Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-based strategies for active contour models
International Journal of Computer Vision
International Journal of Computer Vision
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
Flux Maximizing Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Real-Time Tracking Using Level Sets
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
Object segmentation using graph cuts based active contours
Computer Vision and Image Understanding
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A binary level set model and some applications to Mumford-Shah image segmentation
IEEE Transactions on Image Processing
A 2-phase 2-D thresholding algorithm
Digital Signal Processing
Aurora image segmentation by combining patch and texture thresholding
Computer Vision and Image Understanding
Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images
Pattern Recognition Letters
Color texture image segmentation based on neutrosophic set and wavelet transformation
Computer Vision and Image Understanding
Image segmentation by iterated region merging with localized graph cuts
Pattern Recognition
Minimising retinal vessel artefacts in optical coherence tomography images
Computer Methods and Programs in Biomedicine
A dynamic threshold approach for skin tone detection in colour images
International Journal of Biometrics
Small object detection in cluttered image using a correlation based active contour model
Pattern Recognition Letters
A local region-based Chan-Vese model for image segmentation
Pattern Recognition
Level set evolution with locally linear classification for image segmentation
Pattern Recognition
Region-based image segmentation with local signed difference energy
Pattern Recognition Letters
Active contour model driven by local histogram fitting energy
Pattern Recognition Letters
ORACM: Online region-based active contour model
Expert Systems with Applications: An International Journal
Pattern Recognition and Image Analysis
Adaptive diffusion flow active contours for image segmentation
Computer Vision and Image Understanding
A new level set method for inhomogeneous image segmentation
Image and Vision Computing
Computers in Biology and Medicine
Information Sciences: an International Journal
A nonlinear level set model for image deblurring and denoising
The Visual Computer: International Journal of Computer Graphics
Medical image segmentation schemes for the analysis of gynaecological malignancies
International Journal of Knowledge-based and Intelligent Engineering Systems
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A novel region-based active contour model (ACM) is proposed in this paper. It is implemented with a special processing named Selective Binary and Gaussian Filtering RegularizedLevel Set(SBGFRLS) method, which first selectively penalizes the level set function to be binary, and then uses a Gaussian smoothing kernel to regularize it. The advantages of our method are as follows. First, a new region-based signed pressure force (SPF) function is proposed, which can efficiently stop the contours at weak or blurred edges. Second, the exterior and interior boundaries can be automatically detected with the initial contour being anywhere in the image. Third, the proposed ACM with SBGFRLS has the property of selective local or global segmentation. It can segment not only the desired object but also the other objects. Fourth, the level set function can be easily initialized with a binary function, which is more efficient to construct than the widely used signed distance function (SDF). The computational cost for traditional re-initialization can also be reduced. Finally, the proposed algorithm can be efficiently implemented by the simple finite difference scheme. Experiments on synthetic and real images demonstrate the advantages of the proposed method over geodesic active contours (GAC) and Chan-Vese (C-V) active contours in terms of both efficiency and accuracy.