Skip to main content
SpringerLink
Log in

Implementation of High-Dimensional Feature Map for Segmentation of MR Images

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

A method that considerably reduces the computational and memory complexities associated with the generation of high-dimensional (≥3) feature maps for image segmentation is described. The method is based on the K-nearest neighbor (KNN) classification and consists of two parts: preprocessing of feature space and fast KNN. This technique is implemented on a PC and applied for generating 3D and 4D feature maps for segmenting MR brain images of multiple sclerosis patients.

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. Amato, U., M. Larobina, A. Antoniadis, and B. Alfano. Segmentation of magnetic resonance brain images through discriminant analysis. J. Neurosci. Methods 131(1–2):65–74, 2003.

    Article  PubMed  Google Scholar 

  2. Anbeek, P., K. L. Vincken, M. J. P. van Osch, R. H. C. Bisschops, and J. Grond. Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage 21:1037–1044, 2004.

    Article  PubMed  Google Scholar 

  3. Ashburner, J. Another MRI bias correction approach. In: The 8th International Conference on Functional Mapping of the Human Brain, Sendai, Japan, June 2–6, 2002. Available on CD-Rom, also appeared in Neuroimage 16(Suppl. 2).

  4. Bedell, B. J., and P. A. Narayana. Volumetric analysis of white matter, gray matter, and CSF using fractional volume analysis. Magn. Reson. Med. 39:961–969, 1998.

    PubMed  Google Scholar 

  5. Bedell, B. J., P. A. Narayana, and J. S. Wolinsky. A dual approach for minimizing false lesions classifications on magnetic resonance images. Magn. Reson. Med. 37:94–102, 1997.

    PubMed  Google Scholar 

  6. Borgefors, G. Distance transformation in arbitrary dimensions. Comput. Vis. Graph. Image Process. 27:321–145, 1984.

    Google Scholar 

  7. Cárdenes, R., S. K. Warfield, E. Macìas, J. A. Santana, and J. Ruiz-Alzola. An efficient algorithm for multiple sclerosis lesion segmentation from brain MRI. In: Computer Aided Systems Theory—EUROCAST 2003, 9th International Workshop on Computer Aided Systems Theory, Las Palmas de Gran Canaria, Spain, February 24–28, 2003, Lecture Notes in Computer Science 2809 Springer, Springer-Verlag, Berlin, Hedelberg, edited by R. Moreno-Dìaz and F. Pichler. 2003, pp. 542– 551.

  8. Clarke, L. P., R. P. Velthuizen, S. Phuphanich, J. D. Schellenberg, J. A. Arrington, and M. Silbiger. MRI: Stability of three supervised segmentation techniques. Magn. Reson. Imaging 11:95–106, 1993.

    Article  PubMed  Google Scholar 

  9. Cline, H. E., W. E. Lorenson, R. Kikinis, and F. Jolesz. Three-dimensional segmentation of MR images of the head using probability and connectivity. J. Comput. Assist. Tomogr. 14(6):1037–1045, 1990.

    PubMed  Google Scholar 

  10. Cuisenaire, O. Distance transformations: Fast algorithms and applications to medical image processing, Thèse présentée en vue de l'obtention du grade de Docteur en Sciences Appliquées, UCL/TELE, Louvain-la-Neuve, October 1999.

  11. Cuisenaire, O., and B. Macq. Fast k-NN classification with an optimal k-distance transformation algorithm. In: Proceedings of the 10th European Signal Processing Conference (EUSIPCO), September 2000, pp. 1365–1368.

  12. Fukunaga, K., and P. Narendra. A branch and bound algorithm for k-nearest neighbors. IEEE Trans. Comput. 24:750–753, 1975.

    Google Scholar 

  13. Gerig, G., J. Martin, R. Kikinis, O. Kübler, M. Shenton, and F. Jolesz. Automatic segmentation of dual-echo MR head data. In: Proceedings of the 12th International Conference on Information Processing in Medicine Imaging, Wye, UK, July 1991. Berlin: Springer, 1991, pp. 175–187.

  14. He, R., and P. A. Narayana. Global optimization of mutual information: Application to three-dimensional retrospective registration of magnetic resonance images. Comp. Med. Imaging Graph. 26(4):277–292, 2002.

    Article  Google Scholar 

  15. The MathWorks, Inc., Image Processing Toolbox User's Guide: Objects, Regions, and Feature Measurement, http://www.mathworks.com/access/helpdesk/help/toolbox/images/morph15.html

  16. Research Systems, Inc., IDL-Interactive Data Language, http://www.rsinc.com/

  17. Jiang, Q., and W. Zhang. An improved method for finding nearest neighbors. Pattern Recognit. Lett. 14:531–535, 1993.

    Article  Google Scholar 

  18. Kamgar-Parsi, B., and L. N. Kanal. An improved branch and bound algorithm for computing k-nearest neighbours. Pattern Recognit. Lett. 3:7–12, 1985.

    Article  Google Scholar 

  19. Kim, B. S., and S. B. Park. A fast K nearest neighbor finding algorithm based on the ordered partition. IEEE Trans. PAMI 8:761–766, 1986.

    Google Scholar 

  20. Meier, D. S., and C. R. G. Guttmann. Time-series analysis of MRI intensity pattern in multiple sclerosis. Neuroimage 20:1193–1209, 2003.

    Article  PubMed  Google Scholar 

  21. Niemann, H., and R. Goppert. An efficient branch-and-bound nearest neighbor classifier. Pattern Recognit. Lett. 7:67–72, 1988.

    Article  Google Scholar 

  22. Nyul, L. G., J. K. Udupa, and X. Zhang. New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19(2):143–150, 2000.

    Article  PubMed  Google Scholar 

  23. Ragnelmam, I. The euclidean distance transformation in arbitrary dimensions. Pattern Recognit. Lett. 14:883–888, 1993.

    Article  Google Scholar 

  24. Romero, E., J.-M. Raymackers, B. Macq, and O. Cuisenaire. Automatic fibrosis quantification using a k-NN classificatory. In: 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2001), Istanbul, October 25–28, 2001.

  25. Vaidyanathan, R. P., L. P. Clarke, R. P. Velthuizen, S. Phuphanich, L. O. Hall, and J. C. Bezdek. Comparison of supervised MRI segmentation methods for tumor volume determination during therapy. Magn. Reson. Imaging 13(5):719–728, 1996.

    Article  Google Scholar 

  26. Warfield, S. K. Fast k-NN classification for multichannel image data. Pattern Recognit. Lett. 17:713–721, 1996.

    Article  Google Scholar 

  27. Weickert, J. Anisotropic Diffusion in Image Processing. Teubner: Stuttgart, 1998.

  28. Weisenfeld, N. I., and S. K. Warfield. Normalization of joint image-intensity statistics in MRI using the Kullback–Leibler divergence. Preprint, IEEE ISBI, 2004.

Download references

Author information

Authors and Affiliations

  1. Department of Diagnostic and Interventional Imaging, University of Texas Medical School at Houston, 6431 Fannin Street, Houston, 77030, TX

    Renjie He, Balasrinivasa Rao Sajja & Ponnada A. Narayana

Authors
  1. Renjie He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Balasrinivasa Rao Sajja
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ponnada A. Narayana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Renjie He.

Rights and permissions

Reprints and permissions

About this article

Cite this article

He, R., Sajja, B.R. & Narayana, P.A. Implementation of High-Dimensional Feature Map for Segmentation of MR Images. Ann Biomed Eng 33, 1439–1448 (2005). https://doi.org/10.1007/s10439-005-5888-3

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-005-5888-3

Keywords

Search

Navigation