scholarly journals A Novel Adaptive Level Set Segmentation Method

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Yazhong Lin ◽  
Qian Zheng ◽  
Jiaqiang Chen ◽  
Qian Cai ◽  
Qianjin Feng
Keyword(s):  
Level Set ◽  
Performance Indicators ◽  
Spatial Information ◽  
Initial Contour ◽  
Segmentation Method ◽  
Improved Method ◽  
Local Binary Fitting Model ◽  
Segmentation Of Images ◽  
Accuracy Speed ◽  
Fitting Model

The adaptive distance preserving level set (ADPLS) method is fast and not dependent on the initial contour for the segmentation of images with intensity inhomogeneity, but it often leads to segmentation with compromised accuracy. And the local binary fitting model (LBF) method can achieve segmentation with higher accuracy but with low speed and sensitivity to initial contour placements. In this paper, a novel and adaptive fusing level set method has been presented to combine the desirable properties of these two methods, respectively. In the proposed method, the weights of the ADPLS and LBF are automatically adjusted according to the spatial information of the image. Experimental results show that the comprehensive performance indicators, such as accuracy, speed, and stability, can be significantly improved by using this improved method.

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.

An abdominal aortic aneurysm segmentation method: Level set with region and statistical information

2006 ◽  
Vol 33 (5) ◽  
pp. 1440-1453 ◽  
Author(s):  
Feng Zhuge ◽  
Geoffrey D. Rubin ◽  
Shaohua Sun ◽  
Sandy Napel
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Aortic Aneurysm ◽  
Level Set ◽  
Statistical Information ◽  
Segmentation Method ◽  
Abdominal Aortic

Fast Level Set Segmentation Method in Medical Multi-sensor Images Detection

2012 ◽  
Vol 4 (23) ◽  
pp. 475-482 ◽  
Author(s):  
Huimin Lu ◽  
Yujie Li ◽  
Lifeng Zhang ◽  
Shiyuan Yang ◽  
Seiichi Serikawa
Keyword(s):  
Level Set ◽  
Segmentation Method ◽  
Level Set Segmentation
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