A scalable framework for segmenting magnetic resonance images

Journal of Signal Processing Systems

Volumn 54, Issue 1-3, 2009, Pages 183-203

  Hore, Prodip ^a   Hall, Lawrence O ^a   Goldgof, Dmitry B ^a   Gu, Yuhua ^a   Maudsley, Andrew A ^b   Darkazanli, Ammar ^b  

^a University of South Florida   (United States)

^b UNIVERSITY OF MIAMI MILLER SCHOOL OF MEDICINE   (United States)

Author keywords

Automatic; Clustering; FCM; Magnetic resonance imaging; Scalable; Segmentation

Indexed keywords

AUTOMATIC; CLUSTERING; FCM; SCALABLE; SEGMENTATION;

ATOMS; BRAIN; CLUSTERING ALGORITHMS; FUZZY CLUSTERING; FUZZY RULES; FUZZY SYSTEMS; IMAGE ENHANCEMENT; IMAGE PROCESSING; IMAGE SEGMENTATION; MAGNETIC MATERIALS; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; MAGNETISM;

RESONANCE;

EID: 58149263279     PISSN: 19398018     EISSN: 19398115     Source Type: Journal    
DOI: 10.1007/s11265-008-0243-1     Document Type: Article

References (65)

1. FMRIB Software Library (2007). FSL package. http://www.fmrib.ox.ac.uk/ fsl/.
2. SPM (2008). SPM package. http://www.fil.ion.ucl.ac.uk/spm/.
3. D. L. Pham C. Xu J. L. Prince 2000 Current methods in medical image segmentation Annual Review of Biomedical Engineering 2 315 337
4. J. L. Marroquin B. C. Vemuri S. Botello F. Calderon A. Fernandez-Bouzas 2002 An accurate and efficient bayesian method for automatic a segmentation of brain MRI IEEE Transactions on Medical Imaging 21 8 934 945 August
5. M. S. Atkins B. T. Mackiewich 1998 Fully automatic segmentation of the brain in MRI IEEE Transactions on Medical Imaging 17 1 98 107
6. Lin, P., Yang, Y., Zheng, C.-X., & Gu, J.-W. (2004). An efficient automatic framework for segmentation of MRI brain image. In Proceedings of the fourth international conference on computer and information technology (CIT 04).
7. Legal-Ayala, H. A., & Facon, J. (2004). Automatic segmentation of brain MRI through learning by example. In International conference on image processing (ICIP).
8. Z. Wu K. D. Paulsen J. M. Sullivan Jr. 2005 Adaptive model initialization and deformation for automatic segmentation of t1-weighted brain MRI data IEEE Transactions on Biomedical Engineering 52 6 1128 1131 June
9. Selvathi, D., Arulmurgan, A., Selvi, S. T. S, & Alagappan, S. (2005). MRI image segmentation using unsupervised clustering techniques. In Proceedings of the sixth international conference on computational intelligence and multimedia applications (ICCIMA 05).
10. Wang, Z. M., Song, Q., & Soh, Y. C. (2006). MRI brain image segmentation by adaptive spatial deterministic annealing clustering. In 3rd IEEE international symposium on biomedical imaging.
11. Mayer, A., & Greenspan, H. (2006). Segmentation of brain MRI by adaptive mean shift. In 3rd IEEE international symposium on biomedical imaging.
12. Song, Z., Tustison, N., Avants, B., & Gee, J. (2006). Adaptive graph cuts with tissue priors for brain MRI segmentation. In 3rd IEEE international symposium on biomedical imaging.
13. H. Greenspan A. Ruf J. Goldberger 2006 Constrained Gaussian mixture model framework for automatic segmentation of MR brain images IEEE Transactions on Medical Imaging 25 9 1233 1245
14. Gu, Y., Hall, L., Goldgof, D., Kanade, P., & Murtagh, F. (2005). Sequence tolerant segmentation system of brain MRI. In IEEE international conference on systems, man and cybernetics (pp. 2936-2943). October.
15. B. M. Dawant S. L. Hartmann J.-P. Thirion F. Maes D. Vandermeulen P. Demaerel 1999 Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part I, methodology and validation on normal subjects IEEE Transactions on Medical Imaging 18 10 909 916 October
16. Baillard, C., Hellier, P., & Barillot, C. (2000). Segmentation of 3D brain structures using level sets and denseregistration. In Proceedings IEEE workshop on mathematical methods in biomedical image analysis.
