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Tumor Extraction by Level Set Method using Thershold Algorithm

by Visu Prateek Agarwal, Manoj Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 15
Year of Publication: 2014
Authors: Visu Prateek Agarwal, Manoj Kumar
10.5120/16422-6074

Visu Prateek Agarwal, Manoj Kumar . Tumor Extraction by Level Set Method using Thershold Algorithm. International Journal of Computer Applications. 94, 15 ( May 2014), 38-41. DOI=10.5120/16422-6074

@article{ 10.5120/16422-6074,
author = { Visu Prateek Agarwal, Manoj Kumar },
title = { Tumor Extraction by Level Set Method using Thershold Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 15 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 38-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number15/16422-6074/ },
doi = { 10.5120/16422-6074 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:17:47.026396+05:30
%A Visu Prateek Agarwal
%A Manoj Kumar
%T Tumor Extraction by Level Set Method using Thershold Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 15
%P 38-41
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The level set method may be used as a strongest pawn for segmentation of a tumor to achieve an accurate estimation of its volume. The level set method is a numeric technique for tracking interfaces and shapes. In this method, equation parameters are being set or speed function is being set. A threshold based method is introduced for tumor segmentation. In this paper, tumor segmentation and its extraction is achieved by a threshold based scheme and by utilizing a global threshold, the level set speed function is designed. This threshold based scheme provides better flexibility and it is updated through the whole process. Search based and adaptive bases threshold can be used here for better efficiency through segmentation. Tumor segmentation does not need any vast knowledge about the tumor and non-tumor density function. Depending upon the tumor shape and size, it may be implemented in an automatic or semi-automatic form. Here we use this algorithm for magnetic resonance images (MRI). We see that the performance can be evaluated accurately for quantitatively images. The results from this experiment provide better efficiency and high performance.

