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Fully automatic extraction of human spine curve from MR images using methods of efficient intervertebral disk extraction and vertebra registration

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

A fully automatic method is proposed for extracting human spine curve which is required for gait modeling. By means of the gait modeling, origin of the gait pathology of patients could be found.

Methods

Our method is composed of two parts. The first part is the extraction of intervertebral disk positions where an efficient method is proposed. At the beginning of this part, all possible positions of intervertebral disks are located using a gradient-based method. Then, non-intervertebral disks are filtered out by a graph-based and an active shape model–based methods. In the second part, extracted disk positions are used by a vertebra registration method to segment spine vertebrae. Finally, spine curve is obtained by interpolating centers of segmented vertebrae using cubic spline.

Results

We tested our method with 13 MR data sets of patients. All disk positions of each MR data set were correctly extracted in the first part. The mean deviation of centers of segmented vertebrae that were obtained in the second part and used to interpolate spine curve was around 1.4 mm.

Conclusions

Our method achieves a fully automatic extraction of the spine curve. The extraction of intervertebral disk positions in the first part of our method when compared to model-based methods and manual selection which were proposed in other papers is highly efficient. In the second part including the vertebra registration, a new similarity measurement method, which is used to guide the vertebra atlas fitting process, is proposed to solve the problem of changes in overlap. Through our experiment, results of spine curves are at a highly accurate level.

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References

  1. Cootes TF, Hill A, Taylor CJ, Haslam J (1994) Use of active shape models for locating structure in medical images. Image Vis Comput 12: 355–365

    Article  Google Scholar 

  2. Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models—their training and application. Comput Vis Image Underst 61(1): 38–59

    Article  Google Scholar 

  3. Corso J, Alomari R, Chaudhary V (2008) Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. MICCAI. pp 202–210

  4. Huang S, Lai S, Novak CL (2008) A statistical learning approach to vertebra detection and segmentation from spinal MRI. In: IEEE International symposium on biomedical imaging: from nano to macro. pp 125–128

  5. Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16: 187–197

    Article  CAS  PubMed  Google Scholar 

  6. Michopoulou S, Costaridou L, Panagiotopoulos E, Speller R (2009) Atlas-based segmentation of degenerated lumbar intervertebral discs from MR images of the spine. IEEE Trans Biomed Eng 56: 2225–2231

    PubMed  Google Scholar 

  7. Peng Z, Zhong J, Wee W, Lee J (2005) Automated vertebra detection and segmentation from the whole spine MR images. In: IEEE conference on engineering in medicine and biology. pp 2527–2530

  8. Roche A, Malandain G, Pennec X, Ayache N (1998) The correlation ratio as a new similarity measure for multimodal image registration. In: Proceedings MICCAI, 1496. pp 1115–1124

  9. Rosenthal D, Kecskemethy A (2009) Visualization of the motion of the human spine in the context of gait analysis through splines. In: Proceedings of the 80th annual meeting of the international association of applied mathematics and mechanics. pp 9–13

  10. Schmidt S, Kappes J, Bergtholdt M, Pekar V, Dries S, Bystrov D, Schnoerr C (2007) Spine detection and labeling using a parts-based graphical model. In: Information Processing in Medical Imaging. Springer, pp 122–133

  11. Shi R, Sun D, Qiu Z, Weiss KL (2007) An efficient method for segmentation of MRI spine images. In: IEEE international conference on complex medical engineering. pp 713–717

  12. Studholme C, Hill DLG, Hawkes DJ (1999) An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognit 32: 71–86

    Article  Google Scholar 

  13. Taendl M, Stark T, Erol NE, Kecskemethy A (2008) An intergrated simulation environment for human gait analysis and evaluation. In: Proceedings of the 10th international symposium biomaterials. pp 43–53

  14. Yao J, O’Connor SD, Summers RM (2006) Automated spinal column extraction and partitioning. In: IEEE international symposium on biomedical imaging: nano to macro. pp 390–393

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Correspondence to Zhenyu Tang.

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Tang, Z., Pauli, J. Fully automatic extraction of human spine curve from MR images using methods of efficient intervertebral disk extraction and vertebra registration. Int J CARS 6, 21–33 (2011). https://doi.org/10.1007/s11548-010-0427-6

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  • DOI: https://doi.org/10.1007/s11548-010-0427-6

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