Semi-blind joint super-resolution/segmentation of 3D trabecular bone images by a TV box approach

The investigation of bone fragility diseases, as osteoporosis, is based on the analysis of the trabecular bone microarchitecture. The aim of this paper is to improve the in-vivo trabecular bone segmentation and quantification by increasing the resolution of bone micro-architecture images. We propose a semi-blind joint super-resolution/segmentation approach based on a Total Variation regularization with a convex constraint. A comparison with the bicubic interpolation method and the non-blind version of the proposed method is shown. The validation is performed on blurred, noisy and down-sampled 3D synchrotron micro-CT bone images. Good estimates of the blur and of the high resolution image are obtained with the semi-blind approach. Preliminary results are obtained with the semi-blind approach on real HR-pQCT images.