Dual-T-Snakes model for medical imaging segmentation

The Dual-T-Snakes model plus dynamic programming (DP) techniques is a powerful methodology for boundary extraction and segmentation of 2D images. However, the original Dual-T-Snakes is not efficient for noisy images due to nonconvexity problems. In this paper we improve the model through multigrid and region growing methods to get more robustness against local minima. Besides, we demonstrate the advantage of using pass-band filtering methods and a fuzzy segmentation technique plus Dual-T-Snakes. We test these methods for artificial and cell images.

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