Stabilization of parametric active contours using a tangential redistribution term
IEEE Transactions on Image Processing
A local probabilistic prior-based active contour model for brain MR image segmentation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Active contouring based on gradient vector interaction and constrained level set diffusion
IEEE Transactions on Image Processing
Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm
Multimedia Tools and Applications
Expert Systems with Applications: An International Journal
Active contour method combining local fitting energy and global fitting energy dynamically
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Mean shift based gradient vector flow for image segmentation
Computer Vision and Image Understanding
Computer Vision and Image Understanding
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Active contours or snakes have been extensively utilized in handling image segmentation and classification problems. In traditional active contour models, snake initialization is performed manually by users, and topological changes, such as splitting of the snake, can not be automatically handled. In this paper, we introduce a new method to solve the snake initialization and splitting problem, based on an area segmentation approach: the external force field is segmented first, and then the snake initialization and splitting can be automatically performed by using the segmented external force field. Such initialization and splitting produces multiple snakes, each of which is within the capture range associated to an object and will be evolved to the object boundary. The external force used in this paper is a gradient vector flow with an edge-preserving property (EPGVF), which can prevent the snakes from passing over weak boundaries. To segment the external force field, we represent it with a graph, and a graph-theory approach can be taken to determine the membership of each pixel. Experimental results establish the effectiveness of the proposed approach.