A level set x-ray image segmentation method with local region information

2009 ◽  
Author(s):  
Wei Zheng ◽  
Yanjiang Chen
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Local Region ◽  
Segmentation Method ◽  
X Ray ◽  
Region Information

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.

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.

scholarly journals Shape Prior Embedded Level Set Model for Image Segmentation

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Wansuo Liu ◽  
Dengwei Wang ◽  
Wenjun Shi
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Geometric Transformation ◽  
Probability Weighting ◽  
Shape Prior ◽  
Factors Affecting ◽  
Sensitivity Problem ◽  
Level Set Evolution ◽  
Region Information ◽  
Level Set Model

This paper presents an optimized level set evolution (LSE) without reinitialization (LSEWR) model and a shape prior embedded level set model (LSM) for robust image segmentation. Firstly, by performing probability weighting and coefficient adaptive processing on the original LSEWR model, the optimized image energy term required by the proposed model is constructed. The purpose of the probability weighting is to introduce region information into the edge stop function to enhance the model’s ability to capture weak edges. The introduction of the adaptive coefficient enables the evolution process to automatically adjust its amplitude and direction according to the current image coordinate and local region information, thus completely solving the initialization sensitivity problem of the original LSEWR model. Secondly, a shape prior term driven by kernel density estimation (KDE) is additionally introduced into the optimized LSEWR model. The role of the KDE-driven shape prior term is to overcome the problem of image segmentation in the presence of geometric transformation and pattern interference. Even if there is obvious affine transformation in the shape prior and the target to be segmented, the target contour can still be reconstructed correctly. The extensive experiments on a large variety of synthetic and real images show that the proposed algorithm achieves excellent performance. In addition, several key factors affecting the performance of the proposed algorithm are analyzed in detail.

Enhanced X‐ray image segmentation method using prior shape

2016 ◽  
Vol 11 (2) ◽  
pp. 145-152 ◽  
Author(s):  
Hossein Mahvash Mohammadi ◽  
Jacques A. Guise
Keyword(s):  
Image Segmentation ◽  
Segmentation Method ◽  
X Ray ◽  
Prior Shape

Level Set Segmentation Method in Medical Image Segmentation Research and Application

2012 ◽  
Vol 157-158 ◽  
pp. 1012-1015 ◽  
Author(s):  
Yu Miao ◽  
Wei Li Shi
Keyword(s):  
Image Segmentation ◽  
White Matter ◽  
Level Set ◽  
Medical Image ◽  
Region Of Interest ◽  
Medical Image Segmentation ◽  
The Other ◽  
Segmentation Method ◽  
Level Set Segmentation ◽  
Mri Image

Medical image segmentation can be divided into two categories: one is the region of interest (ROI) identification; the other is the description of the integrity and the extraction of interest region. The emergence of the level set method greatly promoted the development of medical image segmentation. This paper studies three different level set segmentation algorithm to achieve the effective segmentation for brain gray matter and white matter of MRI image.

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