Combined surface and volume processing for fused joint segmentation

  • Peter R. KrekelEmail author
  • Edward R. Valstar
  • Frits H. Post
  • P. M. Rozing
  • Charl P. Botha
Original Article



Segmentation of rheumatoid joints from CT images is a complicated task. The pathological state of the joint results in a non-uniform density of the bone tissue, with holes and irregularities complicating the segmentation process. For the specific case of the shoulder joint, existing segmentation techniques often fail and lead to poor results. This paper describes a novel method for the segmentation of these joints.


Given a rough surface model of the shoulder, a loop that encircles the joint is extracted by calculating the minimum curvature of the surface model. The intersection points of this loop with the separate CT-slices are connected by means of a path search algorithm. Inaccurate sections are corrected by iteratively applying a Hough transform to the segmentation result.


As a qualitative measure we calculated the Dice coefficient and Hausdorff distances of the automatic segmentations and expert manual segmentations of CT-scans of ten severely deteriorated shoulder joints. For the humerus and scapula the median Dice coefficient was 98.9% with an interquartile range (IQR) of 95.8–99.4 and 98.5% (IQR 98.3–99.2%), respectively. The median Hausdorff distances were 3.06 mm (IQR 2.30–4.14) and 3.92 mm (IQR 1.96 –5.92 mm), respectively.


The routine satisfies the criterion of our particular application to accurately segment the shoulder joint in under 2 min. We conclude that combining surface curvature, limited user interaction and iterative refinement via a Hough transform forms a satisfactory approach for the segmentation of severely damaged arthritic shoulder joints.


Segmentation Curvature Hough transform Rheumatoid joints Glenohumeral joint 


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  1. 1.
    Aspert N, Santa-Cruz D, Ebrahimi T (2002) Mesh: measuring errors between surfaces using the hausdorff distance. In: Proceedings of the IEEE international conference on multimedia and expo I, pp 705–708Google Scholar
  2. 2.
    Botha CP (2005) Techniques and software architectures for medical visualisation and image processing. Ph.D. thesis, Delft University of Technology. URL:
  3. 3.
    Boileau P, Walch G (1997) The three-dimensional geometry of the proximal humerus. implications for surgical technique and prosthetic design. J Bone Joint Surg Br 79(5): 857–865CrossRefPubMedGoogle Scholar
  4. 4.
    Branzan-Albu A, Laurendeau D, Hèbert L, Moffet H, Dufour M, Moisan C (2004) Image-guided analysis of shoulder pathologies: modeling the 3d deformation of the subacromial space during arm flexion and abduction. In: Stephane Cotin DM (ed) International symposium on medical simulation (ISMS 2004). vol 1 of lecture notes in computer science. Springer, Cambridge, pp 193–202Google Scholar
  5. 5.
    Bresenham J (1965) Algorithm for computer control of a digital plotter. IBM Syst J 4(4): 25–30CrossRefGoogle Scholar
  6. 6.
    Chambers EW, Èric Colin de Verdière, Erickson J, Lazarus F, Whittlesey K (2008) Splitting (complicated) surfaces is hard: computational geometry 41 (1–2), 94–110, special Issue on the 22nd European workshop on computational geometry (EuroCG), 22nd European workshop on computational geometryGoogle Scholar
  7. 7.
    Di Gioia III, MAM, Blendea S, Jaramaz B (2007) Surgical navigation for total hip arthroplasty. In: Callaghan JJ , Rosenberg AGHR (eds) The adult hip, 2nd edn. vol 2. Lippincott Williams & Wilkins, pp 1053–1059Google Scholar
  8. 8.
    Dijkstra E (1959) A note on two problems in connexion with graphs. Numerische Mathematik 1(1): 269–271CrossRefGoogle Scholar
  9. 9.
    Duda R, Hart P (1972) Use of the Hough transform to detect lines and curves in pictures Comm. ACM 15: 11–15CrossRefGoogle Scholar
  10. 10.
    Fleute M, Lavallee S (1998) Building a complete surface model from sparse data using statistical shape models: application to computer assisted knee surgery system. In: MICCAI. pp 879–887Google Scholar
  11. 11.
    Kang Y, Engelke K, Kalender WA (2003) A new accurate and precise 3-d segmentation method for skeletal structures in volumetric ct data. IEEE Trans Med Imaging 22(5): 586–598CrossRefPubMedGoogle Scholar
  12. 12.
    Krekel P, Botha C, Valstar E, de Bruin P, Rozing P, Post F (2006) Interactive simulation and comparative visualisation of the bone-determined range of motion of the human shoulder. In: Schulze T, Horton G, Preim B, Schlechtweg S (eds) Proceedings of the simulation and visualization. SCS Publishing House, Erlangen, pp 275–288, best Paper AwardGoogle Scholar
  13. 13.
    Larsen A (1995) How to apply larsen score in evaluating radiographs of rheumatoid arthritis in long-term studies. J Rheumatol 22: 1974–1975PubMedGoogle Scholar
  14. 14.
    Lorensen WE, Cline HE (1987) Marching cubes: a high resolution 3d surface construction algorithm. Comput Graphics 21(4): 163–169CrossRefGoogle Scholar
  15. 15.
    Mangan AP, Whitaker RT (1999) Partitioning 3d surface meshes using watershed segmentation. IEEE Trans Vis Comput Graph 5(4): 308–321CrossRefGoogle Scholar
  16. 16.
    Page DL, Sun Y, Koschan AF, Paik Paik J, Abidi MA (2002) Normal vector voting:crease detection and curvature estimation on large, noisy meshes. Graphical Models 64Google Scholar
  17. 17.
    Page DL, Koschan AF, Abidi MA (2003) Perception-based 3d triangle mesh segmentation using fast marching watersheds. cvpr 02, 27Google Scholar
  18. 18.
    Pitiot A, Delingette H, Thompson PM, Ayache N (2004) Expert knowledge-guided segmentation system for brain mri. Neuroimage 23(Suppl 1): S85–S96CrossRefPubMedGoogle Scholar
  19. 19.
    Prescher A (2000) Anatomical basics, variations, and degenerative changes of the shoulder joint and shoulder girdle. Eur J Radiol 35(2): 88–102CrossRefPubMedGoogle Scholar
  20. 20.
    Rajamani KT, Styner MA, Talib H, Zheng G, Nolte LP, Ballester MAG (2007) Statistical deformable bone models for robust 3d surface extrapolation from sparse data. Med Image Anal 11(2): 99–109CrossRefPubMedGoogle Scholar
  21. 21.
    Sato Y, Sasama T, Sugano N, Nakahodo K, Nishii T, Ozono K, Yonenobu K, Ochi T, Tamura S (2000) Intraoperative simulation and planning using a combined acetabular and femoral (caf) navigation system for total hip replacement. pp 1114–1125Google Scholar
  22. 22.
    Tao X, Prince JL, Davatzikos C (2002) Using a statistical shape model to extract sulcal curves on the outer cortex of the human brain. IEEE Trans Med Imaging 21(5): 513–524CrossRefPubMedGoogle Scholar
  23. 23.
    Taubin G (1995) A signal processing approach to fair surface design. In: SIGGRAPH’95: Proceedings of the 22nd annual conference on Computer graphics and interactive techniques, ACM, New York, pp 351–358Google Scholar
  24. 24.
    Tremblay M-E, Branzan-Albu A, Laurendeau D, Hèbert L (2004) Integrating region and edge information for the automatic segmentation for interventional magnetic resonance images of the shoulder complex. In: 1st Canadian conference on computer and robot vision (CRV2004). vol 1. IEEE Computer Society, London, Ontario, pp 279–286Google Scholar
  25. 25.
    van der Glas M, Vos FM, Botha CP, Vossepoel AM (2002) Determination of position and radius of ball joints. In: Sonka M (ed) Proceedings of the SPIE international symposium on medical imaging. vol 4684—image processingGoogle Scholar
  26. 26.
    Vollmer J, Mencel R, Mueller H (1999) Improved laplacian smoothing of noisy surface meshes. Comput Graph Forum 18: 131–138CrossRefGoogle Scholar
  27. 27.
    von Schroeder HP, Kuiper SD, Botte MJ (2001) Osseous anatomy of the scapula. Clin Orthop Relat Res 383: 131–139CrossRefGoogle Scholar
  28. 28.
    Wu C-H, Sun Y-N (2006) Segmentation of kidney from ultrasound b-mode images with texture-based classification. Comput Methods Programs Biomed 84(2/3): 114–123CrossRefPubMedGoogle Scholar
  29. 29.
    Zoroofi RA, Sato Y, Sasama T, Nishii T, Sugano N, Yonenobu K, Yoshikawa H, Ochi T, Tamura S (2003) Automated segmentation of acetabulum and femoral head from 3-d ct images. IEEE Tran Inf Technol Biomed 7(4): 329–343CrossRefGoogle Scholar

Copyright information

© CARS 2009

Authors and Affiliations

  • Peter R. Krekel
    • 1
    Email author
  • Edward R. Valstar
    • 1
  • Frits H. Post
    • 2
  • P. M. Rozing
    • 1
  • Charl P. Botha
    • 2
  1. 1.Department of OrthopaedicsLeiden University Medical CenterLeidenThe Netherlands
  2. 2.Visualisation DepartmentDelft University of TechnologyDelftThe Netherlands

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