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Research Article

Mean-shifted surface curvature algorithm for automatic bone shape segmentation in orthopedic surgery planning: a sensitivity analysis

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Pages 128-141 | Received 07 Sep 2011, Accepted 09 Jan 2012, Published online: 02 Apr 2012

References

  • Suero EM, Hüfner T, Stübig T, Krettek C, Citak M. Use of a virtual 3D software for planning of tibial plateau fracture reconstruction. Injury 2010; 41(6)589–591
  • Wong KC, Kumta SM, Leung KS, Ng KW, Ng EWK, Lee KS. Integration of CAD/CAM planning into computer assisted orthopaedic surgery. Comput Aided Surg 2010; 15(4–6)65–74
  • Oka K, Murase T, Moritomo H, Goto A, Sugamoto K, Yoshikawa H. Corrective osteotomy using customized hydroxyapatite implants prepared by preoperative computer simulation. Int J Med Robotics Comput Assist Surg 2010; 6: 186–193
  • Steinberg EL, Menahem A, Dekel S. Preoperative planning of total hip replacement using the TraumaCad™ system. Arch Orthop Trauma Surg 2010; 130: 1429–1432
  • Fornaro J, Keel M, Harders M, Marincek B, Székely G, Frauenfelder T. An interactive surgical planning tool for acetabular fractures: Initial results. J Orthop Surg Res 2010; 5: 50
  • Chintalapani G, Ellingsen LM, Sadowsky O, Prince JL, Taylor RH, Statistical atlases of bone anatomy: Construction, iterative improvement and validation. In: Ayache N, Ourselin S, Maeder AJ, editors. Proceedings of the 10th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2007), Brisbane, Australia, October 29-November 2, 2007. Part I. Lecture Notes in Computer Science 4791. Berlin: Springer; 2007. pp 499–506
  • Liu J, Udupa JK, Saha PK, Odhner D, Hirsch BE, Siegler S, Simon S, Winkelstein BA. Rigid model-based 3D segmentation of the bones of joints in MR and CT images for motion analysis. Med Phys 2008; 35(8)3637–3649
  • Kainmüller D, Lamecker H, Zachow S, Hege H-C. An articulated statistical shape model for accurate hip joint segmentation. In: Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2009), Minneapolis, MN, September 2009. pp 6345–6351
  • Wu C, Murtha PE, Jaramaz B. Construction of statistical shape atlases for bone structures based on a two-level framework. Int J Med Robot Comput Assist Surg 2010; 6(1)1–17
  • Baldwin MA, Langenderfer JE, Rullkoetter PJ, Laz PJ. Development of subject-specific and statistical shape models of the knee using an efficient segmentation and mesh-morphing approach. Comput Methods Programs Biomed 2010; 97(3)232–240
  • Ramme AJ, Criswell AJ, Wolf BR, Magnotta VA, Grosland NM. EM segmentation of the distal femur and proximal tibia: A high-throughput approach to anatomic surface generation. Ann Biomed Eng 2011; 39(5)1555–1562
  • Schmid J, Kim J, Magnenat-Thalmann N. Robust statistical shape models for MRI bone segmentation in presence of small field of view. Med Image Anal 2011; 15(1)155–168
  • Skalli W, De Guise JA. A hierarchical statistical modeling approach for the unsupervised 3-D reconstruction of the scoliotic spine. IEEE Trans Biomed Eng 2005; 52: 2041–2057
  • Zheng G. Statistically deformable 2D/3D registration for estimating post-operative cup orientation from a single standard AP X-ray radiograph. Ann Biomed Eng 2010; 38(9)2910–2927
  • Zheng G, von Recum J, Nolte LP, Grützner PA, Steppacher SD, Franke J. Validation of a statistical shape model-based 2D/3D reconstruction method for determination of cup orientation after THA. Int J Comput Assist Radiol Surg 2011 Jul 27 [Epub ahead of print]
  • Xi J, Hu X, Jin Y. Shape analysis and parameterized modeling of hip joint. Trans ASME 2003; 3(9)260–265
  • Subburaj K, Ravi B, Agarwal M. Automated identification of anatomical landmarks on 3D bone models reconstructed from CT scan images. Comput Med Imaging Graph 2009; 33(5)359–368
  • Li K, Tashman S, Fu F, Harner C, Zhang X. Automating analyses of the distal femur articular geometry based on three-dimensional surface data. Ann Biomed Eng 2010; 38(9)2928–2936
