Skip to main content
SpringerLink
Log in

Graph-Cut-Based Segmentation of Proximal Femur from Computed Tomography Images with Shape Prior

  • Original Article
  • Published:
Journal of Medical and Biological Engineering Aims and scope Submit manuscript

Abstract

Femur segmentation from computed tomography (CT) images is a fundamental problem in femur-related computer-assisted diagnosis and surgical planning/navigation. In this study, an automatic approach for the segmentation of proximal femur from CT images that incorporates the statistical shape prior into the graph-cut framework (SP-GC) is proposed. The proposed segmentation framework includes two major processes, namely training and segmentation. In the training stage, a training set of three-dimensional CT images from a group of normal subjects were segmented semi-automatically. The shape prior was generated by active shape modeling that included mean shape and shape variance information. In the segmentation stage, two shape terms originated from the training stage were included in the GC energy function. The minimization of the energy function was achieved using a max-flow/min-cut algorithm. The performance of the proposed segmentation method was evaluated by testing on 60 CT datasets from bilateral femurs of 30 normal subjects. Qualitative and quantitative analyses of the segmentation results of the proposed method were performed and the results were compared with two widely used methods, namely the active shape model (ASM) and traditional GC, with results from manual delineation used as the ground truth. The mean dice similarity coefficient of the proposed SP-GC was 0.9600, which is higher than those of ASM and GC (0.8769 and 0.9358, respectively). The mean normalized error rate of the SP-GC results was 10 and 6 % lower than those of ASM and GC, respectively. In terms of the average surface distance measurement, the value for SP-GC was 0.885 mm, compared with 2.148 and 1.154 mm for ASM and GC, respectively. In comparison with ASM, SP-GC has superior performance given a small training set (e.g., n = 12). With increasing number of training samples, the segmentation accuracy of ASM saturated, but that of SP-GC slightly increased.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Ganz, R., Parvizi, J., Beck, M., Leunig, M., Nötzli, H., & Siebenrock, K. A. (2003). Femoroacetabular impingement: a cause for osteoarthritis of the hip. Clinical Orthopaedics and Related Research, 417, 112–120.

    Google Scholar 

  2. Ganz, R., Leunig, M., Leunig-Ganz, K., & Harris, W. H. (2008). The etiology of osteoarthritis of the hip. Clinical Orthopaedics and Related Research, 466, 264–272.

    Article  Google Scholar 

  3. Beaulé, P. E., Zaragoza, E., Motamedi, K., Copelan, N., & Dorey, F. J. (2005). Three-dimensional computed tomography of the hip in the assessment of femoroacetabular impingement. Journal of Orthopaedic Research, 23, 1286–1292.

    Article  Google Scholar 

  4. Clohisy, J. C., Carlisle, J. C., Beaulé, P. E., Kim, Y. J., Trousdale, R. T., Sierra, R. J., et al. (2008). A systematic approach to the plain radiographic evaluation of the young adult hip. Journal of Bone Joint Surgery, 90, 47–66.

    Article  Google Scholar 

  5. Rakhra, K. S., Sheikh, A. M., Allen, D., & Beaulé, P. E. (2009). Comparison of MRI alpha angle measurement planes in femoroacetabular impingement. Clinical Orthopaedics and Related Research, 467, 660–665.

    Article  Google Scholar 

  6. Harris, M. D., Datar, M., Whitaker, R. T., Jurrus, E. R., Peters, C. L., & Anderson, A. E. (2013). Statistical shape modeling of cam femoroacetabular impingement. Journal of Orthopaedic Research, 31, 1620–1626.

    Article  Google Scholar 

  7. Raut, S., Raghuvanshi, M., Dharaskar, R., & Raut, A. (2009). Image segmentation-a state-of-art survey for prediction. Proceedings IEEE International Conference Advanced Computer Control (pp. 420–424).

  8. Naz, S., Majeed, H., & Irshad, H. (2010). Image segmentation using fuzzy clustering: A survey. Proceedings IEEE International Conference Emerging Technologies, (pp. 181–186).

  9. Du, M., Ding, Y., & Jia, Q. (2013). A multi-threshold segmentation method based on ant colony algorithm. 5th International Conference Machine Vision (pp. 878402–878409).

