Segmentation of Coronary CT Angiography Images Based on Deformable Model with New Edge Measures

Abstract

To deal with locally narrow, low-contrast and spatially varying intensity in segmentation of the computed tomography angiographic (CTA) data, a deformable model with newly proposed edge measures was presented for segmenting coronary arteries. The edge measures were derived from the refined vesselness measures of brightness and multi-scale filtering responses, i.e., vesselness and scale. The initial vessel region and boundary region was derived from the multi-scale filtering responses, from which the statistical information of vessel appearance was attained to yield brightness measure. As compared with the multi-scale filtering responses, the refined vesselness measures could effectively suppress non-vascular background while preserving vessel-like structure. Finally, the new edge measures were embedded into deformable model, resulting in better artery segmentation.