A hybrid Harmony Search algorithm to MRI brain segmentation

Proceedings of the 9th IEEE International Conference on Cognitive Informatics, ICCI 2010

Volumn , Issue , 2010, Pages 712-719

  Alia, Osama Moh'd ^a   Mandava, Rajeswari ^a   Aziz, Mohd Ezane ^b  

^a SCHOOL OF PHYSICS   (Malaysia)

^b SCHOOL OF HEALTH SCIENCES   (Malaysia)

Author keywords

Automatic brain MRI segmentation; Dynamic fuzzy clustering; Harmony Search; PBMF index

Indexed keywords

AUTOMATIC BRAIN MRI SEGMENTATION; BRAIN IMAGES; BRAIN MRI; BRAIN SEGMENTATION; CLUSTER CENTERS; CLUSTER VALIDITY INDICES; CLUSTERING RESULTS; CLUSTERING-BASED; DECISION VARIABLES; DYNAMIC CLUSTERING ALGORITHM; DYNAMIC FUZZY CLUSTERING; FUZZY C MEAN; GROUND TRUTH; HARMONY SEARCH; HARMONY SEARCH ALGORITHMS; MAGNETIC RESONANCE BRAIN IMAGES; MEDICAL IMAGE PROCESSING; MEMORY VECTORS; NATURALLY OCCURRING; NUMBER OF CLUSTERS; OBJECTIVE FUNCTIONS; PBMF INDEX; VARIABLE LENGTH; VARIABLE NUMBER;

FUZZY CLUSTERING; FUZZY SYSTEMS; IMAGE SEGMENTATION; INFORMATION SCIENCE; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MATHEMATICAL OPERATORS; MEDICAL IMAGING;

CLUSTERING ALGORITHMS;

EID: 78649857713     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/COGINF.2010.5599819     Document Type: Conference Paper

References (58)

