An improved Chan-Vese (CV) model for lumen segmentation of carotid artery

Lumen segmentation of the carotid artery is an important preprocessing step with clinical application, because it facilitates subsequent analysis, including stenosis grading, the detection and quantification of plaque components in the vessel wall. But it is a challenging task owing to the low and varying contrast between the surrounding tissues. Plus, intensity inhomogeneity often occurs in medical images from different modalities, which causes undesirable side effects when the traditional Chan-Vese (CV) model is applied. In order to overcome the intensity inhomogeneity problem we improve the CV model by adding a local fitting term, which incorporates both local information and global information. We also introduce a penalizing energy to avoid the time-consuming re-initialization procedure in traditional level set method.We propose and validate a semi-automatic method for lumen segmentation of carotid artery in computed tomography angiography(CTA). First we manually select a seedpoint on the lumen area, then the segmentation is automatically obtained using a level set. By combining both global and local statistical information, we manage to overcome the inhomogeneous intensity distribution in the CTA images and provide more satisfying segmentation result than CV model. Experiments on carotid artery CTA data have demonstrated the efficiency and accuracy of our model, in addition our model is also less sensitive to the location of the initial contour. Therefore our method has the potential to replace the manual procedure of lumen segmentation in CTA, which is of great value for doctors with clinical applications.