A new adaptive framework for tubular structures segmentation in X-ray angiography

Abstract

Segmentation of tubular structures in X-ray angiographies, such as the aorta or coronary arteries, is a critical task for the guidance of the heart surgeon during percutaneous cardiac interventions. Extracting these structures is challenging due to the quality of X-ray angiographies (presence of noise, non-homogeneous regions) and to the characteristics of the structure of interest (tubular and fine structures ). To overcome the shortcomings of the conventional local and global methods, we propose in this paper, a coarse to fine segmentation framework to extract tubular structures from x-ray angiographies. The framework first enhances tubular structures by a vesselness filter. Then, the structures are segmented by the proposed adaptive active contour method which combines a local and global fitting energy. Those two forces are weighted according to the image's homogeneity value. Experiments have been conducted on different angiograms acquired on children. The results have shown that the proposed approach gives promising results in the segmentation of tubular structures and outperforms other active contour methods. Thanks to the combination of local and global forces, the proposed system is robust to noise and intensity inhomogeneity.