Vertebral body segmentation using a probabilistic and universal shape model

Melih S. Aslan; Ahmed Shalaby; Aly A. Farag

Vertebral body segmentation using a probabilistic and universal shape model

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Author(s): Melih S. Aslan ^{1, 2} ; Ahmed Shalaby ² ; Aly A. Farag ²
- Affiliations: 1: Computer Science Department, Wayne State University, Detroit, MI 48202, USA;
  2: CVIP Lab, University of Louisville, Louisville, KY 40208, USA
Source: Volume 9, Issue 2, April 2015, p. 234 – 250
DOI: 10.1049/iet-cvi.2013.0154 , Print ISSN 1751-9632, Online ISSN 1751-9640

Received 10/07/2013, Accepted 13/06/2014, Revised 02/06/2014, Published 30/07/2014

Osteoporosis is a bone disease characterised by a reduction in bone mass, resulting in an increased risk of fractures. Doctors need the bone mineral density (BMD) measurements of vertebral bodies in order to diagnose and treat osteoporosis. The authors' objective is to segment the VBs as accurately as possible and hence to increase the accuracy of the BMD measurements and fracture analysis. Three pieces of information (intensity, spatial interaction and shape) are modelled to optimise a probabilistic energy functional. A universal shape prior, which is modelled using the cervical, thoracic and lumbar spinal regions, is proposed. Volumetric computed tomography data sets with various challenges are used in this study. The authors classify data sets based on some features related to the anatomy, imaging modality and level of the bone health. The proposed framework is one of only a few reported in the literature tested on the data obtained from different imaging devices. The experimental results reveal that the proposed method is robust under various noise levels, less variant to the initialisation and faster than existing vertebrae segmentation reports in the literature.

