Segmentation of trabecular bones from vertebral bodies in volumetric CT spine images

Proceedings - International Conference on Image Processing, ICIP

Volumn , Issue , 2009, Pages 3385-3388

  Aslan, Melih S ^a   Ali, Asem ^a   Arnold, Ben ^b   Fahmi, Rachid ^a   Farag, Aly A ^a   Xiang, Ping ^b  

^a University of Louisville   (United States)

^b Image Analysis Inc   (United States)

Author keywords

3D Segmentation; Bone Mineral Density; Segmentation accuracy; Spine column; Trabecular bone

Indexed keywords

BONE; IMAGE PROCESSING; IMAGING SYSTEMS; MINERALS; SILICATE MINERALS; THREE DIMENSIONAL;

3D SEGMENTATION; BONE MINERAL DENSITY; CT IMAGE; DATA SETS; GRAPH CUT; GROUND TRUTH; REPRODUCIBILITIES; SEGMENTATION ACCURACY; SEGMENTATION ERROR; TRABECULAR BONES; VERTEBRAL BODY; VOLUME GROWING; VOLUMETRIC CT;

COMPUTERIZED TOMOGRAPHY;

EID: 77951950841     PISSN: 15224880     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIP.2009.5413887     Document Type: Conference Paper

References (16)

1. Y. Kang and K. Engelke and W. A. Kalender, New accurate and precise 3D segmentation method for skeletal structures in volumetric CT data IEEE Transaction on Medical Imaging (TMI). vol. 22, no. 5, pp. 586-598, 2003.
2. W. A. Kalender and D. Felsenberg and H. Genant and M. Fischer and J. Dequeker and J. Reeve, The European Spine Phantom - a tool for standardization and quality control in spinal bone measurements by DXA and QCT European J. Radiology vol. 20, pp. 83-92, 1995.
3. A. Mastmeyer and K. Engelke and C. Fuchs and W. A. Kalender, A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine Medical Image Analysis. vol. 10, no. 4, pp. 560-577, 2006.
4. www.back.com.
5. J. Kaminsky and P. Klinge and M. Bokemeyer and W. Luedemann and M Samii, Specially adapted interactive tools for an improved 3D-segmentation of the spine Computerized Medical Imaging and Graphics vol. 28, no. 3, pp. 119-127, 2004.
6. S. Tan and J. Yao and M. M. Ward and L. Yao and R. M. Summers, Computer aided evaluation of Ankylosing Spondylitis IEEE International Symposium on Biomedical Imaging (ISBI'06) pp. 339-342, 2006.
7. S. Tan and J. Yao and M. M. Ward and L. Yao and R. M. Summers, Computer Aided Evaluation of Ankylosing Spondylitis Using High-Resolution CT IEEE Transaction on Medical Imaging (TMI). vol. 27, no. 9, pp. 1252-1267, 2008.
8. L. Ibanez and W. Schroeder and J. Cates, The ITK software guide Kitware Inc., 2003.
9. Thomas B. Sebastiana and Hseyin Teka and Joseph J. Criscob and Benjamin B. Kimia, Segmentation of carpal bones from CT images using skeletally coupled deformable models Medical Image Analysis vol.7, no.1, pp. 21-45, 2003.
10. L. C. Perelman and J. Paradis and E. Barrett, R. A. Zoroofi and Y. Sato and T. Sasama and T. Nishii and N. Sugano and K. Yonenobu and H. Yoshikawa and T. Ochi and S. Tamura Automated segmentation of acetabulum and femoral head from 3-D CT images. IEEE Trans Inf Technol Biomed. vol. 7, no. 4, pp. 329-343, 2003.
11. A. M. Ali and A. A. Farag, Automatic Lung Segmentation of Volumetric Low-Dose CT Scans Using Graph Cuts Proc. of International Symposium on Visual Computing (ISVC'08) pp. 258-267, 2006.
12. W. A. Kalender and E. Klotz and C. Suess, Vertebral bone mineral analysis: an integrated approach with CT Radiology. vol. 164, no. 2, pp. 419-423, 1987.
13. P. Steiger and J. E. Block and S. Steiger and A. F. Heuck and A. Friedlander and B. Ettinger and S. T. Harris and C. C. Gluer and H. K. Genant, Spinal bone mineral density measured with quantitative CT: effect of region of interest, vertebral level, and technique Radiology vol. 175, no. 2, pp. 537-543, 1990.
14. A.A. Farag, A. El-Baz, G. Gimelfarb, Density estimation using modified expectation maximization for a linear combination of gaussians. Proc. of International Conference on Image Processing (ICIP'04) vol. 3, pp. 1871-1874, 2004.
15. Y. Boykol, V. Kolmogorov, An experiment comparison of min-cut/max-flowalgorithms for energy minimization in vision IEEE Transaction on Pattern Analysis and Machine Intelligence vol. 26, pp. 1124-1137, 2004.
16. R. Adams and L. Bischof, Seeded Region Growing IEEE Trans. Pattern Anal. Mach. Intell. vol. 16, no. 6, pp. 641-647, 1994.