  10. Xiao, B., Jing, Y., & Guan, Y. (2013). A novel automatic thresholding segmentation method with local adaptive thresholds. arXiv preprint, arXiv: 1305.5160.

  11. Yi, F., & Moon, I. (2012). Image segmentation: A survey of graph-cut methods. Proceedings IEEE International Conference Systems and Informatics (pp. 1936–1941).

  12. Felzenszwalb, P. F., & Huttenlocher, D. P. (2004). Efficient graph-based image segmentation. Journal of Computer Vision, 59, 167–181.

    Article  Google Scholar 

  13. Sundaramoorthi, G., Yezzi, A., & Mennucci, A. C. (2008). Coarse-to-fine segmentation and tracking using sobolev active contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 851–864.

    Article  Google Scholar 

  14. Willett, R. M., & Nowak, R. D. (2007). Minimax optimal level-set estimation. IEEE Transactions on Image Processing, 16, 2965–2979.

    Article  MathSciNet  Google Scholar 

  15. Falcão, A. X., Udupa, J. K., & Miyazawa, F. K. (2000). An ultra-fast user-steered image segmentation paradigm: Live wire on the fly. IEEE Transactions on Medical Imaging, 19, 55–62.

    Article  Google Scholar 

  16. Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995). Active shape models-their training and application. Computer Vision and Image Understanding, 61, 38–59.

    Article  Google Scholar 

  17. Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001). Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 681–685.

    Article  Google Scholar 

  18. Yokota, F., Okada, T., Takao, M., Sugano, N., Tada, Y., & Tomiyama, N. et al. (2013). Automated CT segmentation of diseased hip using hierarchical and conditional statistical shape models. International Conference Medical Image Computing and Computer-Assisted Intervention (pp. 190–197).

  19. Wang, D., Shi, L., Chu, W. C., Cheng, J. C., & Heng, P. A. (2009). Segmentation of human skull in MRI using statistical shape information from CT data. Journal of Magnetic Resonance Imaging, 30, 490–498.

    Article  Google Scholar 

  20. Boykov, Y. Y., & Kolmogorov, V. (2004). An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 1124–1137.

    Article  Google Scholar 

  21. Krcah, M., Székely, G., & Blanc, R. (2011). Fully automatic and fast segmentation of the femur bone from 3D-CT images with no shape prior. Proceedings IEEE International Conference Biomedical Imaging: From Nano to Macro (pp. 2087–2090).

  22. Freedman, D., & Zhang, T. (2005). Interactive graph cut based segmentation with shape priors. Proceedings IEEE International Conference Computer Vision and Pattern Recognition, 1, 755–762.

    Google Scholar 

  23. Zoroofi, R. A., Sato, Y., Sasama, T., Nishii, T., Sugano, N., Yonenobu, K., et al. (2003). Automated segmentation of acetabulum and femoral head from 3-D CT images. IEEE Transactions on Information Technology in Biomedicine, 7, 329–343.

    Article  Google Scholar 

  24. Cheng, Y., Zhou, S., Wang, Y., Guo, C., Bai, J., & Tamura, S. (2013). Automatic segmentation technique for acetabulum and femoral head in CT images. Pattern Recognition, 46, 2969–2984.

    Article  Google Scholar 

  25. O’Neill, G. T., Lee, W. S., & Beaulé, P. (2012). Segmentation of cam-type femurs from CT scans. The Vision Computing, 28, 205–218.

    Article  Google Scholar 

  26. Nakagomi, K., Shimizu, A., Kobatake, H., Yakami, M., Fujimoto, K., & Togashi, K. (2013). Multi-shape graph cuts with neighbor prior constraints and its application to lung segmentation from a chest CT volume. Medical Image Analysis, 17, 62–77.

    Article  Google Scholar 

  27. Essa, E., Xie, X., Sazonov, I., Nithiarasu, P., & Smith, D. (2013). Shape prior model for media-adventitia border segmentation in IVUS using graph cut. Medical Computer Vision, 7766, 114–123.

    Google Scholar 

  28. Xiong, W., Li, A. L., Ong, S. H., & Sun, Y. (2013) Automatic 3D prostate MR image segmentation using graph cuts and level sets with shape prior. International Conference Advances in Multimedia Information Processing (pp. 211–220).

  29. Wang, H., Zhang, H., & Ray, N. (2013). Adaptive shape prior in graph cut image segmentation. Pattern Recognition, 46, 1409–1414.

