Identification of incorrect segmentation and centerline correction of coronary arteries in CT angiographic images

For computer-aided diagnosis of cardiovascular diseases, accurately extracted centerlines of coronary arteries in computed tomography (CT) angiographic images are important, because they are the basis of curved multi-planar reformation (cMPR) creation and stenosis analysis. Within all the centerline extraction methods, the methods performed after vessel segmentation are easier to implement, of less running time and more robust, and for these reasons, they are still used in many practical applications. However, because of segmentation errors and vascular abnormalities, segmented vessels are not always correct. If centerline extraction seriously depends on vessel segmentation, incorrectly segmented vessels may probably result in errors in their centerlines (like incorrect local curvatures of centerlines). To obtain correct centerlines, we need to identify the incorrectly segmented vessels first, and then correct the centerlines of these vessels. In this paper, we propose two automatic methods to identify the incorrectly segmented vessels and correct their centerlines respectively. For the identification of incorrect vessel segmentation, we propose a method based on the vessel diameter fitting. And for centerline correction, we propose a method based on the precise smoothness to draw deviated centerlines back to the correct locations. We have validated the proposed methods on real CT angiographic datasets of coronary arteries. The quantitative evaluation results show that the proposed methods can effectively detect and correct centerline errors arising from the erroneous vessel segmentation in most cases. And we can see from the experimental results that the number of false positive and false negative center points reduces a lot when compared with the original results without centerline correction.