References

1. 1)
  - 24. Sebastiana, T.B., Teka, H., Criscob, J.J., Kimia, B.B.: ‘Segmentation of carpal bones from CT images using skeletally coupled deformable models’, Med. Image Anal., 2003, 7, (1), pp. 21–45 (doi: 10.1016/S1361-8415(02)00065-8).
2. 2)
  - 37. Klinder, T., Ostermann, J., Ehm, M., Franz, A., Kneser, R., Lorenz, C.: ‘Automated model-based vertebra detection, identification, and segmentation in ct images’, Med. Image Anal., 2009, 13, pp. 471–482 (doi: 10.1016/j.media.2009.02.004).
3. 3)
  - 28. Schmid, J., Kim, J., Thalmann, N.M.: ‘Robust statistical shape models for mri bone segmentation in presence of small field of view’, Med. Image Anal., 2011, 15, pp. 155–168 (doi: 10.1016/j.media.2010.09.001).
4. 4)
  - 14. Kang, Y., Engelke, K., Kalender, W.A.: ‘New accurate and precise 3D segmentation method for skeletal structures in volumetric CT data’, IEEE Trans. Med. Imaging, 2003, 22, (5), pp. 586–598 (doi: 10.1109/TMI.2003.812265).
5. 5)
  - 25. Alomari, R.S., Corso, J.J., Chaudhary, V.: ‘Labeling of lumbar discs using both pixel- and object-level features with a two-level probabilistic model’, IEEE Trans. Med. Imaging, 2011, 30, (1), pp. 1–10 (doi: 10.1109/TMI.2010.2047403).
6. 6)
  - 51. Nelder, A.J., Mead, R.: ‘A simplex method for function minimization’, Comput. J., 1968, 7, pp. 308–313 (doi: 10.1093/comjnl/7.4.308).
7. 7)
  - T.F. Cootes , G.J. Edwards , C.J. Taylor . Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. , 6 , 681 - 685
8. 8)
  - 30. Abd El Munim, H.: ‘Implicit curve/surface evolution with application to the image segmentation problem’, PhD thesis, University of Louisville, 2007.
9. 9)
  - 6. Farag, A.A., El-Baz, A., Gimelfarb, G.: ‘Density estimation using modified expectation maximization for a linear combination of Gaussians’. Proc. of the Int. Conf. on Image Processing, 2004, vol. 3, pp. 1871–1874.
10. 10)
  - 11. Ali, A.M., Farag, A.A.: ‘Automatic lung segmentation of volumetric low-dose CT scans using graph cuts’. Proc. of the Int. Symp. on Visual Computing (ISVC'08), 2008, pp. 258–267.
11. 11)
  - 52. Aslan, M.S., Mostafa, E., Abdelmunim, H., Shalaby, A., Farag, A.A., Arnold, B.: ‘A novel probabilistic simultaneous segmentation and registration using level set’. Proc. of the Int. Conf. on Image Processing (ICIP), 2011, pp. 2161–2164.
12. 12)
  - 7. Cremers, D., Osher, S.J., Soatto, S.: ‘Kernel density estimation and intrinsic alignment for shape priors in level set segmentation’, Int. J. Comput. Vis., 2006, 69, (3), pp. 335–351 (doi: 10.1007/s11263-006-7533-5).
13. 13)
  - 19. Kim, Y., Kim, D.: ‘A fully automatic vertebra segmentation method using 3D deformable fences’, Comput. Med. Imaging Graph., 2009, 33, pp. 343–352 (doi: 10.1016/j.compmedimag.2009.02.006).
14. 14)
  - 12. Boykov, Y., Lee, V.S., Rusinek, H., Bansal, R.: ‘Segmentation of dynamic n-d data sets via graph cuts using Markov models’. Proc. of MICCAI-2001 (LNCS, 2208), 2001, pp. 1058–1066.
15. 15)
  - 26. Kelm, B.M., Wels, M., Zhou, S.K., et al: ‘Spine detection in CT and MR using iterated marginal space learning’, Med. Image Anal., 2012, 17, (8), pp. 1283–1292 (doi: 10.1016/j.media.2012.09.007).
16. 16)
  - 5. Borman, S.: ‘The expectation maximization algorithm: a short tutorial’. Technical Report. Available at http://www.seanborman.com/publications,’ 2004.
17. 17)
  - 26. Tsechpenakis, G., Chatzis, S.P.: ‘Deformable probability maps: probabilistic shape and appearance-based object segmentation’, Comput. Vis. Image Underst., 2011, 115, (8), pp. 1157–1169 (doi: 10.1016/j.cviu.2010.09.010).
18. 18)
  - 16. Mastmeyer, A., Engelke, K., Fuchs, C., Kalender, W.A.: ‘A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine’, Med. Image Anal., 2006, 10, (4), pp. 560–577 (doi: 10.1016/j.media.2006.05.005).
19. 19)
  - 21. Aslan, M.S., Ali, A., Farag, A.A., Abdelmunim, H., Arnold, B., Xiang, P.: ‘A new segmentation and registration approach for vertebral body analysis’. Proc. of IEEE Int. Symp. on Biomedical Imaging (ISBI), 2011, pp. 2006–2009.
20. 20)
  - 22. Aslan, M.S., Shalaby, A., Farag, A.A.: ‘Clinically desired segmentation method for vertebral bodies’. Proc. of IEEE Int. Symp. on Biomedical Imaging (ISBI), 2013, pp. 840–843.
21. 21)
  - 33. Leventon, M., Grimson, E., Faugeras, O.: ‘Statistical shape influence in geodesic active contours’. Proc. of Int. Conf. on CVPR, 2000, vol. 1, pp. 316–323.
22. 22)
  - 17. Kaminsky, J., Klinge, P., Bokemeyer, M., Luedemann, W., Samii, M.: ‘Specially adapted interactive tools for an improved 3D-segmentation of the spine’, Comput. Med. Imaging Graph., 2004, 28, (3), pp. 119–127 (doi: 10.1016/j.compmedimag.2003.12.001).
23. 23)
  - 39. North, O.H.: ‘An analysis of the factors which determine signal/noise discrimination in pulsed-carrier Systems’. Proc. of IEEE:RCA Labs., Princeton, NJ, Rep. PTR-6C., 1943, pp. 1016–1063.
24. 24)
  - 3. Duda, R., Hart, P., Stork, D.: ‘Pattern classification’ (John Wiley and Sons Inc., 2001).
