A robust and efficient framework for tubular structure segmentation in chest CT images

Abstract

BACKGROUND: Tubular structure segmentation in chest CT images can reduce false positives (FPs) dramatically and improve the performance of nodules malignancy levels classification. OBJECTIVE: In this study, we present a framework that can segment the pulmonary tubular structure regions robustly and efficiently. METHODS: Firstly, we formulate a global tubular structure identification model based on Frangi filter. The model can recognize irregular vascular structures including bifurcation, small vessel, and junction, robustly and sensitively in 2D images. In addition, to segment the vessels from JVN, we design a local tubular structure identification model with a sliding window. Finally, we propose a multi-view voxel discriminating scheme on the basis of the previous two models. This scheme reduces the computational complexity of obtaining high entropy spatial tubular structure information. RESULTS: Experimental results have shown that the proposed framework achieves TPR of 85.79%, FPR of 24.83%, and ACC of 84.47% with the average elapsed time of 162.9 seconds. CONCLUSIONS: The framework provides an automated approach for effectively segmenting tubular structure from the chest CT images.