  • Cerveri P, Marchente M, Bartels W, Corten K, Simon JP, Manzotti A. Automated method for computing the morphological and clinical parameters of the proximal femur using heuristic modeling techniques. Ann Biomed Eng 2010; 38(5)1752–1766
  • Cerveri P, Marchente M, Bartels W, Corten K, Simon JP, Manzotti A. Towards automatic computer-aided knee surgery by innovative methods for processing the femur surface model. Int J Med Robot Comput Assist Surg 2010; 6(3)350–361
  • Cerveri P, Marchente M, Manzotti A, Confalonieri N. Determination of the Whiteside line on the femur surface model by fitting high-order polynomial functions to the cross-section profiles of the intercondylar fossa. Comput Aided Surg 2011; 16(2)71–85
  • Oddy M, Jones M, Pendegrass C, Pilling J, Wimhurst J. Assessment of reproducibility and accuracy in templating hybrid total hip arthroplasty using digital radiographs. J Bone Joint Surg Br 2006; 88: 581–585
  • Yau WP, Leung A, Liu KG, Yan CH, Wong LL, Chiu KY. Interobserver and intra-observer errors in obtaining visually selected anatomical landmarks during registration process in non-image-based navigation-assisted total knee arthroplasty. J Arthroplasty 2007; 22(8)1150–1161
  • Morton NA, Maletsky LP, Pal S, Laz PJ. Effect of variability in anatomical landmark location on knee kinematic description. J Orthopaedic Res 2007; 25(9)1221–1230
  • Taddei F, Ansaloni M, Testi D, Viceconti M. Virtual palpation of skeletal landmarks with multimodal display interfaces. Med Inform Internet Med 2007; 32(3)191–198
  • Rohr K. Extraction of 3D anatomical point landmarks based on invariance principles. Pattern Recognition 1999; 32(1)3–15
  • Costa L da F, dos Reis SF, Arantes RAT, Alves ACR, Mutinari G. Biological shape analysis by digital curvature. Pattern Recognition 2004; 37: 515–524
  • Worz S, Rohr K. Localization of anatomical point landmarks in 3D medical images by fitting 3D parametric intensity models. Med Image Anal 2006; 10: 41–58
  • Subburaj K, Ravi B, Agarwal M. Automated identification of anatomical landmarks on 3D bone models reconstructed from CT scan images. Comput Med Imaging Graph 2009; 33(5)359–368
  • Koenderink JJ, Van Doorn AJ. Surface shape and curvature scales. Image and Vision Computing 1992; 10(8)557–565
  • Subburaj K, Ravi B, Agarwal M. Computer-aided methods for assessing lower limb deformities in orthopaedic surgery planning. Comput Med Imaging Graph 2010; 34(4)277–288
  • Zhang X, Li G, Xiong Y, He F, 3D mesh segmentation using mean-shifted curvature. In: Chen F, Jüttler B, editors. Proceedings of the 5th International Conference on Advances in Geometric Modeling and Processing (GMP ‘08), Hangzhou, China, April 2008. Lecture Notes in Computer Science 4975. Berlin: Springer-Verlag; 2008. pp 465–474
  • Cheng Y. Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Mach Intell 1995; 17(8)790–799
  • Comaniciu D, Meer P. Distribution free decomposition of multivariate data. Pattern Analysis and Applications 1999; 2: 22–30
  • Comaniciu D, Meer P. Mean shift: A robust approach towards feature space analysis. IEEE Trans Pattern Anal Mach Intell 2002; 24: 603–619
  • Shamir A, Shapira L, Cohen-Or D. Mesh analysis using geodesic mean-shift. The Visual Computer 2006; 22(2)99–108
  • Park M, Brocklehurst K, Collins RT, Liu Y. Deformed lattice detection in real-world images using mean-shift belief propagation. IEEE Trans Pattern Anal Mach Intell 2009; 31(10)1804–1816
  • Ye X, Beddoe G, Slabaugh G. Automatic graph cut segmentation of lesions in CT using mean shift superpixels. Int J Biomed Imaging 2010; 2010: 983963, (Epub)
  • Dong C, Wang G. Curvatures estimation on triangular mesh. Journal of Zhejiang University (SCIENCE) 2005; 6A(S1)128–136
  • Faugeras O. Three-Dimensional Computer Vision: A Geometric View-Point. MIT Press, Cambridge, MA 1993
  • Tao W, Jin H, Zhang Y. Color image segmentation based on mean shift and normalized cuts. IEEE Transactions on Systems, Man and Cybernetics, Part B 2007; 37(5)1382–1389
  • Yamauchi H, Gumhold S, Zayer R, Seidel H-P. Mesh segmentation driven by Gaussian curvature. The Visual Computer 2005; 21(8–10)659–668

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