Abstract
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.
Original language | English |
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Pages (from-to) | 83-90 |
Number of pages | 8 |
Journal | Pattern Recognition Letters |
Volume | 31 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Jan 2010 |
Keywords
- Active contours
- Automatic initialization
- Gradient vector flow
- Segmentation
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence