Automatic medical image segmentation by integrating kfcm clustering and level set based FTC model

IAENG Transactions on Electrical Engineering Volume 1: Special Issue of the International Multiconference of Engineers and Computer Scientists 2012

Volumn , Issue , 2012, Pages 257-270

  Rastgarpour, M ^a   Shanbehzadeh, J ^b  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b KHARAZMI UNIVERSITY   (Iran)

Author keywords

[No Author keywords available]

Indexed keywords

EFFICIENCY; IMAGE SEGMENTATION; MEDICAL IMAGING; TOPOLOGY;

CURVE EVOLUTION; FUZZY MEMBERSHIP; GEOMETRIC DEFORMABLE MODELS; HIGH-EFFICIENCY; INITIAL CONTOUR; ITS EFFICIENCIES; MODEL SEGMENT; TOPOLOGICAL CHANGES;

MEDICAL IMAGE PROCESSING;

EID: 84973462523     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1142/9789814439084_0020     Document Type: Chapter

References (33)

1. Isgum, I., et al., Multi-atlas-based segmentation with local decision fusion—Application to cardiac and aortic segmentation in CT scans. Medical Imaging, IEEE Transactions on, 2009. 28(7): p. 1000-1010.
2. Rastgarpour, M. and J. Shanbehzadeh. Novel Classification of Current Methods, Available Softwares and Datasets in Medical Image Segmentation. in The 2011 World Congress in Computer Science, Computer Engineering, and Applied Computing (WORLDCOMP'11). 2011. LV, USA.
3. Gonzalez, R.C. and E. Richard, Woods, digital image processing. 2002, Prentice Hall Press, ISBN 0-201-18075-8.
4. Rastgarpour, M. and J. Shanbehzadeh, The Status Quo of Artificial Intelligence Methods in Automatic Medical Image Segmentation. International Journal of Computer Theory and Engineering (IJCTE) 2012: p.-in press.
5. Wang, S. and R.M. Summers, Machine Learning and Radiology. Medical Image Analysis, 2012. 16 (5): p. 933-951.
6. Rastgarpour, M. and J. Shanbehzadeh, Application of AI Techniques in Medical Image Segmentation and Novel Categorization of Available Methods and Tools. Lecture Notes in Engineering and Computer Science: Proceedings of The International MultiConference of Engineers and Computer Scientists 2011, IMECS 2011, 2011. 2188: p. 519-523.
7. Rastgarpour, M. and J. Shanbehzadeh, The Problems, Applications and Growing Interest in Automatic Segmentation of Medical Images from the year 2000 till 2011. International Journal of Computer Theory and Engineering (IJCTE) 2012: p.-in press.
8. Zhang, Y.J., Advances in image and video segmentation. 2006: Irm Pr.
9. Wu, Y., W. Hou, and S. Wu, Brain MRI segmentation using KFCM and Chan-Vese model. Transactions of Tianjin University, Springer, 2011. 17(3): p. 215-219.
10. Zhang, D.Q., et al. Kernel-based fuzzy clustering incorporating spatial constraints for image segmentation. in Proc. of the 2th Int. Conf. on Mach. Lear. and Cyb. 2003: IEEE.
11. Zhang, D.Q. and S.C. Chen, A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artificial Intelligence in Medicine, 2004. 32(1): p. 37-50.
12. Chan, T.F. and L.A. Vese, Active contours without edges. Image Processing, IEEE Transactions on, 2001. 10(2): p. 266-277.
13. Reddy, G., et al. Image segmentation using kernel fuzzy c-means clustering on level set method on noisy images. in International Conference on Communications and Signal Processing (ICCSP). 2011: IEEE.
14. Li, C., et al. Level set evolution without re-initialization: a new variational formulation. in Computer Vision and Pattern Recognition (CVPR) IEEE Computer Society Conference on. 2005.
15. Osher, S. and J.A. Sethian, Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of computational physics, 1988. 79(1): p. 12-49.
16. Caselles, V., et al., A geometric model for active contours in image processing. Numerische Mathematik, 1993. 66(1): p. 1-31.
17. Malladi, R., J.A. Sethian, and B.C. Vemuri, Shape modeling with front propagation: A level set approach. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1995. 17(2): p. 158-175.
18. Suri, J.S., et al., Shape recovery algorithms using level sets in 2-D/3-D medical imagery: A state-of-the-art review. Information Technology in Biomedicine, IEEE Transactions on, 2002. 6(1): p. 8-28.
19. Caselles, V., R. Kimmel, and G. Sapiro, Geodesic active contours. INTERNATIONAL JOURNAL OF COMPUTER VISION, 1997. 22(1): p. 61-79.
20. Bernard, O., et al., Variational B-spline level-set: a linear filtering approach for fast deformable model evolution. Image Processing, IEEE Transactions on, 2009. 18(6): p. 1179-1191.
21. Li, C., et al., Minimization of region-scalable fitting energy for image segmentation. Image Processing, IEEE Transactions on, 2008. 17(10): p. 1940-1949.
22. Lankton, S. and A. Tannenbaum, Localizing region-based active contours. Image Processing, IEEE Transactions on, 2008. 17(11): p. 2029-2039.
23. Shi, Y. and W.C. Karl, A real-time algorithm for the approximation of levelset-based curve evolution. Image Processing, IEEE Transactions on, 2008. 17(5): p. 645-656.
24. Dice, L.R., Measures of the amount of ecologic association between species. Ecology, 1945. 26(3): p. 297-302.
25. Dietenbeck, T., et al. CREASEG: a free software for the evaluation of image segmentation algorithms based on level-set. in Image Processing (ICIP), 17th IEEE Int. Conf. on2010. Hong Kong: IEEE.
26. Rastgarpour, M., S. Alipour, and J. Shanbehzadeh, Improved Fast Two Cycle by using KFCM Clustering for Image Segmentation. Lecture Notes in Engineering and Computer Science: Proceedings of The International MultiConference of Engineers and Computer Scientists 2012, IMECS 2012, 2012. 1: p. 678-682.
27. Muller, K.R., et al., An introduction to kernel-based learning algorithms. Neural Networks, IEEE Transactions on, 2001. 12(2): p. 181-201.
28. Merriman, B., J.K. Bence, and S. Osher, Diffusion generated motion by mean curvature. 1992: Dept. of Mathematics, University of California, Los Angeles.
29. Merriman, B., J.K. Bence, and S.J. Osher, Motion of multiple junctions: A level set approach. Journal of computational physics, 1994. 112(2): p. 334-363.
30. Ray, N. and S.T. Acton. Image segmentation by curve evolution with clustering. in Proc. of 34th Asilomar Conf.on Sig., Sys., and Computers. Pacific. 2000. Grove, CA, USA: IEEE.
31. Insight Segmentation and Registration Toolkit (ITK). Available from: http://www.itk.org/.
32. Staal, J., et al., Ridge-based vessel segmentation in color images of the retina. Medical Imaging, IEEE Transactions on, 2004. 23(4): p. 501-509.
33. Internet Brain Segmentation Repository (IBSR). Available from: http://www.cma.mgh.harvard.edu/ibsr/.