the requirement of curve evolution theory. In the proposed method, pixels within a selected region near contour boundaries have the opportunity to be updated in each iteration, which enables the contour to be evolved gradually. Experimental results on synthetic and real world images demonstrate the advantages of the proposed model when dealing with intensity inhomogeneity images.

scholarly journals Active Contour Model for Ultrasound Images with Rayleigh Distribution

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Guodong Wang ◽  
Qian Dong ◽  
Zhenkuan Pan ◽  
Ximei Zhao ◽  
Jinbao Yang ◽  
...  
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Speckle Noise ◽  
Rayleigh Distribution ◽  
Ultrasound Images ◽  
Fast Method ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Multiplicative Noises ◽  
Contour Model

Ultrasound images are often corrupted by multiplicative noises with Rayleigh distribution. The noises are strong and often called speckle noise, so segmentation is a hard work with this kind of noises. In this paper, we incorporate multiplicative noise removing model into active contour model for ultrasound images segmentation. To model gray level behavior of ultrasound images, the classic Rayleigh probability distribution is considered. Our model can segment the noisy ultrasound images very well. Finally, a fast method called Split-Bregman method is used for the easy implementation of segmentation. Experiments on a variety of synthetic and real ultrasound images validate the performance of our method.

scholarly journals Active Contour Model Coupling with Higher Order Diffusion for Medical Image Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Guodong Wang ◽  
Jie Xu ◽  
Qian Dong ◽  
Zhenkuan Pan
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Higher Order ◽  
Medical Image Segmentation ◽  
Diffusion Method ◽  
Split Bregman ◽  
Contour Model ◽  
The Difference

Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active contour model can converge to the edge of the image even with the intensity inhomogeneities. Because of the introduction of Laplace information, the difference scheme becomes more difficult. To enhance the efficiency of the segmentation, the fast Split Bregman algorithm is designed for the segmentation implementation. The performance of our method is demonstrated through numerical experiments of some medical image segmentations with intensity inhomogeneities.

A Local Statistical Information Active Contour Model for Image Segmentation

2014 ◽  
Vol 6 (2) ◽  
pp. 33-49 ◽  
Author(s):  
Shigang Liu ◽  
Yali Peng ◽  
Guoyong Qiu ◽  
Xuanwen Hao
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Statistical Information ◽  
Intensity Inhomogeneity ◽  
Image Domain ◽  
Contour Model ◽  
Proposed Model ◽  
Spatially Varying ◽  
Level Set Formulation

This paper presents a local statistical information (LSI) active contour model. Assuming that the distribution of intensity belonging to each region is a Gaussian distribution with spatially varying statistical information, and defining an energy function, the authors integrate the entire image domain. Then, this energy is incorporated into a variational level set formulation. Finally, by minimizing the energy functional, a curve evolution equation can be obtained. Because the image local information is considered, the proposed model can effectively deal with the image with intensity inhomogeneity. Experimental results on synthetic and real images demonstrate that the proposed model can effectively segment the image with intensity inhomogeneity.

EFFICIENT ACTIVE CONTOUR MODEL FOR MULTIPHASE SEGMENTATION WITH APPLICATION TO BRAIN MR IMAGES

2013 ◽  
Vol 27 (01) ◽  
pp. 1355001 ◽  
Author(s):  
YUNYUN YANG ◽  
YI ZHAO ◽  
BOYING WU
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Energy Model ◽  
Mr Images ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Contour Model ◽  
New Energy ◽  
Brain Mr Images

In this paper, we propose an efficient active contour model for multiphase image segmentation in a variational level set formulation. By incorporating the globally convex segmentation idea and the split Bregman method into the multiphase formulation of the local and global intensity fitting energy model, our new model improved the original local and global intensity fitting energy model in the following aspects. First, we propose a new energy functional using the globally convex segmentation method to guarantee fast convergence. Second, we incorporate information from the edge into the energy functional by using a non-negative edge detector function to detect boundaries more easily. Third, instead of a constant value to control the influence of the local and global intensity fitting terms, we use a weight function varying with the locations of the image to balance the weights between the local and the global fitting terms dynamically. Lastly, the special structure of our energy functional enables us to apply the split Bregman method to minimize the energy much more efficiently. We have applied our model to synthetic images and real brain MR images with promising results. Experimental results demonstrate the efficiency and superiority of our model.

scholarly journals Implicit Active Contour Model with Local and Global Intensity Fitting Energies

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaozeng Xu ◽  
Chuanjiang He
Keyword(s):  
Steady State ◽  
Initial Function ◽  
Active Contour ◽  
Active Contour Model ◽  
Level Line ◽  
Zero Level ◽  
Contour Model ◽  
Proposed Model ◽  
Local Intensity ◽  
Global Image Information