References
  1. S. Saini, Radiologic measurement of tumor size in clinical trials: past, present, and future, American Journal of Roentgenol 176 (2) (2001) 333–334.
  2. A. Sorensen, S. Patel, C. Harmath, et al. , Comparison of diameter and perimeter methods for tumor volume calculation, Journal of Clinical Oncology 19 (2) (2001) 551–557.
  3. J. Zhou, T. Lim, V. Chong, J. Huang, Segmentation and visualization of nasopharyngeal carcinoma using MRI, Computers in Biology and Medicine 33 (5) (2003) 407–424.
  4. G. Moonis, J. Liu, J. Udupa, D. Hackney, Estimation of tumor volume with fuzzy connectedness Segmentation of MR images, AJNR Am J Neuroradiol 23 (3) (2002) 356–363.
  5. J. Liu, J. Udupa, D. Odhner, D. Hackney, G. Moonis, A system for brain tumor volume estimation via mr imaging and fuzzy connectedness, Computerized Medical Imaging and Graphics 29 (1) (2005) 21–34.
  6. K. Held, E. Kops, B. Krause, W. Wells, R. Kikinis, H. Muller-Gartner, Markov random field segmentation of brain mr images, IEEE Transactions on Medical Imaging 16 (1997) 878–886.
  7. Y. Zhang, M. Brady, S. Smith, Segmentation of brain mr images through a hiddenmarkov random field model and the expectation–maximization algorithm, IEEE Transactions on Medical Imaging 20 (2001) 45–57.
  8. C. Lee, M. Schmidt, A. Murtha, A. Bistritz, J. Sander, R. Greiner, Segmenting brain tumor with conditional random fields and support vector machines, in: Proceedings of Workshop on Computer Vision for Biomedical Image Applications at International Conference on Computer Vision, 2005, pp. 469– 478.
  9. J. Corso, A. Yuille, N. Sicotte, A. Toga, Detection and segmentation of pathological structures by the extended graph-shifts algorithm, in: Proceedings of Medical Image Computing and Computer Aided Intervention (MICCAI), vol. 1, 2007, pp. 985–994.
  10. J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha, A. Yuille, Efficient multilevel brain tumor segmentation with integrated bayesian model classification, IEEE Transactions on Medical Imaging 27 (5) (2008) 629–640
  11. J. A. Sethian, Level Set Methods and Fast Marching Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision, and Materials Science, second ed. , Cambridge University Press, 1999.
  12. S. Osher, and J. A. Sethian, "Fronts propagating with curvature-dependent speed: algorithms based on Harmilton-Jacobi formulations," Journal of Computational Physics, vol. 79, pp. 12-49, 1988
  13. V. Caselles, R. Kimmel, and G. Sapiro, "On geodesic active contours," International Journal of Computer Vision, vol. 22, no. 1, pp. 61-79,1997
  14. A. Yezzi, Jr. , S. Kichenassamy, P. Olver, A. Kumar and A. Tannenbaum, "A Geometric Snake Model for Segmentation of Medical Imagery," IEEE Transactions on Medical Imaging, vol. 16, no. 2, pp. 199-209, 1997
  15. W. J. Niessen, B. M. ter Haar Romeny, and M. A. Viergever, "Geodesic Deformable Model for Medical Image Analysis," IEEE Transactions on Medical Imaging, vol. 17, no. 4, PPpp. 634-641,1998
  16. X. Zeng, L. H. Staib, R. T. Schultz, and J. S. Duncan, "Segmentation and Measurement of the Cortex from 3-D MR Images Using Coupled-surfaces," IEEE Transactions on Medical Imaging, vol. 18, pp. 927-937,1997
  17. M. Droske, B. Meyer, M. Rumpf, C. Schaller, An adaptive level set method for medical image segmentation, in: Proceedings of the Annual Symposium on Information Processing in Medical Imaging, 2001.
  18. S. Ho, H. Cody, G. Gerig, Snap: A software package for user-guided geodesic snake segmentation, Tech. rep. , University of North Carolina, Chapel Hill (April 2003).
  19. Z. Zhou, J. You, P. Heng, D. Xia, Cardiacmr image segmentation and left ventricle surface reconstruction based on level set method, in: Studies in Health Technology and Informatics, 2005, pp. 629–632.
  20. Q. Su, K. -Y. K. Wong, G. S. K. Fung, A semi-automatic clustering-based level set method for segmentation of endocardium from msct images, in: Proceedings 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 2007, pp. 6023–6026.
  21. H. Khotanlou, J. Atif, O. Colliot, I. Bloch, 3d brain tumor segmentation using fuzzy classification and deformable models, in: Int. Workshop on Fuzzy Logic and Applications, 2005, pp. 312–318.
  22. S. Ho, E. Bullitt, G. Gerig, Levelset evolution with region competition: automatic 3d segmentation of brain tumors, in: Proc. of Int'l Conf. On Pattern Recognition, 2002, pp. 532–535.
  23. M. Prastawa, E. Bullitt, S. Ho, G. Gerig, A brain tumor segmentation framework based on outlier detection, Medical Image Analysis 8 (3) (2004) 275–283.
  24. S. Taheri, S. Ong, V. Chong, Threshold-based 3d tumor segmentation using level set (tsl), in: Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision, 2007.
  25. R. Malladi, J. A. Sethian, B. C. Vemuri, Shapemodeling with front propagation: a level set approach, IEEE Trans. on Pattern Analysis and Machine Intelligence 17 (2) (1995) 158–175.
  26. D. Adalsteinsson, J. Sethian, A fast level set method for propagating interfaces, Journal of Computational Physics 118 (2) (1995) 269–277.
  27. B. Ostle, L. Malone, Statistics in Research: Basic Concepts and Techniques for Research Workers, 4th ed. , Iowa State University Press, 1988.
  28. S. J. Osher, R. P. Fedkiw, Level Set Methods and Dynamic Implicit Surfaces,Springer,2003.
Index Terms

Computer Science
Information Sciences

Keywords

Tumor Extraction

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