Automated vessel tree segmentation: Challenges in computer aided quantification of diffuse parenchyma lung diseases

Identification and characterization of diffuse parenchyma lung disease patterns challenges computer aided diagnosis (CAD) schemes in computed tomography (CT). Accuracy of these preprocessing stages is expected to influence the accuracy of lung CAD schemes. Although algorithms aimed at improving the accuracy of segmentation of lung fields in presence of DPLDs have been reported, the corresponding vessel tree segmentation stage is under-researched. In this paper, an automated vessel tree segmentation scheme is proposed, utilizing a 3D multi-scale vessel segmentation filter based on eignen value analysis of the Hessian matrix and unsupervised segmentation, followed by texture classification refinement to correct possible over-segmentation. Performance of the proposed scheme in vessel tree segmentation was evaluated by means of volume overlap (no refinement: 0.794, refinement: 0.925), true positive fraction (no refinements: 0.938, refinement: 0.902) and false positive fraction (no refinement: 0.241, refinement: 0.077) to pixel exact ground truth of 3 MDCT scans.