3D reconstruction of the human rib cage from 2D projection images using a statistical shape model

  • Jalda DworzakEmail author
  • Hans Lamecker
  • Jens von Berg
  • Tobias Klinder
  • Cristian Lorenz
  • Dagmar Kainmüller
  • Heiko Seim
  • Hans-Christian Hege
  • Stefan Zachow
Original Article



This paper describes an approach for the three-dimensional (3D) shape and pose reconstruction of the human rib cage from few segmented two-dimensional (2D) projection images. Our work is aimed at supporting temporal subtraction techniques of subsequently acquired radiographs by establishing a method for the assessment of pose differences in sequences of chest radiographs of the same patient.


The reconstruction method is based on a 3D statistical shape model (SSM) of the rib cage, which is adapted to binary 2D projection images of an individual rib cage. To drive the adaptation we minimize a distance measure that quantifies the dissimilarities between 2D projections of the 3D SSM and the projection images of the individual rib cage. We propose different silhouette-based distance measures and evaluate their suitability for the adaptation of the SSM to the projection images.


An evaluation was performed on 29 sets of biplanar binary images (posterior–anterior and lateral). Depending on the chosen distance measure, our experiments on the combined reconstruction of shape and pose of the rib cages yield reconstruction errors from 2.2 to 4.7mm average mean 3D surface distance. Given a geometry of an individual rib cage, the rotational errors for the pose reconstruction range from 0.1° to 0.9°.


The results show that our method is suitable for the estimation of pose differences of the human rib cage in binary projection images. Thus, it is able to provide crucial 3D information for registration during the generation of 2D subtraction images.


Geometry reconstruction Biplanar projection images Distance measure 3D pose difference 


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  1. 1.
    van Ginneken B, Ter Haar Romeny B, Viergever M (2001) Computer-aided diagnosis in chest radiography: a survey. IEEE Trans Med Imaging 20(12): 1228–1241CrossRefPubMedGoogle Scholar
  2. 2.
    Kano A, Doi K, MacMahon H, Hassell DD, Giger ML (1994) Digital image subtraction of temporally sequential chest images for detection of interval change. Med Phys 21: 453–461CrossRefPubMedGoogle Scholar
  3. 3.
    Kakeda S, Kamada K, Hatakeyama Y, Aoki T, Korogi Y, Katsuragawa S, Doi K (2006) Effect of temporal subtraction technique on interpretation time and diagnostic accuracy of chest radiography. Am J Roentgenol 187(5): 1253–1259CrossRefGoogle Scholar
  4. 4.
    von Berg J, Manke D, Schaefer-Prokop C, Neitzel U (2008) Impact of patient pose differences on subtle interval change detection by temporal subtraction in chest radiographs. A phantom study, Eur Radiol 18, Suppl 1. In: Proceedings of ECR 2008, p 212Google Scholar
  5. 5.
    Dubousset J, Charpak G, Dorion I, Skalli W, Lavaste F, Deguise J, Kalifa G, Ferey S (2005) A new 2D and 3D imaging approach to musculoskeletal physiology and pathology with low-dose radiation and the standing position: the EOS system. Bull Acad Natl Med 189: 287–297PubMedGoogle Scholar
  6. 6.
    Bertrand S, Laporte S, Parent S, Skalli W, Mitton D (2008) Three-dimensional reconstruction of the rib cage from biplanar radiography. ITBM-RBM 29(4): 278–286Google Scholar
  7. 7.
    van Ginneken B, Ter Haar Romeny BM (2000) Automatic delineation of ribs in frontal chest radiograph. Proc SPIE 3979: 825–836CrossRefGoogle Scholar
  8. 8.
    Yue Z, Goshtasby A, Ackerman L (1995) Automatic detection of rib borders in chest radiographs. IEEE Trans Med Imaging 14(3): 525–536CrossRefPubMedGoogle Scholar
  9. 9.
    Loog M, van Ginneken B (2006) Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification. IEEE Trans Med Imaging 25: 602–611CrossRefPubMedGoogle Scholar
  10. 10.
    Park M, Jin JS, Wilson LS (2003) Detection and labeling ribs on expiration chest radiographs. Medical Imaging 2003: Physics of Medical Imaging. In: Proceedings of the SPIE, vol 5030, pp 1021–1031Google Scholar
  11. 11.
    Rucklidge WJ (1997) Efficiently locating objects using the Hausdorff distance. Int J Comput Vis 24: 251–270CrossRefGoogle Scholar
  12. 12.
    Dubuisson M, Jain A (1994) A modified Hausdorff distance for object matching Pattern Recognition. In: Proceedings of the 12th IAPR international conference on computer vision and imaging process, vol 1, pp 566–568Google Scholar
  13. 13.
    Besl P, McKay H (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2): 239–256CrossRefGoogle Scholar
  14. 14.
    Feldmar J, Ayache N, Betting F (1995) 3D-2D projective registration of free-form curves and surfaces. ICCV’95, p 549Google Scholar
  15. 15.
    Fleute M, Lavallée S (1999) Nonrigid 3-D/2-D registration of images using statistical models. MICCAI 1999. LNCS, vol 1679, pp 138–147Google Scholar
  16. 16.
    Lamecker H, Wenckebach TH, Hege HC (2006) Atlas-based 3D-shape reconstruction from X-ray images. In: Proceedings of the international conference on pattern recognition (ICPR2006), Bd vol I. IEEE Comput Society, Los Alamitos, pp 371–374Google Scholar
  17. 17.
    Zheng G (2006) Reconstruction of patient-specific 3D bone model from biplanar X-ray images and point distribution models. ICIP06, pp 1197–1200Google Scholar
  18. 18.
    Rusinkiewicz S, Levoy M (2001) Efficient variants of the ICP algorithm. In: Proceedings of the international conference on 3-D digital imaging and modeling, pp 145–152Google Scholar
  19. 19.
    Lavallée S, Szeliski R (1995) Recovering the position and orientation of free-form objects from image contours using 3D distance maps. IEEE Trans Pattern Anal Mach Intell 17(4): 378–390CrossRefGoogle Scholar
  20. 20.
    Cyr C, Kamal A, Sebastian T, Kimia B (2000) 2D-3D registration based on shape matching. In: Proceedings of the IEEE Workshop on mathematical methods in biomedical image analysis, pp 198–203Google Scholar
  21. 21.
    Bhunre P, Leow WK, Howe TS (2007) Recovery of 3D Pose of Bones in Single 2D X-ray Images. In: IEEE workshop on applications of computer vision, WACV ’07, p 48Google Scholar
  22. 22.
    Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models—their training and application. Comput Vis Image Underst 61(1): 38–59CrossRefGoogle Scholar
  23. 23.
    Benameur S, Mignotte M, Destrempes F, De Guise JA (2005) Three-dimensional biplanar reconstruction of scoliotic rib cage using the estimation of a mixture of probabilistic prior models. IEEE Trans Biomed Eng 52(10): 1713–1728CrossRefPubMedGoogle Scholar
  24. 24.
    Mahfouz M, Badawi A, Fatah EEA, Kuhn M, Merkl B (2006) Reconstruction of 3D patient-specific bone models from biplanar X-ray images utilizing morphometric measurements. Proc Int Conf Image Process Comput Vis Pattern Recognit IPCV 2: 345–349Google Scholar
  25. 25.
    Danserau J, Srokest IAF (1988) Measurements of the three-dimensional shape of the rib cage. J Biomech 21: 893–901CrossRefGoogle Scholar
  26. 26.
    Marzan GT (1976) Rational design for close-range photogrammetry. PhD Thesis, Department of Civil Engineering, University of Illinois at Urbana-ChampaignGoogle Scholar
  27. 27.
    Delorme S, Petit Y, de Guise J, Labelle H, Aubin C, Dansereau J (2003) Assessment of the 3-D reconstruction and high-resolution geometrical modeling of the human skeletal trunk from 2-D radiographic images. IEEE Trans Biomed Eng 50(8): 989–998CrossRefPubMedGoogle Scholar
  28. 28.
    Novosad J, Cheriet F, Petit Y, Labelle H (2004) Three-dimensional (3-D) reconstruction of the spine from a single X-ray image and prior vertebra models. IEEE Trans Biomed Eng 51(9): 1628–1639CrossRefPubMedGoogle Scholar
  29. 29.
    Mitton D, Zhao K, Bertrand S, Zhao CF, Laporte S, Yang C, An KN, Skallia W (2008) 3D reconstruction of the ribs from lateral and frontal X-rays in comparison to 3D CT-scan reconstruction. J Biomech 41: 706–710CrossRefPubMedGoogle Scholar
  30. 30.
    Dworzak J, Lamecker H, von Berg J, Klinder T, Lorenz H, Kainmüller D, Seim H, Hege HC, Zachow S (2008) Towards Model-based 3-D Reconstruction of the Human Rib Cage from Radiographs. In: Proc 7. Jahrestag der Dtsch Ges für Computer- und Roboterassistierte Chirurgie (CURAC), pp 193–196Google Scholar
  31. 31.
    Klinder T, Lorenz C, von Berg J, Dries SPM, Bülow T, Ostermann J (2007) Automated model-based rib cage segmentation and labeling in CT images. MICCAI 2007, Part II. LNCS, vol 4792, pp 195–202Google Scholar
  32. 32.
    Rohlfing T (2000) Multimodale Datenfusion für die bildgesteuerte Neurochirurgie und Strahlentherapie. PhD Thesis, Technische Universität BerlinGoogle Scholar

Copyright information

© CARS 2009

Authors and Affiliations

  • Jalda Dworzak
    • 1
    Email author
  • Hans Lamecker
    • 1
  • Jens von Berg
    • 2
  • Tobias Klinder
    • 2
  • Cristian Lorenz
    • 2
  • Dagmar Kainmüller
    • 1
  • Heiko Seim
    • 1
  • Hans-Christian Hege
    • 1
  • Stefan Zachow
    • 1
  1. 1.Visualization and Data AnalysisZuse Institute BerlinBerlinGermany
  2. 2.Medical Imaging SystemsPhilips Research EuropeHamburgGermany

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