Multiscale vessel segmentation: a level set approach

Authors:
Gang Yu;Yalin Miao;Peng Li;Zhengzhong Bian
Affiliations:
School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China;School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China;School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China;School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
Venue:
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2005

Citing 4
Cited 0

Shape Modeling with Front Propagation: A Level Set Approach

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Geodesic Active Contours

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Muliscale Vessel Enhancement Filtering

MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Influence of the Noise Model on Level Set Active Contour Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

This paper presents a novel efficient multiscale vessel segmentation method using the level-set framework. This technique is based on the active contour model that evolves according to the geometric measure of vessel structures. Inspired by the multiscale vessel enhancement filtering, the prior knowledge about the vessel shape is incorporated into the energy function as a region information term. In this method, a new region-based external force is combined with existing geometric snake variation models. A new speed function is designed to precisely control the curve deformation. This multiscale method is more efficient for the segmentation of vessel and line-like structures than the conventional active contour methods. Furthermore, the whole model is implemented in a level-set framework. The solution is stable and robust for various topologic changes. This method was compared with other geometric active contour models. Experimental results of human lung CT images show that this multiscale method is accurate.