CFP last date
20 January 2025
Reseach Article

Frame Work for Auto Segmentation of Fibrocartilaginous Disc of Scoliosis Affected Spine

by B. R. Benujah, X. Jushwanth Xavier, S. S.uma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 5
Year of Publication: 2013
Authors: B. R. Benujah, X. Jushwanth Xavier, S. S.uma
10.5120/10918-5851

B. R. Benujah, X. Jushwanth Xavier, S. S.uma . Frame Work for Auto Segmentation of Fibrocartilaginous Disc of Scoliosis Affected Spine. International Journal of Computer Applications. 65, 5 ( March 2013), 7-11. DOI=10.5120/10918-5851

@article{ 10.5120/10918-5851,
author = { B. R. Benujah, X. Jushwanth Xavier, S. S.uma },
title = { Frame Work for Auto Segmentation of Fibrocartilaginous Disc of Scoliosis Affected Spine },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 5 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume65/number5/10918-5851/ },
doi = { 10.5120/10918-5851 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:17:50.073245+05:30
%A B. R. Benujah
%A X. Jushwanth Xavier
%A S. S.uma
%T Frame Work for Auto Segmentation of Fibrocartilaginous Disc of Scoliosis Affected Spine
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 5
%P 7-11
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A framework for automatic segmentation of fibrocartilaginous disc of scoliosis affected spine from MRI image is presented in this paper. This method uses a combination of statistical and spectral texture features for discriminating closed regions representing fibrocartilaginous disc from background in MR images of the spine. Texture features are extracted from the closed regions based on the watershed approach. The feature selection step is based on principal component analysis and clustering process. It permits to decide among all the extracted features which ones resulted in the highest rate of good classification. With the help of the selected texture features and classification, the problem of over-segmentation underlying in existing automatic segmentation methods can be solved successfully by discriminating fibrocartilaginous disc from the background on MRI of scoliotic spines.

References
  1. A. L. Martel, O. Heid, M. Slomczykowski, R. Kerslake, and L. P. Nolte, "Assessment of 3-dimensional magnetic resonance imaging fast low angle shot images for computer assisted spinal surgery," Comput. Aided Surg. , vol. 3, pp. 40–44, 1998.
  2. C. L. Hoad, A. L. Martel, R. Kerslake, and M. Grevitt, "A 3D MRI sequence for computer assisted surgery of the lumbar spine," Phys. Med. Biol. , vol. 46, no. 8, pp. 213–220, 2001.
  3. B. A. Georgy and J. R. Hesselink, "MR imaging of the spine: recent advances in pulse sequences and special techniques," AJR Amer. J. Roentgenol. , vol. 162, pp. 923–934, 1994.
  4. Z. Peng, J. Zhong, W. Wee, and J. H. Lee, "Automated vertebra detection and segmentation from the whole spine MR images," in Conf. Proc. IEEE Eng. Med. Biol. Soc. , 2005, vol. 3, pp. 2527–2530.
  5. J. Carballido-Gamio, S. J. Belongie, and S. Majumdar, "Normalized cuts in 3-D for spinal MRI segmentation," IEEE Trans. Med. Imag. , vol. 23, no. 1, pp. 36–44, Jan. 2004.
  6. S. Booth and D. A. Clausi, "Image segmentation using MRI vertebral cross-sections," in Proc. Can. Conf. Electr. Comput. Eng. , May 13–16, 2001, pp. 1303–1308.
  7. P. Dokladal, I. Bloch, M. Couprie, D. Ruijters, R. Urtasun, and L. Garnero, "Topologically controlled segmentation of 3Dmagnetic resonance images of the head by using morphological operators," Pattern Recog. , vol. 36, no. 10, pp. 2463–2478, 2003.
  8. C. Chevrefils, F. Cheriet, G. Grimard, and C. -E. Aubin, "Watershed segmentation of intervertebral disk and spinal canal from MRI images," in Proc. Image Anal. Recog. 4th Int. Conf. , ICIAR'07, 22–24, Aug. , pp. 1017– 1027.
  9. T. Hurtut and F. Cheriet, "Automatic closed edge detection using level lines selection," in Proc. Image Anal. Recog. 4th Int. Conf. , ICIAR'07,, 22–24 Aug. , pp. 187–197.
  10. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Syst. , Man, Cybern. , vol. SMC-9, no. 1, pp. 62–66, Jan. 1979.
  11. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using Matlab. Englewood Cliffs, NJ: Prentice-Hall, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Scoliosis classification over-segmentation segmentation fibrocartilaginous disc