A Laplace-Hamming Binarization Approach for Second-Generation HR-pQCT Rescues Fine Feature Segmentation.

Although second-generation high-resolution peripheral quantitative computed tomography (XCTII) provides the highest resolution in vivo bone microstructure assessment, the manufacturer's standard image processing protocol omits fine features in both trabecular and cortical compartments. To optimize fine structure segmentation, we implemented a binarization approach based on a Laplace-Hamming (LH) segmentation, and documented the reproducibility and accuracy of XCTII structure segmentation using both the standard Gaussian-based binarization and the proposed LH segmentation approach. To evaluate reproducibility, 20 volunteers (9 women, 11 men; 23-75 years) were recruited and three repeat scans of the radii and tibiae were acquired using the manufacturer's standard in vivo protocol. To evaluate accuracy, cadaveric structure phantoms (14 radii, 6 tibiae) were scanned on XCTII using the same standard in vivo protocol and on μCT at 24.5 μm resolution. XCTII images were analyzed twice - first, with the manufacturer's standard patient evaluation protocol and second with the proposed LH segmentation approach. The LH approach rescued fine features evident in the grayscale images but omitted or over-represented (thickened) by the standard approach. The LH approach significantly reduced error in trabecular volume fraction (BV/TV) and thickness (Tb.Th) compared to the standard approach; however, higher error was introduced for trabecular separation (Tb.Sp). The LH approach improved the correlation between XCTII and μCT for cortical porosity (Ct.Po) and significantly reduced error in cortical pore diameter (Ct.Po.Dm) compared to the standard approach. The LH approach resulted in improved precision compared to the standard approach for BV/TV, Tb.Th, Ct.Po, and Ct.Po.Dm at the radius and for Ct.Po at the tibia. Our results suggest that the proposed LH approach produces substantially improved binary masks, reduces proportional bias, provides greater accuracy and reproducibility in important outcome metrics, all due to more accurate segmentation of the fine features in both trabecular and cortical compartments. This article is protected by copyright. All rights reserved.