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Objective To extract the contour of coronary formcoronary CT angiography image accurately and rapidly,we proposed a coronary artery segmentation model based on fuzzy clustering method and C-V model.Methods First,we made a coarse processing for the original image data.Then,the obtained membership matrix and the clustering information were coupled into the improved C-V model to complete the segmentation of the coronary artery image.Finally,qualitative and quantitative analysis of this model and the other two traditional models of coronary angiography image segmentation results were given.Results Qualitative analysis of the results:the improved model finished the coronary segmentation with fewer iterations.This model had a stronger ability to segment small and complex tissue,and the target edges were smooth.Quantitative analysis of the results:using the improved model iterated 200 times which took 11.722s,overlapping rate was 83.42%;iterated 400 times which took 16.943s,overlapping rate was 85.13%.Conclusions This model can finish the coronary segmentation with fewer iterations and have the characteristics of fast segmentation,strong anti-noise ability and smooth edge.It can be used to segment coronary,and provide a reference for the three-dimensional reconstruction of the image of the coronary.