Skip to main content

Advertisement

SpringerLink
Log in

Segmentation of the temporalis muscle from MR data

  • Original article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Objective A method for segmenting the temporalis from magnetic resonance (MR) images was developed and tested. The temporalis muscle is one of the muscles of mastication which plays a major role in the mastication system.

Materials and methods The temporalis region of interest (ROI) and the head ROI are defined in reference images, from which the spatial relationship between the two ROIs is derived. This relationship is used to define the temporalis ROI in a study image. Range-constrained thresholding is then employed to remove the fat, bone marrow and muscle tendon in the ROI. Adaptive morphological operations are then applied to first remove the brain tissue, followed by the removal of the other soft tissues surrounding the temporalis. Ten adult head MR data sets were processed to test this method.

Results Using five data sets each for training and testing, the method was applied to the segmentation of the temporalis in 25 MR images (five from each test set). An average overlap index (κ) of 90.2% was obtained. Applying a leave-one-out evaluation method, an average κ of 90.5% was obtained from 50 test images.

Conclusion A method for segmenting the temporalis from MR images was developed and tested on in vivo data sets. The results show that there is consistency between manual and automatic segmentations.

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

Similar content being viewed by others

References

  1. Meehan M, Teschner M, Girod S (2003) Three-dimensional simulation and prediction of craniofacial surgery. J Orthodon Craniofac Res 6(1):103–107

    Google Scholar 

  2. Xia J, Samman N, Yeung RW, Wang D, Shen SG, Ip HH, Tideman H (2000) Computer-assisted three-dimensional surgical planning and simulation. 3D soft tissue planning and prediction. Int J Oral Maxillofac Surg 29(4):250–258

    Article  PubMed  CAS  Google Scholar 

  3. Gladilin E, Zachow S, Deuflhard P, Hege HC (2004) Anatomy and physics-based facial animation for craniofacial surgery simulations. Med Biol Eng Comput 42(2):167–170

    Article  PubMed  CAS  Google Scholar 

  4. Ng HP, Ong SH, Hu QM, Foong KWC, Goh PS, Nowinski WL (2006) Muscles of mastication model-based MR image segmentation. Int J Comput Assis Radiol Surg 1(3):137–148

    Article  Google Scholar 

  5. Hu QM, Hou ZJ, Nowinski WL (2006) Supervised range- constrained thresholding. IEEE Trans Image Process 15(1):228–240

    Article  PubMed  Google Scholar 

  6. Qiao Y, Hu QM, Qian GY, Luo SH, Nowinski WL (2007) Thresholding based on variance and intensity contrast. Patt Recogn 40(2):596–608

    Article  Google Scholar 

  7. Vasilic B, Wehrli FW (2005) A novel local thresholding algorithm for trabecular bone volume fraction mapping in the limited spatial resolution regime of in vivo MRI. IEEE Trans Med Imaging 24(12):1574–1585

    Article  PubMed  Google Scholar 

  8. Shiffman S, Rubin GD, Napel S (2000) Medical image segmentation using analysis of isolable-contour maps. IEEE Trans Med Imaging 19(11):1064–1074

    Article  PubMed  CAS  Google Scholar 

  9. Ray N, Acton ST, Altes T, Lange EE, Brookeman JR (2003) Merging parametric active contours within homogeneous image regions for MRI-based lung segmentation. IEEE Trans Med Imaging 22(2):189–199

    Article  PubMed  Google Scholar 

  10. Pluempitiwiriyawej C, Moura JMF, Wu YJ, Ho C (2005) STACS: New active contour scheme for cardiac MR image segmentation. IEEE Trans Med Imaging 24(5):593–603

    Article  PubMed  Google Scholar 

  11. Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369

    Article  PubMed  CAS  Google Scholar 

  12. Shen S, Sandham W, Granat M, Sterr A (2005) MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Trans Inform Technol Biomed 9(3):459–467

    Article  Google Scholar 

  13. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  Google Scholar 

  14. Hu QM, Qian GY, Nowinski WL (2005) Fast connected-component labelling in three-dimensional binary images based on iterative recursion. Comput Visi Image Understand 99(3):414–434

    Article  Google Scholar 

  15. Leemput VK, Maes F, Vandermeulen D, Suetens P (1999) Automated model-based tissue classification of MR images of the brain. IEEE Trans on Med Imag 18(10):897–908

    Article  Google Scholar 

  16. Fukunaga K, Hummels DM (1989) Leave-one-out procedures for nonparametric error estimates. IEEE Trans Patt Anal Mach Intell 11(4):421–423

    Article  Google Scholar 

  17. Hu QM, Nowinski WL (2003) A rapid algorithm for robust and automatic extraction of the midsagittal plane of the human cerebrum from neuroimages based on local symmetry and outlier removal. NeuroImage 20(4):2153–2165

    Article  PubMed  Google Scholar 

  18. Hou ZJ, Hu QM, Nowinski WL (2006) On minimum variance thresholding. Patt Recogn Lett 27(14):1732–1743

    Article  Google Scholar 

  19. Suri JS, Liu K, Singh S, Laxminarayan SN, Zeng X, Reden L (2002) Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review. IEEE Trans Inform Technol Biomed 6(1):8–28

    Article  Google Scholar 

  20. Yang J, Staib LH, Duncan JS (2004) Neighbor-constrained segmentation with level set based 3-D deformable models. IEEE Trans Med Imaging 23(8):940–948

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. NUS Graduate School for Integrative Sciences and Engineering, Singapore, Singapore

    H. P. Ng & K. W. C. Foong

  2. Biomedical Imaging Lab, Agency for Science Technology and Research, Singapore, Singapore

    H. P. Ng, Q. M. Hu, J. Liu & W. L. Nowinski

  3. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    S. H. Ong

  4. Division of Bioengineering, National University of Singapore, Singapore, Singapore

    S. H. Ong

  5. Department of Preventive Dentistry, National University of Singapore, Singapore, Singapore

    K. W. C. Foong

  6. Department of Diagnostic Radiology, National University of Singapore, Singapore, Singapore

    P. S. Goh

Authors
  1. H. P. Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Q. M. Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. H. Ong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. K. W. C. Foong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. P. S. Goh
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. J. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. W. L. Nowinski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to H. P. Ng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ng, H.P., Hu, Q.M., Ong, S.H. et al. Segmentation of the temporalis muscle from MR data. Int J CARS 2, 19–30 (2007). https://doi.org/10.1007/s11548-007-0073-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-007-0073-9

Keywords

Search

Navigation