On active contour models and balloons
CVGIP: Image Understanding
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-based strategies for active contour models
International Journal of Computer Vision
Shape and topology constraints on parametric active contours
Computer Vision and Image Understanding
International Journal of Computer Vision
Automatic Contour Detection by Encoding Knowledge into Active Contour Models
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Robust B-spline Snakes For Ultrasound Image Segmentation
Journal of Signal Processing Systems
Active contours initialization for ultrasound carotid artery images
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Information Sciences: an International Journal
Small object detection in cluttered image using a correlation based active contour model
Pattern Recognition Letters
Harris function based active contour external force for image segmentation
Pattern Recognition Letters
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Active contour is a well-known image segmentation technique, commonly used to find object boundaries in images. Its main benefit is its ability to retrieve an ordered collection of points. However, fitting precisely a deformable contour to actual boundaries depends strongly on its initialization and requires adjusting various parameters. This paper presents an original method to initialize quasi-automatically explicit deformable models when segmenting regions that require no change of topology. The proposed method relies on a careful study of the gradient vector flow. Two original concepts are introduced, namely strong and weak divergence centers. The analysis of the properties of these centers leads to establishing a quasi-automatic method to setup an initial curve that will reach all the boundaries of a target region. Results using synthetic and real images are presented, showing the validity of our approach.