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.

[1]  A Odgaard,et al.  Three-dimensional methods for quantification of cancellous bone architecture. , 1997, Bone.

[2]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[3]  Erner,et al.  MILES FORMULAE FOR BOOLEAN MODELS OBSERVED ON LATTICES , 2009 .

[4]  Rafael Molina,et al.  On the Hierarchical Bayesian Approach to Image Restoration: Applications to Astronomical Images , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Junfeng Yang,et al.  A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration , 2009, SIAM J. Imaging Sci..

[6]  Bruno Sixou,et al.  Total variation super-resolution for 3D trabecular bone micro-structure segmentation , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[7]  Sharmila Majumdar,et al.  Multicenter precision of cortical and trabecular bone quality measures assessed by high‐resolution peripheral quantitative computed tomography , 2013, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[8]  Ivo F. Sbalzarini,et al.  Coupling Image Restoration and Segmentation: A Generalized Linear Model/Bregman Perspective , 2013, International Journal of Computer Vision.

[9]  Michael K. Ng,et al.  Blind Deconvolution Using Generalized Cross-Validation Approach to Regularization Parameter Estimation , 2011, IEEE Transactions on Image Processing.

[10]  Xavier Bresson,et al.  Fast Global Minimization of the Active Contour/Snake Model , 2007, Journal of Mathematical Imaging and Vision.

[11]  Mostafa Kaveh,et al.  A regularization approach to joint blur identification and image restoration , 1996, IEEE Trans. Image Process..

[12]  Nobuyuki Otsu,et al.  ATlreshold Selection Method fromGray-Level Histograms , 1979 .

[13]  Xavier Bresson,et al.  Completely Convex Formulation of the Chan-Vese Image Segmentation Model , 2012, International Journal of Computer Vision.

[14]  Aggelos K. Katsaggelos,et al.  Variational Bayesian Blind Deconvolution Using a Total Variation Prior , 2009, IEEE Transactions on Image Processing.

[15]  Ieee Staff.  2017 25th European Signal Processing Conference (EUSIPCO) , 2017 .

[16]  Tony F. Chan,et al.  Total variation blind deconvolution , 1998, IEEE Trans. Image Process..

[17]  E. Vettorazzi,et al.  Accuracy of trabecular structure by HR-pQCT compared to gold standard μCT in the radius and tibia of patients with osteoporosis and long-term bisphosphonate therapy , 2014, Osteoporosis International.

[18]  P Cloetens,et al.  A synchrotron radiation microtomography system for the analysis of trabecular bone samples. , 1999, Medical physics.

[19]  Aggelos K. Katsaggelos,et al.  Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation , 2006, IEEE Transactions on Image Processing.

[20]  Michael K. Ng,et al.  Solving Constrained Total-variation Image Restoration and Reconstruction Problems via Alternating Direction Methods , 2010, SIAM J. Sci. Comput..

[21]  Nikolas P. Galatsanos,et al.  Hierarchical Bayesian image restoration from partially-known blurs , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[22]  José M. Bioucas-Dias,et al.  Fast Image Recovery Using Variable Splitting and Constrained Optimization , 2009, IEEE Transactions on Image Processing.

[23]  M. Bouxsein,et al.  In vivo assessment of trabecular bone microarchitecture by high-resolution peripheral quantitative computed tomography. , 2005, The Journal of clinical endocrinology and metabolism.

[24]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .