Three-dimensional biplanar reconstruction of scoliotic rib cage using the estimation of a mixture of probabilistic prior models

IEEE Transactions on Biomedical Engineering

Volumn 52, Issue 10, 2005, Pages 1713-1728

  Benameur, Said ^a,b,d   Mignotte, Max ^a,b,e   Destrempes, François ^b   De Guise, Jacques A ^a,c  

^a CENTRE HOSPITALIER DE L UNIVERSITÉ DE MONTRÉAL   (Canada)

^b UNIVERSITÉ DE MONTRÉAL   (Canada)

^c ÉCOLE DE TECHNOLOGIE SUPÉRIEURE   (Canada)

^d Image Processing Laboratory   (Canada)

^e DIRO   (Canada)

Author keywords

3 D reconstruction model; 3 D 2 D registration; Biplanar radiographies; Medical imaging; Mixtures of probabilistic principal component analyzers; Reduction of dimensionality; Scoliosis; Shape model; Stochastic optimization

Indexed keywords

3-D RECONSTRUCTION MODEL; 3-D/2-D REGISTRATION; BIPLANAR RADIOGRAPHIES; MIXTURES OF PROBABLISTIC PRINCIPAL COMPONENT ANALYZERS; REDUCTION OF DIMENSIONALITY; SCOLIOSIS; SHAPE MODEL; STOCHASTIC OPTIMIZATION;

ALGORITHMS; COMPUTERIZED TOMOGRAPHY; DATABASE SYSTEMS; IMAGE PROCESSING; MATHEMATICAL MODELS; MEDICAL IMAGING; PATHOLOGY; PRINCIPAL COMPONENT ANALYSIS;

RADIOGRAPHY;

ALGORITHM; ARTICLE; COMPUTER ASSISTED TOMOGRAPHY; DATA BASE; ENERGY; HUMAN; MATHEMATICAL COMPUTING; RADIATION; RADIOGRAPHY; RIB MALFORMATION; SCOLIOSIS; THORAX; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; IMAGING, THREE-DIMENSIONAL; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PRINCIPAL COMPONENT ANALYSIS; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RIBS; SCOLIOSIS; SUBTRACTION TECHNIQUE; SYSTEMS INTEGRATION;

EID: 26444523397     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2005.855717     Document Type: Article

References (41)

1. S. Delorme, P. Violas, J. Dansereau, J. de Guise, C. Aubin, and H. Labelle, "Preoperative and early postoperative three-dimensional changes of the rib cage after posterior instrumentation in adolescent idiopathic scoliosis," Eur. Spine J., vol. 10, pp. 101-106, 2000.
2. B. Carvalho and G. Herman, "Helical CT reconstruction from wide cone-beam angle data using ART," in Proc. XVI Brazilian Symp. Computer Graphics and Image Processing, São Carlos, São Paulo, Brazil, Oct. 2003, pp. 363-370.
3. A. Reeves, D. Yankelevitz, and C. Henschke, "Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images," IEEE Trans. Med. Imag., vol. 22, no. 10, pp. 1259-1274, Oct. 2003.
4. J. Lötjönen, P.-J. Reissman, I. Magnin, and T. Katila, "Model extraction from magnetic resonance volume data using the deformable pyramid," Med. Image Anal., vol. 3, no. 4, pp. 387-406, 1999.
5. S. Sandor and R. Leahy, "Surface-based labeling of cortical anatomy using a deformable atlas," IEEE Trans. Med. Imag., vol. 16, no. 1, pp. 41-54, Feb. 1997.
6. J. Dansereau and I. Stokes, "Measurements of three dimensional shape of the rib cage," J. Biomech., vol. 21, pp. 893-901, 1988.
7. G. Marzan, "Rational Design for Close-Range Photogrammetry," Ph.D. dissertation, Dept. Civil Eng., Univ. Illinois at Urbana-Champaign, Urbana, 1976.
8. E. Marcil, J. Dansereau, H. Labelle, and J. D. Guise, "Incorporation of patient displacement into a trunk reconstruction technique," in Proc. IEEE 17th Annu. Conf. Engineering in Medicine and Biology Society, vol. 1, Sep. 1995, pp. 385-386.
9. S. Benameur, M. Mignotte, S. Parent, H. Labelle, W. Skalli, and J. D. Guise, "A hierarchical statistical modeling approach for the unsupervised 3-D reconstruction of the scoliotic spine," in Proc. 10th IEEE Int. Conf. Image Processing, vol. 1, Barcelona, Spain, Sep. 2003, pp. 561-564.
10. S. Mouren, "Reconstruction 3-D Biplanaire de Cages Thoraciques Scoliotiques à l'aide de modèles déformables de côtes," Master's thesis, Dept. Mech. Eng., Ecole Polytechnique, Montréal, QC, Canada, 2003.
11. M. Tipping and C. Bishop, "Mixtures of probabilistic principal component analyzers," Neural Computation, vol. 11, no. 2, pp. 443-482, 1999.
12. G. Celeux and J. Diebolt, "The SEM algorithm: A probabilistic teacher algorithm derived from the EM algorithm for the mixture problem," Computational Statist. Quart., vol. 2, pp. 73-82, 1985.
13. J. Diebolt and G. Celeux, "Asymptotic properties of a stochastic EM algorithm for estimating mixture proportions," Stochastic Models, vol. 9, pp. 599-613, 1993.
14. B. Delyon, M. Lavielle, and E. Moulines, "Convergence of a stochastic approximation version of the EM algorithm," Ann. Statist., vol. 27. no. 1, pp. 94-128, 1999.
15. D. Chauveau, "A stochastic EM algorithm for mixtures with censored data," J. Statistical Planning Inference, vol. 46, pp. 1-25, 1995.
16. G. Celeux, D. Chauveau, and J. Diebolt, "Stochastic versions of the EM algorithm," J. Statistical Computation Simulat., vol. 55, pp. 287-314, 1996.
17. T. Lei and W. Sewchand, "Statistical approach to X-ray CT imaging and its applications in image analysis-II: A new stochastic model-based image segmentation technique for X-ray CT image," IEEE Trans. Med. Imag., vol. 11, no. 1, pp. 62-69, Mar. 1992.
18. T. Lei and J. Udupa, "Performance evaluation of finite normal mixture model-based image segmentation techniques," IEEE Trans. Image Process., vol. 12, no. 10, pp. 1153-1169, Oct. 2003.
19. F. Destrempes and M. Mignotte, "Unsupervised localization of shapes using statistical models," in Proc. Int. Conf. Signal and Image Processing, Kauai. HI, Aug. 2002, pp. 60-65.
20. C. Drexler, F. Mattern, and J. Denzler, "Hierarchic object models and classification based on probabilistic PCA," in Proc. IAPR Workshop Machine Vision Applications, Nara, Japan, Dec. 2002, pp. 435-438.
21. K. Grauman, G. Shakhnarovich, and T. Darrell, "Inferring 3-D structure with a statistical image-based shape model," in Proc. IEEE Int. Conf. Computer Vision, Nice, France, Oct. 2003, pp. 641-648.
22. M. Tipping and C. Bishop, "Probabilistic principal component analysis," J. Roy. Statist. Soc., ser. B, vol. 21, no. 3, pp. 611-622, 1999.
23. F. Destrempes, "Détection non-supervisée de contours et localization de formes à l'aide de modèles statistiques," Master's thesis, Dept. Comput. Sci. Operations Res., University of Montréal, Montréal, QC, Canada, 2002.
24. J. Hartigan and M. Wong, "A K-means clustering algorithm," Appl. Statist., vol. 28. pp. 100-108, 1979.
25. G. Celeux and G. Soromenho, "An entropy criterion for assessing the number of clusters in a mixture model," J. Classification, vol. 13, pp. 195-212, 1996.
26. M. Miloslavsky and M. van de Laan, "Fitting of mixtures with unspecified number of components using cross validation distance estimate," Computational Statist. Data Anal., vol. 41, pp. 413-428, 2003.
27. O. Zamir and O. Etzioni, "Web document clustering: A feasibility demonstration," in Proc. 21st Annu. Int. ACM SIGIR Conf. Research and Development in Information Retrieval, Melbourne, Australia, Aug. 1998, pp. 46-54.
28. S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images," IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-6, pp. 721-741, 1984.
29. G. Golub and C. V. Loan, Matrix Computations, 3rd ed. Baltimore, MD: Johns Hopkins Univ. Press, 1996.
30. T. Cootes and C. J. Taylor, "A mixture model for representing shape variation," in Proc. Br. Machine Vision Conf., vol. 1, Essex, U.K., 1997, pp. 110-119.
31. B. Horn, H. Hilden, and S. Negahdaripour, "Closed-form solution of absolute orientation using orthonormal matrices," J. Opt. Soc. Am. A, vol. 5, no. 7, pp. 1127-1135, 1988.
32. P. Besl and N. McKay, "A method for registration of 3-D shapes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, no. 2, pp. 239-256, Feb. 1992.
33. J. Canny, "A computational approach to edge detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 6, pp. 679-697, Jun. 1986.
34. A. Jain, Y. Zhong, and S. Lakshmanan, "Object matching using deformable templates," IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 3, pp. 267-278, Mar. 1996.
35. M. Mignotte, J. Meunier, and J.-C. Tardif, "Endocardial boundary estimation and tracking in echocardiographic images using deformable templates and Markov random fields," Pattern Anal. Applicat., vol. 4, no. 4, pp. 256-271, 2001.
36. M. Mignotte, "Unsupervised statistical sketching for nonphotorealistic rendering models," in Proc. 10th IEEE Int. Conf. Image Processing, vol. 3, Barcelona, Spain, Sep. 2003, pp. 573-577.
37. O. Fancois, "Global optimization with exploration/selection algorithms and simulated annealing," Ann Appl Probability, vol. 12, pp. 248-271, 2002.
38. S. Benameur, M. Mignotte, S. Parent, H. Labelle, W. Skalli, and J. D. Guise, "3D/2D registration and segmentation of scoliotic vertebrae using statistical models," Computerized Med Imag Graphics, vol. 27, no. 5, pp. 321-337, 2003.
39. G. Wiggins, C. Shaffrey, M. Abel, and A. Menezes, "Pediatric spinal deformities," J Neurosurg Am Assoc Neurological Surgeons, vol. 14, no. 1, pp. 1-14, 2003.
40. M. Rehani, G. Bongartz, W. Kalender, S. Golding, L. Gordon, T. Murakami, and P. Shrimpton, "Managing Patient Dose in Computed Tomography," Annals of the International Commission on Radiological Protection, Int. Comm. Radiological Protection (ICRP) 87, vol. 30, 2001.
41. M. Doody, J. Lonstein, M. Stovall, D. Hacker, N. Luckyanov, and C. Land, "Breast cancer mortality after diagnostic radiography: Findings from the U.S. scoliosis cohort study," Spine, vol. 25, no. 16, pp. 2052-2063, 2000.