17. Y. Zhang M. Brady S. Smith 2001 Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm IEEE Transactions on Medical Imaging 20 1 45 57
18. S. J. Kiebel J. Ashburner J.-B. Poline K. J. Friston 1997 MRI and PET coregistration-a cross validation of statistical parametric mapping and automated image registration NeuroImage 5 271 279
19. R. Guillemaud J. M. Brady 1997 Estimating the bias field of MR images IEEE Transactions on Medical Imaging 16 3 238 251
20. T. W. Cheng D. B. Goldgof L. O. Hall 1998 Fast fuzzy clustering Fuzzy Sets and Systems 93 49 56
21. N. R. Pal J. C. Bezdek 2002 Complexity reduction for "large image" processing IEEE Transactions on Systems, Man, and Cybernetics, Part B 32 5 598 611
22. S. Eschrich J. Ke L. O. Hall D. B. Goldgof 2003 Fast accurate fuzzy clustering through data reduction IEEE Transactions on Fuzzy Systems 11 2 262 270
23. J. C. Bezdek R. J. Hathaway M. J. Sabin W. T. Tucker 1987 Convergence theory for fuzzy C-means: Counterexamples and repairs IEEE Transactions on Systems, Man and Cybernetics 17 5 873 877
24. Altman, D. (1999). Efficient fuzzy clustering of multi-spectral images. In IEEE FUZZY.
25. R. J. Hathaway J. C. Bezdek 2006 Extending fuzzy and probabilistic clustering to very large data sets Journal of Computational Statistics and Data Analysis 51 215 234
26. J. F. Kolen T. Hutcheson 2002 Reducing the time complexity of the fuzzy C-means algorithm IEEE Transactions on Fuzzy Systems 10 263 267
27. Hore, P., Hall, L. O., & Goldgof, D. B. (2006). A cluster ensemble framework for large data sets. In IEEE international conference on systems, man and cybernetics.
28. C. Borgelt R. Kruse 2003 Speeding up fuzzy clustering with neural network techniques Fuzzy Systems 2 852 856
29. Bradley, P. S., Fayyad, U., & Reina, C. (1998). Scaling clustering algorithms to large databases. In Proceedings of the fourth international conference on knowledge discovery and data mining, KDD-1998 (pp. 9-15).
30. F. Farnstrom J. Lewis C. Elkan 2000 Scalability for clustering algorithms revisited ACM SIGKDD Explorations 2 51 57
31. Hore, P., Hall, L. O., & Goldgof, D. B. (2007). Single pass fuzzy C means. IEEE-FUZZ.
32. Hore, P. (2007). Scalable frameworks and algorithms for cluster ensembles and clustering data streams. Ph.D. Dissertation, Dept. of CSE, Univ. of South Florida, May.
33. Gupta, C., Grossman, R. (2004). GenIc: A single pass generalized incremental algorithm for clustering. In Proceedings of the fourth {SIAM} international conference on data mining (SDM 04) (pp. 22-24).
34. B. Thiesson C. Meek D. Heckerman 2001 Accelerating EM for large databases Machine Learning Journal 45 279 299
35. P. S. Bradley U. M. Fayyad C. A. Reina 2000 Clustering very large databases using EM mixture models ICPR 2 76 80
36. Karkkainen, I., & Franti, P. (2005). Gradual model generator for single-pass clustering. ICDM, 681-684.
37. Aggarwal, C. C., Han, J., Wang, J., & Yu, P. S. (2003). A framework for clustering evolving data streams, In Proc. of VLDB.
38. Aggarwal, C. C., Han, J., Wang, J., & Yu, P. S. (2004). A framework for projected clustering of high dimensional data streams. In Proc. of VLDB.
39. Yang, J. (2003). Dynamic clustering of evolving streams with a single pass. In Proc. of ICDE.
40. Nasraoui, O., Cardona, C., Rojas, C., & Gonzlez, F. (2003). Tecno-streams: Tracking evolving clusters in noisy data streams with a scalable immune system learning model. In Proc. of ICDM (pp. 235-242).
41. Cao, F., Estery, M., Qian, W., & Zhou, A. (2006). Density-based clustering over an evolving data stream with noise. SDM.
42. O'Callaghan, L., Mishra, N., Meyerson, A., Guha, S., & Motwani, R. (2002). Streaming-data algorithms for high-quality clustering. In Proceedings of IEEE international conference on data engineering, March.
