Region-based image segmentation with local signed difference energy

Authors:
Lingfeng Wang;Huaiyu Wu;Chunhong Pan
Affiliations:
NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Venue:
Pattern Recognition Letters
Year:
2013

Citing 18
Cited 1

Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects

IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation

International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model

International Journal of Computer Vision
Geodesic active contours

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Level Set Evolution without Re-Initialization: A New Variational Formulation

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation

International Journal of Computer Vision
Dynamical Statistical Shape Priors for Level Set-Based Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)

Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
Nonlinear Dynamical Shape Priors for Level Set Segmentation

Journal of Scientific Computing
Active contours with selective local or global segmentation: A new formulation and level set method

Image and Vision Computing
Γ-convergence approximation to piecewise smooth medical image segmentation

MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction

Journal of Scientific Computing
Level set segmentation based on local gaussian distribution fitting

ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Snakes, shapes, and gradient vector flow

IEEE Transactions on Image Processing
Active contours without edges

IEEE Transactions on Image Processing
Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification

IEEE Transactions on Image Processing

Robust level set image segmentation via a local correntropy-based K-means clustering

Pattern Recognition

Quantified Score

Hi-index	0.10

Visualization

Abstract

Intensity inhomogeneity often causes considerable difficulties in image segmentation. To tackle this problem, we propose a new region-based level set method. The proposed method considers the local image information by describing it as a novel local signed difference (LSD) energy, which possesses both local separability and global consistency. The LSD energy term is integrated into an objective energy functional, which is minimized via a level set evolution process. Extensive experiments are performed to evaluate the proposed method, showing improvements in both accuracy and efficiency, as compared with the state-of-the-art approaches.