Shape feature extraction from an image usually suffers from effects like imaging noise and segmentation errors. A robust shape description algorithm is therefore a prerequisite. In this paper

relations between performances on elastic quadratic wire (EQW) based shape representation model and the model parameters are revealed first. By taking advantage of these relations

we focus on building a new EQW model that adaptively preserves geometrical features. Then the proposed EQW model is further embedded into the Live Wire algorithm. In experiments

the improved Live Wire algorithm is tested on medical and remote sensing image segmentation. Qualitatively

the results show that the proposed model has better performances on extracting object boundaries while avoiding meaningless morphology caused by noisy or incomplete data. Quantitatively

the segmentation errors and the temporal cost of the improved Live Wire are both acceptable.