Trabecular texture analysis in dental cbct by multi-roi multi-feature fusion

Peiyi Li, Xiong Yang, Fangfang Xie, Jie Yang, Erkang Cheng, Vasileios Megalooikonomou, Yong Xu, Haibin Ling

Abstract:

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 (MFMR)
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.