An active contour model and its algorithms with local and global Gaussian distribution fitting energies

2014 ◽  
Vol 263 ◽  
pp. 43-59 ◽  
Author(s):  
Hui Wang ◽  
Ting-Zhu Huang ◽  
Zongben Xu ◽  
Yilun Wang
Keyword(s):  
Gaussian Distribution ◽  
Active Contour ◽  
Active Contour Model ◽  
Distribution Fitting ◽  
Contour Model

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.

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

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.

Extraction of Cerebral Hemorrhage on CT Images Using Level Set Algorithm and Otsu Threshold

2021 ◽  
Vol 11 (7) ◽  
pp. 2033-2039
Author(s):  
Xiaoliang Jiang ◽  
Qile Zhang
Keyword(s):  
Level Set ◽  
Cerebral Hemorrhage ◽  
Active Contour ◽  
Active Contour Model ◽  
Ct Images ◽  
Initial Curve ◽  
Distribution Fitting ◽  
Edge Information ◽  
Manual Delineation ◽  
Contour Model

Extraction of cerebral hemorrhage on CT images has always been the focus of several research hotspots and is still challenging as it does not show clear boundary. In this paper, a novel segmentation framework is presented for extracting the cerebral hemorrhage in brain CT images with weak boundary. Firstly, we utilize the Otsu threshold algorithm to get the coarse outline approximate to the target boundary as the initial curve of level set algorithm. Then, the active contour model is employed using both edge information and global Gaussian distribution fitting energy of images to modify energy function of level set. The proposed approach is applied on real images which from Quzhou People’s Hospital. Compared to manual delineation, the proposed technique shows a higher JS value than the existing methods and requires less interaction which is listed in the literature.

Active Contour Driven by Local Gaussian Distribution Fitting and Local Signed Difference Based on Local Entropy

2016 ◽  
Vol 30 (03) ◽  
pp. 1655011 ◽  
Author(s):  
Xiao Liang Jiang ◽  
Bai Lin Li ◽  
Jian Ying Yuan ◽  
Xiao Liang Wu
Keyword(s):  
Level Set ◽  
Gaussian Distribution ◽  
Active Contour ◽  
Active Contour Model ◽  
Local Entropy ◽  
Distribution Fitting ◽  
Proposed Model ◽  
Global Consistency ◽  
Weak Boundaries ◽  
Level Set Formulation

Intensity inhomogeneity often causes considerable difficulties in image segmentation. In order to tackle this problem, we propose a novel region-based active contour model in a variational level set formulation. We first define a data fitting energy with a local Gaussian distribution fitting (LGDF) term, which induces a local force to attract the contour and stops it at object boundaries, and a local signed difference (LSD) term based on local entropy, which possesses both local separability and global consistency. This energy is then incorporated into a level set formulation with a level set regularization term that is necessary for accurate computation in the corresponding level set method. Experimental results show that the proposed model can not only segment images with intensity inhomogeneities and weak boundaries but also be robust to the noise, initial contours.

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