Multiscale Medialness for Robust Segmentation of 3D Tubular Structures
نویسندگان
چکیده
Segmentation of tubular structures like blood vessels and airways in 3D volume data is of vital interest for medical applications like diagnosis and surgical planning. The proposed method uses a vessel detection filter, which is based on a novel multiscale medialness function and also provides a radius estimate. Based on the filter ouput, centerlines of the tubes are extracted and the vessel tree is reconstructed. The final segmentation step uses the tube representation to initialize and constrain a level set method for tubular structures. Computer generated and real data sets are used for performance evaluation of the method. Results show the robustness of the method against noise and anisotropic voxels as well as its applicability to blood vessels and airway trees.
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