Carotid artery stenosis is generally a constriction caused by atherosclerosis or carotid artery lumen bottleneck. Carotid arteries are located closely to bones and osteoid structures. Osteoid structures and carotid arteries are frequently confused with each other when performing vessel evaluations. This study provides a novel method for carotid artery lumen segmentation on CTA images by using automatic vessel segmentation with inverse approach, in which vessel segmentation is performed after bone region is segmented and eliminated. The region growing and random walk segmentation methods are utilized in the elimination of bone region and the vessel segmentation. The seed points in the mentioned methods are not manually determined by any starting point. In automatic segmentation, seeds are selected from the experimentally determined intervals according to the local histogram. The stages of preprocessing and post-processing are utilized for better segmentation. The tracking of vessel centers based on continuity is employed for 3D reconstruction and 3D imaging of the vessels. Experiments were conducted with different data sets including various CTA images by using the mentioned methods. As a result, dice similarity rate above 92% was achieved together with 0.16 mm Msd and 99% accuracy. It was concluded based on these results that the proposed method provides successful results in different points of common, internal, external, vertebral arteries, carotid bifurcation and locales close to osteoid structures which are deemed challenging regions for carotid artery lumen segmentation. (C) 2017 Elsevier Ltd. All rights reserved.