    Article  Google Scholar 

  30. Chen, X., Udupa, J. K., Alavi, A., & Torigian, D. A. (2013). GC-ASM: Synergistic integration of graph-cut and active shape model strategies for medical image segmentation. Computer Vision and Image Understanding, 117, 513–524.

    Article  Google Scholar 

  31. Belongie, S., Malik, J., & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 509–522.

    Article  Google Scholar 

  32. Heinonen, T., Eskola, H., Dastidar, P., Laarne, P., & Malmivuo, J. (1997). Segmentation of T1 MR scans for reconstruction of resistive head models. Computer Methods and Programs in Biomedicine, 54, 173–181.

    Article  Google Scholar 

  33. Gonzalez, G. C., & Woods, R. E. (1992). Digital imaging processing. Cambridge, MA: Wesley.

    Google Scholar 

  34. Boykov, Y. Y., & Jolly, M. P. (2001). Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. Proceedings IEEE International Conference Computer Vision, 1, 105–112.

    Google Scholar 

  35. Lorensen, W. E., & Cline, H. E. (1987). Marching cubes: A high resolution 3D surface construction algorithm. International Conference ACM Siggraph Computer Graphics, 21, 163–169.

    Article  Google Scholar 

  36. van Ginneken, B., Frangi, A. F., Staal, J. J., ter Haar Romeny, B. M., & Viergever, M. A. (2002). Active shape model segmentation with optimal features. IEEE Transactions on Medical Imaging, 21, 924–933.

    Article  Google Scholar 

  37. Golub, G. H., & Reinsch, C. (1970). Singular value decomposition and least squares solutions. Numerische Mathematik, 14, 403–420.

    Article  MATH  MathSciNet  Google Scholar 

  38. Besl, P. J., & McKay, N. D. (1992). Method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 239–256.

    Article  Google Scholar 

  39. Diederichs, G., Seim, H., Meyer, H., Issever, A. S., Link, T. M., Schröder, R. J., & Scheibel, M. (2008). CT-based patient-specific modeling of glenoid rim defects: a feasibility study. American Journal of Roentgenology, 191, 1406–1411.

    Article  Google Scholar 

  40. Shi, L., Wang, D., Chu, W. C., Burwell, G. R., Wong, T. T., Heng, P. A., & Cheng, J. C. (2011). Automatic MRI segmentation and morphoanatomy analysis of the vestibular system in adolescent idiopathic scoliosis. Neuroimage, 54, S180–S188.

    Article  Google Scholar 

  41. Kang, Y., Engelke, K., & Kalender, W. A. (2003). A new accurate and precise 3-D segmentation method for skeletal structures in volumetric CT data. IEEE Transactions on Medical Imaging, 22, 586–598.

    Article  Google Scholar 

  42. Lempitsky, V., Kohli, P., Rother, C., & Sharp, T. (2009). Image segmentation with a bounding box prior. Proceedings IEEE International Conference Computer Vision (pp. 277–284).

  43. Peng, Y., & Liu, R. (2010). Object segmentation based on watershed and graph cut. Proceedings IEEE International Conference Image and Signal Processing, 3, 1431–1435.

    Google Scholar 

  44. Slabaugh, G., & Unal, G. (2005). Graph cuts segmentation using an elliptical shape prior. Proceedings IEEE International Conference Image Processing, 2, 1222–1225.

    Google Scholar 

Download references

Acknowledgments

This work was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No.: CUHK 473012).

Author information

Authors and Affiliations

  1. Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, 999077, China

    Junbin Huang, James F. Griffith & Defeng Wang

  2. Research Center for Medical Image Computing, The Chinese University of Hong Kong, Hong Kong SAR, 999077, China

    Junbin Huang & Defeng Wang

  3. Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, 999077, China

    Lin Shi

  4. Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Hong Kong SAR, 999077, China

    Lin Shi

Authors
  1. Junbin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James F. Griffith
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Defeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lin Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lin Shi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, J., Griffith, J.F., Wang, D. et al. Graph-Cut-Based Segmentation of Proximal Femur from Computed Tomography Images with Shape Prior. J. Med. Biol. Eng. 35, 594–607 (2015). https://doi.org/10.1007/s40846-015-0079-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40846-015-0079-7

Keywords

Search

Navigation