25. 25)
  - 23. Tan, S., Yao, J., Ward, M.M., Yao, L., Summers, R.M.: ‘Computer aided evaluation of ankylosing spondylitis using high-resolution CT’, IEEE Trans. Med. Imaging, 2008, 27, (9), pp. 1252–1267 (doi: 10.1109/TMI.2008.920612).
26. 26)
  - 13. Aslan, M.S., Ali, A., Rara, H., et al: ‘A Novel 3D segmentation of vertebral bones from volumetric CT images using graph cuts’. Fifth Int. Symp. on Visual Computing (ISVC'09), 2009, vol. 5876, pp. 519–528.
27. 27)
  - 38. Aslan, M.S.: ‘Probabilistic and geometric shape based segmentation methods’, PhD dissertation, University of Louisville, 2012.
28. 28)
  - 1. Department of Health and Human Services: ‘A report of the surgeon general: bone health and osteoporosis’, U. S. Public Health Service, 2004.
29. 29)
  - 40. Turin, G.L.: ‘An introduction to matched filters’, IEEE Trans. Inf. Theory, 1960, 6, (3), pp. 311–329 (doi: 10.1109/TIT.1960.1057571).
30. 30)
  - 32. Ali, A.: ‘Image labeling by energy minimization with appearance and shape priors’, PhD dissertation, University of Louisville, 2008.
31. 31)
  - 25. Tsai, A., Yezzi, A., Wells, W., et al: ‘A shape-based approach to the segmentation of medical imagery using level sets’, IEEE Trans. Med. Imaging, 2003, 22, (2), pp. 137–154 (doi: 10.1109/TMI.2002.808355).
32. 32)
  - S. Geman , D. Geman . Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. , 6 , 721 - 741
33. 33)
  - 43. Reddy, C., Rajaratnam, B.: ‘Learning mixture models via component-wise parameter smoothing’, Comput. Stat. Data Anal., 2010, 54, pp. 732–749 (doi: 10.1016/j.csda.2009.04.012).
34. 34)
  - T. Chan , L. Vese . Active contours without edges. IEEE Trans. Image Process. , 2 , 266 - 277
35. 35)
  - 15. Kalender, W.A., Felsenberg, D., Genant, H., Fischer, M., Dequeker, J., Reeve, J.: ‘The European Spine Phantom – a tool for standardization and quality control in spinal bone measurements by DXA and QCT’, Eur. J. Radiology, 1995, 20, pp. 83–92 (doi: 10.1016/0720-048X(95)00631-Y).
36. 36)
  - 2. Roberts, M., Cootes, T., Adams, J.: ‘Vertebral shape: automatic measurement with dynamically sequenced active appearance models’. Proc. of the Int. Conf. Medical Image Computing and Computer Assisted Intervention, 2005, vol. 2, pp. 733–740.
37. 37)
  - 44. Ali, A., Farag, A.A., Gimel'farb, G.: ‘Analytical method for MGRF Potts model parameters estimation’. Proc. of the Int. Conf. on Pattern Recognition (ICPR-08), 2008.
38. 38)
  - 50. Besag, J.E.: ‘On the statistical analysis of dirty pictures’, J. R. Stat. Soc., B, 1986, 48, pp. 259–302.
39. 39)
  - S. Osher , J.A. Sethian . Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations. J. Comput. Phys. , 1 , 12 - 49
40. 40)
  - R.C. Dubes , A.K. Jain . Random field models in image analysis. J. Appl. Stat. , 131 - 164
41. 41)
  - 42. Aslan, M.S., Ali, A., Rara, H., Farag, A.A.: ‘An automated vertebra identification and segmentation in CT images’. Proc. Fifth Int. Conf. Image Processing (ICIP'10), 2010, pp. 233–236.
42. 42)
  - 31. Saha, P.K., Liang, G., Elkins, J.M., et al: ‘A new osteophyte segmentation algorithm using the partial shape model and its applications to rabbit femur anterior cruciate ligament transection via micro-ct imaging’, IEEE Trans. Biomed. Eng., 2011, 58, (8), pp. 2212–2238 (doi: 10.1109/TBME.2011.2129519).
43. 43)
  - H. Derin , H. Elliott . Modeling and segmentation of noisy and textured images using Gibbs random fields. IEEE Trans. Pattern Anal. Mach. Intell. , 1 , 39 - 55
44. 44)
  - 10. Li, C., Xu, C., Gui, C., Fox, M.D.: ‘Level set evolution without re-initialization: a new variational formulation’. Proc. of the 2005 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR'05), 2005.
45. 45)
  - M. Kass , A. Witkin , D. Terzopoulos . Snakes: active contour models. Int. J. Comput. Vis. , 4 , 321 - 331
46. 46)
  - 34. Stegmann, M.B.: ‘Active appearance models: theory, extensions, and cases’, Master thesis, Technical University of Denmark, 2000.
47. 47)
  - J.C. Lagarias , J.A. Reeds , M.H. Wright , P.E. Wright . Convergence properties of the Nelder–Mead simplex method in low dimensions. SIAM J. Optim. , 1 , 112 - 147
48. 48)
  - 51. Goldman, L.W.: ‘Principles of CT: radiation dose and image quality’, J. Nucl. Med. Technol., 2007, 35, pp. 213–225 (doi: 10.2967/jnmt.106.037846).
49. 49)
  - T. Moon . The expectation-maximization algorithm. IEEE Signal. Process. Mag. , 6 , 47 - 60
50. 50)
  - 50. Kumar, B.V.K.V., Savvides, M., Xie, C.: ‘Correlation pattern recognition for face recognition’, Proc. IEEE, 2006, 94, (11), pp. 1963–1976 (doi: 10.1109/JPROC.2006.884094).
51. 51)
  - 27. Cremers, D.: ‘Statistical shape knowledge in variational image segmentation’, PhD dissertation, 2002.
52. 52)
  - 20. Yao, J., O’ Connor, S.D., Summers, R.M.: ‘Automated spinal column extraction and partitioning’. Proc. of IEEE Int. Symp. on Biomedical Imaging, 2006.

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