43. Guha, S., Meyerson, A., Mishra, N., Motwani, R., & O'Callaghan, L. (2003). Clustering data streams: Theory and practice. In IEEE transactions on knowledge and data engineering (pp. 515-528).
44. R. J. Hathaway J. C. Bezdek 1995 Optimization of clustering criteria by reformulation IEEE Transactions on Fuzzy Systems 3 241 245
45. T. N. Tran R. Wehrens L. M. C. Buydens 2005 Clustering multispectral images: a tutorial Chemometrics and Intelligent Laboratory Systems 77 3 17
46. K.-S. Chuang H.-L. Tzeng S. Chen J. Wu T.-J. Chen 2006 Fuzzy c-means clustering with spatial information for image segmentation Computerized Medical Imaging and Graphics 30 9 15
47. Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification (2nd edn.). New York: Wiley-Interscience.
48. M. S. Cohen R. D. DuBois M. M. Zeineh 2000 Rapid and effective correction of RF inhomogeneity for high field magnetic resonance imaging Human Brain Mapping 10 4 204 211
49. S. M. Smith 2002 Fast robust automated brain extraction Human Brain Mapping 17 3 143 155 November
50. Rousseau, F., Maudsley, A., Ebel, A., Darkazanli, A., Weber, P., Sivasankaran, K., et al. (2005). Evaluation of sub-voxel registration accuracy between MRI and 3D MR spectroscopy of the brain. San Diego: SPIE Medical Imaging.
51. K. Young Y. Chen J. Kornak G. B. Matson N. Schuff 2005 Classification of high dimensional, multi-spectral data sets using computational mechanics Physical Review Letters 94 9870
52. A. A. Maudsley A. Darkazanli J. R. Alger L. O. Hall N. Schuff C. Studholme 2006 Comprehensive processing, display, and analysis for in vivo MR spectroscopic imaging NMR in Biomedicine 19 492 503
53. MIDAS (2008). MIDAS Project Website. http://midas.med.miami.edu/.
54. Hore, P., Hall, L. O., & Goldgof, D. B. (2007). Creating streaming iterative soft clustering algorithms. NAFIPS.
55. Hore, P., Hall, L. O., & Goldgof, D. B. (2007). A fuzzy C means variant for clustering evolving data streams. IEEE-SMC.
56. L. M. Fletcher-Heath L. O. Hall D. B. Goldgof F. Reed Murtagh 2001 Automatic segmentation of non-enhancing brain tumors in magnetic resonance images Artificial Intelligence in Medicine 21 43 63
57. M. C. Clark L. O. Hall D. B. Goldgof R. Velthuizen R. Murtagh M. S. Silbiger 1998 Automatic tumor segmentation using knowledge-based techniques IEEE Transactions on Medical Imaging 17 2 187 201
58. Clark, M. C., Hall, L. O., Goldgof, D. B., Velthuizen, R., Murtagh, R., & Silbiger, M. S. (1998). Unsupervised brain tumor segmentation using knowledge-based fuzzy techniques. In H.-N. Teodorescu, A. Kandel, & L. C. Jain (Eds.), Fuzzy and neuro-fuzzy systems in medicine (pp. 137-169). Lille: CRC.
59. M. Clark D. Goldgof L. O. Hall L. Clarke M. Silbiger C. Li 1994 MRI segmentation using fuzzy clustering techniques integrating knowledge IEEE Engineering in Medicine & Biology 13 5 730 742
60. D. L. Pham J. L. Prince 1999 Adaptive fuzzy segmentation of magnetic resonance images IEEE Transactions on Medical Imaging 18 9 737 752
61. J. C. Bezdek L. O. Hall L. P. Clarke 1993 Review of MR image segmentation techniques using pattern recognition Medical Physics 20 1033 1048
62. D. L. Pham J. L. Prince 1999 An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities Pattern Recognition Letters 20 1 57 68
63. M. N. Ahmed S. M. Yamany 2002 A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data IEEE Transactions on Medical Imaging 21 3 193 199
64. A. M. Bensaid J. C. Bezdek L. O. Hall R. P. Velthuizen L. P. Clarke 1992 Partially supervised fuzzy c-means algorithm for segmentation of MR images Proc. SPIE 1710 522 528
65. S. Bouix M. Martin-Fernandez L. Ungar M. Nakamura M.-S. Koo R. W. McCarley 2007 On evaluating brain tissue classifiers without a ground truth NeuroImage 36 1207 1224