Automatic segmentation of the lumen in magnetic resonance images of the carotid artery

Abstract

The segmentation of the lumen and vessel wall in Magnetic Resonance (MR) images of carotid arteries represents a crucial step towards the evaluation of cerebrovascular diseases. However, the automatic segmentation of the lumen is still a challenge due to the usual low quality of the images and the presence of elements that compromise the accuracy of the results. In this article, we describe a fully automatic method to identify the location of the lumen in MR images of the carotid artery. A circularity index is used to assess the roundness of the regions identified by the K-means algorithm in order to obtain the one with the maximum value, i.e. the potential lumen region. Then, an active contour algorithm is employed to refine the boundary of the region found. The method achieved a maximum Dice coefficient of 0.91 ± 0.04 and 0.74 ± 0.16 in 181 postcontrast 3D-T1-weighted and 181 proton density-weighted MR images, respectively. Therefore, the method seems to be promising for identifying the correct location of the lumen in MR images.