Segmentation of high-resolution CT enables the assessment of bone microstructure and provides relevant information in determining bone quality and fracture risk. Segmentation is typically performed with a global threshold value, expressed in bone mineral density (BMD). While consistent, a global threshold can lead to a poor representation of bone microstructure; amongst others, caused by altered bone physiology, image noise, image inhomogeneities, and partial volume effects. Adaptive threshold segmentations can preserve fine features, yet, it does not retain any quantitative information on BMD. We propose a new method for bone microstructure segmentation based on a Gaussian mixture model (GMM). This technique models the normalized image histogram as a bi-modal Gaussian distribution, reflecting bone and non-bone voxels, and gives an analytical description of the intensity threshold where probability of belonging to either class is equal. Next, the technique calculates a range of intensities around this threshold, where the tissue class is uncertain; only intensities within this range are adaptively segmented; others with a single threshold. Verification n was performed using simulated images with a priori known distributions. The GMM subsequentially reconstructed the input models. Next, HR-pQCT and photon-counting CT (PCCT) images of cadaveric wrists were segmented, and segmentations were scored against reference segmentations from micro-CT. GMM segmentation accuracy was compared to adaptive and global thresholding. The optimal threshold from simulated images could accurately be determined, provided the bi-modal components did not accumulate into a single Gaussian. For HR-pQCT, full adaptive segmentation achieved the highest accuracy (86.2±3.7%), though GMM yielded comparable results (83.0±2.3%). For PCCT, GMM achieved higher accuracy (77.9±2.3%) than adaptive segmentation (76.4±2.4%). We conclude that this method can segment bone microstructure with accuracy comparable to conventional techniques. Through the definition of uncertain intensities, the GMM method provides the opportunity to tune the segmentation towards higher sensitivity or specificity.
Quintiens et al. (Tue,) studied this question.