Intensity Inhomogeneity Image Segmentation Based on Active Contours Driven by Self-Similarity

2019 ◽  
pp. 185-198
Author(s):  
Xu Li ◽  
Hairong Liu ◽  
Xiaoping Yang
Keyword(s):  
Image Segmentation ◽  
Active Contours ◽  
Intensity Inhomogeneity ◽  
Self Similarity

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.

MEDICAL IMAGES SEGMENTATION USING ACTIVE CONTOURS DRIVEN BY GLOBAL AND LOCAL IMAGE FITTING ENERGY

2012 ◽  
Vol 12 (02) ◽  
pp. 1250015 ◽  
Author(s):  
HAIJUN WANG ◽  
MING LIU
Keyword(s):  
Image Segmentation ◽  
Active Contours ◽  
Energy Functional ◽  
Intensity Inhomogeneity ◽  
Image Information ◽  
Regularization Term ◽  
Global Image Information ◽  
Global And Local ◽  
Local Image ◽  
Local Term

In this paper, we propose a global and local Chan-Vese model which utilizes both global image information and local image information for image segmentation. We define an energy functional with a global term, which incorporates global image information to improve the robustness of the proposed method, and a local term which is dominant near the object boundaries. The regularization term is added to the energy functional to avoid the time-consuming re-initialization. The comparisons with the C–V model, LBF model and LGIF model show that our model can segment images with intensity inhomogeneity in less iteration steps and take less time.

An Improved Image Segmentation Active Contour Model

2014 ◽  
Vol 513-517 ◽  
pp. 3463-3467
Author(s):  
Li Fen Zhou ◽  
Chang Xu Cai
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Local Region ◽  
Intensity Inhomogeneity ◽  
Contour Model ◽  
Global And Local ◽  
Region Information

The Chan-Vese (C-V) active contour model has low computational complexity, initialization and insensitive to noise advantagesand utilizes global region information of images, so it is difficult to handle images with intensity inhomogeneity. The Local binary fitting (LBF) model based on local region information has its certain advantages in mages segmentation of weak boundary or uneven greay.but , the segmentation results are very sensitive to the initial contours, In order to address this problem, this paper proposes a new active contour model with a partial differential equation, which integrates both global and local region information. Experimental results show that it has a distinctive advantage over C-V model for images with intensity inhomogeneity, and it is more efficient than LBF.

scholarly journals Local and Global Active Contour Model for Image Segmentation With Intensity Inhomogeneity

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 54224-54240 ◽  
Author(s):  
Qing Cai ◽  
Huiying Liu ◽  
Yiming Qian ◽  
Jing Li ◽  
Xiaojun Duan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Intensity Inhomogeneity ◽  
Contour Model

A New Image Segmentation Method Based on Improved Fast Implicit Level Set Scheme in X/γ-Ray Inspection System

2011 ◽  
Vol 103 ◽  
pp. 705-710 ◽  
Author(s):  
Yu Jie Li ◽  
Hui Min Lu ◽  
Li Feng Zhang ◽  
Shi Yuan Yang ◽  
Serikawa Seiichi
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Active Contours ◽  
Operator Splitting ◽  
Inspection System ◽  
Segmentation Method ◽  
Piecewise Constant ◽  
Γ Ray ◽  
Luggage Inspection ◽  
Segmentation Of Images

Digital X/γ-ray imaging technology has been widely used to help people deliver effective and reliable security in airports, train stations, and public buildings. Nowadays, luggage inspection system with digital radiographic/computed tomography (DR/CT) represents a most advanced nondestructive inspection technology in aviation system, which is capable of automatically discerning interesting regions in the luggage objects with CT subsystem. In this paper, we propose a new model for active contours to detect luggage objects in the system, in order to facilitate people to identify the things in luggage. The proposed method is based on techniques of piecewise constant and piecewise smooths Chan-Vese Model, semi-implicit additive operator splitting (AOS) scheme for image segmentation. Different from traditional models, the fast implicit level set scheme (FILS) is ordinary differential equation (ODE). Characterized by no need of any pre-information of topology of images and efficient segmentation of images with complex topology, the FILS scheme is fast more than traditional level set scheme 30 times. At the same time, it performs well in image segmentation of DR images in our experiments.

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