Skip to main content

Advertisement

SpringerLink
Log in

Anatomical landmark localization in breast dynamic contrast-enhanced MR imaging

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

In this article, we present a novel approach to localize anatomical features—breast costal cartilage—in dynamic contrast-enhanced MRI using level sets. Current breast MRI diagnosis involves magnetic-resonance compatible needles for localization [12]. However, if the breast costal cartilage structure can be used as an alternative to the MR needle, this will not only assist in avoiding invasive procedures, but will also facilitate monitoring of the movement of breasts caused by cardiac and respiratory motion. This article represents a novel algorithm for achieving reliable detection and extraction of costal cartilage structures, which can be used for the analysis of motion artifacts, with possible shape variations of the structure caused by uptake of contrast agent, as well as a potential for the registration of breast. The algorithm represented in this article is to extract volume features from post-contrast MR images at three different time slices for the analysis of motion artifacts, and we validate the current algorithm according to the anatomic structure. This utilizes the level-set method [18] for the size selection of the region of interest. The variable shape of contours acquired from a level-set-based segment image actually determines the feature region of interest, which is used as a guide to achieve initial masks for feature extraction. Following this, the algorithm uses a K-means method for classification of the feature regions from other types of tissue and morphological operations with a choice of an appropriate structuring element to achieve reliable masks and extraction of features. The segments of features can be therefore obtained with the application of extracted masks for subsequent motion analysis of breast and for potential registration purposes.

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
Fig. 9

Similar content being viewed by others

References

  1. Antoine Maintz JB, Viergever MA (1992) A survey of medical image registration. Comput Surv 24(4):325–376

    Article  Google Scholar 

  2. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  PubMed  CAS  Google Scholar 

  3. Drake R, Vogl W, Mitchell A (2009) Gray’s Anatomy for Students, 2nd Ed. Elsevier, Australia

    Google Scholar 

  4. Frantz S, Rohr K, Stiehl HS (2005) Development and validation of a multi-step approach to improved detection of 3-D point landmarks in tomographic images. Image Vis Comput 23(11):956–971

    Article  Google Scholar 

  5. Guo YJ, Sivaramakrishna R, Lu CC, Suri JS, Laxminarayan S (2006) Breast image registration techniques: a survey. Med Biol Eng Comput 44(1-2):15–26

    Article  PubMed  Google Scholar 

  6. Hayton P, Brady M, Tarassenko L, Moore N (1997) Analysis of dynamic MR breast images using a model of contrast enhancement. Med Image Anal 1(3):207–224

    Article  PubMed  CAS  Google Scholar 

  7. Hendrick RE (2008) Breast MRI. Fundamentals and technical aspects. Springer, New York

    Google Scholar 

  8. Jonasson L, Hagmann P, Pollo C, Bresson X, Wilson CR, Meuli R, Thiran JP (2007) A level set method for segmentation of the thalamus and its nuclei in DT-MRI. Signal Process 87(2):309–321

    Article  Google Scholar 

  9. Lu WZ, Yao JH, Lu C, Prindiville S, Chow C (2006) DCE-MRI segmentation and motion correction based on active contour model and forward mapping. In: Seventh ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing, vol 19–20, Las Vegas, pp 208–212

  10. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematical statistics and probability, University of California Press, Berkeley, pp 281–297

  11. Malladi R, Sethian J, Vemuri B (1995) Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17(2):158–175

    Article  Google Scholar 

  12. Morris EA, Liberman L (2005) Breast MRI diagnosis and intervention. Springer, New York

    Google Scholar 

  13. Osher S, Sethian JA (1988) Fronts propagating with curvature-dependent speed: algorithms based on hamilton–jacobi formulations. J Comput Phys 79(1):12–49

    Article  Google Scholar 

  14. Paragios N, Deriche R (2002) Geodesic active regions: a new framework to deal with frame partition problems in computer vision. J Vis Commun Image Represent 13(1-2):249–268

    Article  Google Scholar 

  15. Serra J (1982) Image Analysis and Mathematical Morphology. Academic Press, London

    Google Scholar 

  16. Shechter G, Resar JR, McVeigh ER (2006) Displacement and velocity of the coronary arteries: cardiac and respiratory motion. IEEE Trans Med Imaging 25:369–375

    Article  PubMed  Google Scholar 

  17. Sinha S, Sinha U (2009) Recent advances in breast MRI and MRS. NMR Biomed 22(1):3–16

    Article  PubMed  CAS  Google Scholar 

  18. Zhang Y, Matuszewski BJ, Shark LK, Moore CJ (2008) Medical image segmentation using new hybrid level-set method. In: Proceeding of the fifth international conference on bioMedical visualization, Applied Digital Signal and Image Processing Research centre, pp 71–76

  19. Zhukov L, Museth K, Breen D, Whitakery R, Barr AH (2003) Level set modeling and segmentation of DT-MRI brain data. J Electron Imaging 12(1):125–133

    Article  Google Scholar 

Download references

Acknowlegement

This study was supported in part by the Australian Research Council (ARC) Discovery Project funding scheme—Project No. DP0988064

Author information

Authors and Affiliations

  1. Centre for Biomedical Engineering, School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, SA, 5005, Australia

    X. X. Yin, B. W.-H. Ng & D. Abbott

  2. Department of Computer Science and Software Engineering, The Melbourne School of Engineering, The University of Melbourne, Melbourne , VIC, 3010, Australia

    X. X. Yin & K. Ramamohanarao

  3. Apollo Medical Imaging Technology Pty. Ltd., North Melbourne, VIC, 3051, Australia

    Q. Yang

  4. Department of Anatomy and Cell Biology, The University of Melbourne and Sydney School of Medicine, The University of Notre Dame, Melbourne , Australia

    A. Pitman

Authors
  1. X. X. Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. W.-H. Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Q. Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Pitman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. K. Ramamohanarao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. D. Abbott
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to X. X. Yin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yin, X.X., Ng, B.WH., Yang, Q. et al. Anatomical landmark localization in breast dynamic contrast-enhanced MR imaging. Med Biol Eng Comput 50, 91–101 (2012). https://doi.org/10.1007/s11517-011-0772-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-011-0772-9

Keywords

Search

Navigation