Trabecular texture analysis in dental CBCT by multi-ROI multi-feature fusion

Variations in trabecular bone texture are known to be correlated with bone diseases, such as osteoporosis. In this paper we propose a multi-feature multi-ROI (MMFMRFMR) approach for analyzing trabecular patterns inside the oral cavity using cone beam computed tomography (CBCT) volumes. For each dental CBCT volume, a set of features including fractal dimension, multi-fractal spectrum and gradient based features are extracted from eight regions-of-interest (ROI) to address the low image quality of trabecular patterns. Then, we use generalized multi-kernel learning (GMKL) to effectively fuse these features for distinguishing trabecular patterns from different groups. To validate the proposed method, we apply it to distinguish trabecular patterns from different gender-age groups. On a dataset containing dental CBCT volumes from 96 subjects, divided into gender-age subgroups, our approach achieves 96.1% average classification rate, which greatly outperforms approaches without the feature fusion.