Automatic detection of lung vessel bifurcation in thoracic CT images

Computer-aided diagnosis (CAD) systems for detection of lung nodules have been an active topic of research for last few years. It is desirable that a CAD system should generate very low false positives (FPs) while maintaining high sensitivity. This work aims to reduce the number of false positives occurring at vessel bifurcation point. FPs occur quite frequently on vessel branching point due to its shape which can appear locally spherical due to the intrinsic geometry of intersecting tubular vessel structures combined with partial volume effects and soft tissue attenuation appearance surrounded by parenchyma. We propose a model-based technique for detection of vessel branching points using skeletonization, followed by branch-point analysis. First we perform vessel structure enhancement using a multi-scale Hessian filter to accurately segment tubular structures of various sizes followed by thresholding to get binary vessel structure segmentation [6]. A modified Reebgraph [7] is applied next to extract the critical points of structure and these are joined by a nearest neighbor criterion to obtain complete skeletal model of vessel structure. Finally, the skeletal model is traversed to identify branch points, and extract metrics including individual branch length, number of branches and angle between various branches. Results on 80 sub-volumes consisting of 60 actual vessel-branching and 20 solitary solid nodules show that the algorithm identified correctly vessel branching points for 57 sub-volumes (95% sensitivity) and misclassified 2 nodules as vessel branch. Thus, this technique has potential in explicit identification of vessel branching points for general vessel analysis, and could be useful in false positive reduction in a lung CAD system.