Curvelet-based geodesic snakes for image segmentation with multiple objects

Curvelet transform is a multiscale and multidirectional geometric wavelet transform, which is an optimal sparse representation of edges and contours of objects. In this paper, a curvelet-based geodesic snake (CGS) is proposed for image segmentation of multiple objects. By producing the edge map of objects by curvelet thresholding instead of simple gradient methods, the proposed method shows great promises to recognize edges of multiple objects with weak edges and strong noises. In addition, we design several rules to quantitatively compare the segmentation accuracy.

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