Minimal weighted local variance as edge detector for active contour models
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Elastic interaction models for active contours and surfaces
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Efficient geometrical potential force computation for deformable model segmentation
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
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Image segmentation is defined as partitioning an image into non-overlapping regions based on the intensity or texture. The active contour methods provide an effective way for segmentation, in which the boundary of an object (usually with large image gradient value) is detected by an evolving curve. But, these methods have limitations due to the fact that real images may have objects with complex geometric structures and shapes, and are often corrupted by noise. Developing more robust and accurate active contour methods has been an active research area since the idea of the methods was proposed. In this paper, we propose a new active contour method and apply the method to medical image segmentation. This new method uses a long-ranged interaction between image boundaries and the moving curves, which is inspired by the elastic interaction between line defects in solids (dislocations). The new method is moreefficient and effective, especially in detecting thin, weak and blurred structures such as the images of blood vessels.