The segmentation of the CT image based on k clustering and graph-cut

Computed tomography angiography (CTA) is widely used to assess heart disease, like coronary artery disease. In order to complete the auto-segmentation of cardiac image of dual-source CT (DSCT) and extract the structure of heart accurately, this paper proposes a hybrid segmentation method based on k clustering and Graph-Cuts (GC). It identifies the initial label of pixels by this method. Based on this, it creates the energy function of the label with the knowledge of anatomic construction of heart and constructs the network diagram. Finally, it minimizes the energy function by the method of max-flow/min-cut theorem and picks up region of interest. The experiment results indicate that the robust, accurate segmentation of the cardiac DSCT image can be realized by combining Graph-Cut and k clustering algorithm.