The quantitative assessment of metabolic bone diseases relies on tissue properties such as bone mineral density (BMD) and bone microarchitecture. In spite of an increasing number of publications using high-resolution peripheral quantitative computed-tomography (HR-pQCT), the accurate and reproducible separation of cortical and trabecular bone remains challenging. In this paper, we present a novel, fully automated, threshold-independent technique for the segmentation of cortical and trabecular bone in HR-pQCT scans. This novel post-processing method is based on modeling appearance characteristics from manually annotated cases. In our experiments the algorithm automatically selected texture features with high differentiating power and trained a classifier to separate cortical and trabecular bone. From this mask, cortical thickness and tissue volume could be calculated with high accuracy. The overlap between the proposed threshold-independent segmentation tool (TIST) and manual contouring was 0.904±0.045 (Dice coefficient). In our experiments, TIST obtained higher overall accuracy in our measurements than other techniques.
Copyright © 2012. Published by Elsevier Inc.