We propose a new active contour model which integrates a local intensity fitting (LIF) energy with an auxiliary global intensity fitting (GIF) energy. The LIF energy is responsible for attracting the contour toward object boundaries and is dominant near object boundaries, while the GIF energy incorporates global image information to improve the robustness to initialization of the contours. The proposed model not only can provide desirable segmentation results in the presence of intensity inhomogeneity but also allows for more flexible initialization of the contour compared to the RSF and LIF models, and we give a theoretical proof to compute a unique steady state regardless of the initialization; that is, the convergence of the zero-level line is irrespective of the initial function. This means that we can obtain the same zero-level line in the steady state, if we choose the initial function as a bounded function. In particular, our proposed model has the capability of detecting multiple objects or objects with interior holes or blurred edges.

scholarly journals Fractional Differentiation-Based Active Contour Model Driven by Local Intensity Fitting Energy

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Ming Gu ◽  
Renfang Wang
Keyword(s):  
Fractional Order ◽  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Input Image ◽  
Intensity Inhomogeneity ◽  
Initial Contour ◽  
Level Set Function ◽  
Contour Model ◽  
Proposed Model

A novel active contour model is proposed for segmentation images with inhomogeneity. Firstly, fractional order filter is defined by eight convolution masks corresponding to the image orientation in the eight compass directions. Then, the fractional order differentiation image is obtained and applied to the level set method. Secondly, we defined a new energy functional based on local image information and fractional order differentiation image; the proposed model not only can describe the input image more accurately but also can deal with intensity inhomogeneity. Local fitting term can enhance the ability of the model to deal with intensity inhomogeneity. The defined penalty term is used to reduce the occurrence of false boundaries. Finally, in order to eliminate the time-consuming step of reinitialization and ensure stable evolution of level set function, the Gaussian filtering method is used. Experiments on synthetic and real images show that the proposed model is efficient for images with intensity inhomogeneity and flexible to initial contour.

scholarly journals A Vessel Active Contour Model for Vascular Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Yun Tian ◽  
Qingli Chen ◽  
Wei Wang ◽  
Yu Peng ◽  
Qingjun Wang ◽  
...  
Keyword(s):  
Vector Field ◽  
Active Contour ◽  
Active Contour Model ◽  
Contour Model ◽  
Proposed Model ◽  
Weighted Means ◽  
Speed Up ◽  
Local Intensity ◽  
Local Binary Fitting Model ◽  
Vessel Tree

This paper proposes a vessel active contour model based on local intensity weighting and a vessel vector field. Firstly, the energy function we define is evaluated along the evolving curve instead of all image points, and the function value at each point on the curve is based on the interior and exterior weighted means in a local neighborhood of the point, which is good for dealing with the intensity inhomogeneity. Secondly, a vascular vector field derived from a vesselness measure is employed to guide the contour to evolve along the vessel central skeleton into thin and weak vessels. Thirdly, an automatic initialization method that makes the model converge rapidly is developed, and it avoids repeated trails in conventional local region active contour models. Finally, a speed-up strategy is implemented by labeling the steadily evolved points, and it avoids the repeated computation of these points in the subsequent iterations. Experiments using synthetic and real vessel images validate the proposed model. Comparisons with the localized active contour model, local binary fitting model, and vascular active contour model show that the proposed model is more accurate, efficient, and suitable for extraction of the vessel tree from different medical images.

scholarly journals Local- and Global-Statistics-Based Active Contour Model for Image Segmentation

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Boying Wu ◽  
Yunyun Yang
Keyword(s):  
Image Segmentation ◽  
Gaussian Distribution ◽  
Active Contour ◽  
Active Contour Model ◽  
Energy Functional ◽  
Split Bregman ◽  
Distribution Fitting ◽  
Total Variation Norm ◽  
Contour Model ◽  
Global Statistics

This paper presents a local- and global-statistics-based active contour model for image segmentation by applying the globally convex segmentation method. We first propose a convex energy functional with a local-Gaussian-distribution-fitting term with spatially varying means and variances and an auxiliary global-intensity-fitting term. A weight function that varies dynamically with the location of the image is applied to adjust the weight of the global-intensity-fitting term dynamically. The weighted total variation norm is incorporated into the energy functional to detect boundaries easily. The split Bregman method is then applied to minimize the proposed energy functional more efficiently. Our model has been applied to synthetic and real images with promising results. With the local-Gaussian-distribution-fitting term, our model can also handle some texture images. Comparisons with other models show the advantages of our model.

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