1. W. Dou, S. Ruan, Y. Chen, D. Bloyet, and J.-M. Constans, "A framework of fuzzy information fusion for the segmentation of brain tumor tissues on mr images," Image and Vision Computing, vol. 25, no. 2, pp. 164-171, 2007.
2. J. Wang, J. Kong, Y. Lu, M. Qi, and B. Zhang, "A modified fcm algorithm for mri brain image segmentation using both local and nonlocal spatial constraints," Computerized Medical Imaging and Graphics, vol. 32, no. 8, pp. 685-698, 2008.
3. A. W.-C. Liew and H. Yan, "Current methods in the automatic tissue segmentation of 3d magnetic resonance brain images," Current medical imaging reviews, vol. 2, pp. 91-103, 2006.
4. I.Wells,W. M.,W. E. L. Grimson, R. Kikinis, and F. A. Jolesz, "Adaptive segmentation of mri data," Medical Imaging, IEEE Transactions on, vol. 15, no. 4, pp. 429-442, 1996.
5. T. Kapur, W. E. L. Grimson, W. M. Wells, and R. Kikinis, "Segmentation of brain tissue from magnetic resonance images," Medical Image Analysis, vol. 1, no. 2, pp. 109-127, 1996.
6. J. Zhou and J. C. Rajapakse, "Fuzzy approach to incorporate hemodynamic variability and contextual information for detection of brain activation," Neurocomputing, vol. 71, no. 16-18, pp. 3184-3192, 2008.
7. L. Szilagyi, Z. Benyo, S. M. Szilagyi, and H. S. Adam, "Mr brain image segmentation using an enhanced fuzzy c-means algorithm," in Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, 2003, pp. 724-726.
8. H. A. Mokbel, M. E.-S. Morsy, and F. E. Z. Abou-Chadi, "Automatic segmentation and labeling of human brain tissue from mr images," in Seventeenth National Radio Science Conference, 17th NRSC ', 2000, pp. 1-8.
9. L. Xiaohe, Z. Taiyi, and Q. Zhan, "Image segmentation using fuzzy clustering with spatial constraints based on markov random field via bayesian theory," IEICE Trans. Fundam. Electron. Commun. Comput. Sci., vol. E91-A, no. 3, pp. 723-729, 2008.
10. K. Van Leemput, F. Maes, D. Vandermeulen, and P. Suetens, "Automated model-based tissue classification of mr images of the brain," IEEE Transactions on Medical Imaging, vol. 18, no. 10, pp. 897-908, 1999. (Pubitemid 32211105)
11. K. Van Leemput, F. Maes, D. Vandermeulen, and P. Suetens, "Automated model-based bias field correction of mr images of the brain," IEEE Transactions on Medical Imaging, vol. 18, no. 10, pp. 885-896, 1999. (Pubitemid 32211104)
12. J. C. Bezdek, L. O. Hall, and L. P. Clarke, "Review of mr image segmentation techniques using pattern recognition," Medical Physics, vol. 20, no. 4, pp. 1033-1048, 1993.
13. Y. L. Chang and X. Li, "Adaptive image region-growing," IEEE Transactions on Image Processing, vol. 3, no. 6, pp. 868-872, 1994.
14. R. Adams and L. Bischof, "Seeded region growing," IEEE Transactions on pattern analysis and machine intelligence, vol. 16, no. 6, pp. 641-647, 1994.
15. R. Pohle and K. D. Toennies, "Segmentation of medical images using adaptive region growing," in Proceedings of SPIE (Medical Imaging), vol. 4322, San Diego, 2001, pp. 1337-1346.
16. J. Sijbers, P. Scheunders, M. Verhoye, A. Van der Linden, D. van Dyck, and E. Raman, "Watershed-based segmentation of 3d mr data for volume quantization," Magnetic Resonance Imaging, vol. 15, no. 6, pp. 679-688, 1997.
17. M. S. Atkins and B. T. Mackiewich, "Fully automatic segmentation of the brain in mri," IEEE Transactions on Medical Imaging, vol. 17, no. 1, pp. 98-107, 1998.
18. M. Ashtari, J. L. Zito, B. I. Gold, J. A. Lieberman, M. T. Borenstein, and P. G. Herman, "Computerized volume measurement of brain structure," Investigative Radiology, vol. 25, no. 7, pp. 798-805, 1990.
19. L. Ji and H. Yan, "Attractable snakes based on the greedy algorithm for contour extraction," Pattern Recognition, vol. 35, no. 4, pp. 791-806, 2002.
20. T. McInerney and D. Terzopoulos, "Deformable models in medical image analysis: a survey," Medical Image Analysis, vol. 1, no. 2, pp. 91-108, 1996.
21. Y. Zhou and J. Bai, "Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain mri," IEEE Transactions on Biomedical Engineering, vol. 54, no. 1, pp. 122-129, 2007.
22. M. C. Clark, "Knowledge-guided processing of magnetic resonance images of the brain," Ph.D. dissertation, University of South Florida, Department of Computer Science and Engineering, South Florida USA, 1998.
23. M. Clark, L. Hall, D. Goldgof, and M. Silbiger, "Using fuzzy information in knowledge guided segmentation of brain tumors," in Fuzzy Logic in Artificial Intelligence Towards Intelligent Systems, 1997, pp. 167-181.
24. M. Sonka, S. K. Tadikonda, and S. M. Collins, "Knowledge-based interpretation of mr brain images," IEEE Transactions on Medical Imaging, vol. 15, no. 4, pp. 443-452, 1996.
25. S. Shen, W. Sandham, M. Granat, and A. Sterr, "Mri fuzzy segmentation of brain tissue using neighborhood attraction with neuralnetwork optimization," IEEE Transactions on Information Technology in Biomedicine, vol. 9, no. 3, pp. 459-467, 2005.
26. D. L. Pham, C. Xu, and J. L. Prince, "Current methods in medical image segmentation," Annual Review of Biomedical Engineering, vol. 2, no. 1, pp. 315-337, 2000.
27. P. Hore, L. O. Hall, D. B. Goldgof, Y. Gu, A. A. Maudsley, and A. Darkazanli, "A scalable framework for segmenting magnetic resonance images," Journal of Signal Processing Systems, vol. 54, no. 1-3, pp. 183-203, 2008.
28. J. Kang, L. Min, Q. Luan, X. Li, and J. Liu, "Novel modified fuzzy cmeans algorithm with applications," Digital Signal Processing, vol. 19, no. 2, pp. 309-319, 2009.
29. A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: a review," ACM Comput. Surv., vol. 31, no. 3, pp. 264-323, 1999.
30. S. E. Brian, L. Sabine, and L. Morven. Wiley Publishing, 2009.
31. H. Zhou, "Fuzzy c-means and its applications in medical imaging," in Computational Intelligence in Medical Imaging: Techniques and Applications, 2009, p. 213.
32. A. Rosenfeld and A. C. Kak, Digital Picture Processing. Academic Press, Inc. New York, NY., 1982, vol. 2nd.
33. A. K. Jain, R. P. W. Duin, and M. Jianchang, "Statistical pattern recognition: a review," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 4-37, 2000.
34. O. M. Alia, R. Mandava, D. Ramachandram, and M. E. Aziz, "Harmony search- based cluster initialization for fuzzy c-means segmentation of mr images." in International Technical Conference of IEEE Region 10 (TENCON), Singapore, 2009, pp. 1-6.
35. O. M. Alia, R. Mandava, D. Ramachandram, and M. E. Aziz, "A novel image segmentation algorithm based on harmony fuzzy search algorithm," in International Conference of Soft Computing and Pattern Recognition, SOCPAR '09., Malacca, Malaysia, 2009, pp. 335-340.
36. J. C. Bezdek and R. J. Hathaway, "Optimization of fuzzy clustering criteria using genetic algorithms," in Proceedings of the First IEEE Conference on Evolutionary Computation,IEEE World Congress on Computational Intelligence, vol. 2, Orlando, FL, USA, 1994, pp. 589-594.
37. U. Maulik and I. Saha, "Modified differential evolution based fuzzy clustering for pixel classification in remote sensing imagery," Pattern Recognition, vol. 42, no. 9, pp. 2135-2149, 2009.
38. P. M. Kanade and L. O. Hall, "Fuzzy ants and clustering," IEEE Transactions on Systems, Man and Cybernetics,Part A, vol. 37, no. 5, pp. 758-769, 2007.
39. K. S. Al-Sultan and C. A. Fedjki, "A tabu search-based algorithm for the fuzzy clustering problem," Pattern Recognition, vol. 30, no. 12, pp. 2023-2030, 1997.
40. S. Das, A. Abraham, and A. Konar, "Metaheuristic pattern clustering-an overview," in Metaheuristic Clustering. Springer, 2009, pp. 1-62.
41. S. Das and A. Konar, "Automatic image pixel clustering with an improved differential evolution," Applied Soft Computing, vol. 9, no. 1, pp. 226-236, 2009.
42. S. Saha and S. Bandyopadhyay, "A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters," Information Sciences, vol. 179, no. 19, pp. 3230-3246, 2009.
43. S. Saha and S. Bandyopadhyay, "Mri brain image segmentation by fuzzy symmetry based genetic clustering technique," in IEEE Congress on Evolutionary Computation, CEC, 2007, pp. 4417-4424.
44. R. Campello, E. Hruschka, and V. Alves, "On the efficiency of evolutionary fuzzy clustering," Journal of Heuristics, vol. 15, no. 1, pp. 43-75, 2009.
45. M. K. Pakhira, S. Bandyopadhyay, and U. Maulik, "A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification," Fuzzy Sets and Systems, vol. 155, no. 2, pp. 191-214, 2005.
46. U. Maulik and S. Bandyopadhyay, "Fuzzy partitioning using a realcoded variable-length genetic algorithm for pixel classification," IEEE Transactions on Geoscience and Remote Sensing., vol. 41, no. 5, pp. 1075-1081, 2003.
47. E. R. Hruschka, R. J. G. B. Campello, A. A. Freitas, and A. C. P. L. F. d. Carvalho, "A survey of evolutionary algorithms for clustering," IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, vol. 39, pp. 133-155, 2009.
48. D. Horta, M. Naldi, R. Campello, E. Hruschka, and A. de Carvalho, "Evolutionary fuzzy clustering: An overview and efficiency issues," in Foundations of Computational Intelligence, 2009, pp. 167-195.
49. S. Das, A. Abraham, and A. Konar, Metaheuristic Clustering. Springer Verlag, Germany, 2009.
50. Z. W. Geem, J. H. Kim, and G. Loganathan, "A new heuristic optimization algorithm: harmony search." SIMULATION, vol. 76, no. 2, pp. 60-68, 2001.
51. "Brain web: Simulated brain database. mcconnell brain imaging centre. montreal neurological institute, mcgill university, http://www.bic.mni. mcgill.ca/brainweb"
52. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, 1981.
53. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the em algorithm," Journal of the Royal Statistical Society. Series B (Methodological), vol. 39, no. 1, pp. 1-38, 1977.
54. G. Ingram and T. Zhang, in Music-Inspired Harmony Search Algorithm. Springer Berlin / Heidelberg, 2009, pp. 15-37.
55. Z. W. Geem, Harmony Search Algorithms for Structural Design Optimization. Springer Berlin / Heidelberg, 2009.
56. Z. W. Geem, C.-L. Tseng, and Y. Park, "Harmony search for generalized orienteering problem: Best touring in china," in Advances in Natural Computation. Springer Berlin / Heidelberg, 2005, pp. 741-750.
57. M. K. Pakhira, S. Bandyopadhyay, and U. Maulik, "Validity index for crisp and fuzzy clusters," Pattern Recognition, vol. 37, no. 3, pp. 487-501, 2004.
58. A. Ben-Hur and I. Guyon, "Detecting stable clusters using principal component analysis," Methods in Molecular Biology-Clifton then Totowa, vol. 224, pp. 159-182, 2003.