Evolving fuzzy image segmentation

IEEE International Conference on Fuzzy Systems

Volumn , Issue , 2011, Pages 1603-1609

  Othman, Ahmed A ^a   Tizhoosh, Hamid R ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Evolving fuzzy systems; Image segmentation; SIFT

Indexed keywords

EVOLVING FUZZY SYSTEMS; FUZZY IMAGE SEGMENTATION; GOLD STANDARDS; MEDICAL IMAGES; SEGMENTATION ACCURACY; SEGMENTATION RESULTS; SEGMENTATION TECHNIQUES; SEGMENTED IMAGES; SIFT; SYSTEM USE; TEST IMAGES; TRAINING IMAGE;

FUZZY SYSTEMS; MEDICAL IMAGING; PIXELS;

IMAGE SEGMENTATION;

EID: 80053065118     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZY.2011.6007601     Document Type: Conference Paper

References (36)

1. D. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol. 60, no. 2, pp. 91-110, 2004.
2. A. Moghaddamzadeh and N. Bourbakis, "A fuzzy region growing approach for segmentation of color images," Pattern recognition, vol. 30, no. 6, pp. 867-881, 1997.
3. J. Yang, S. Hao, and P. Chung, "Color image segmentation using fuzzy C-means and eigenspace projections," Signal Processing, vol. 82, no. 3, pp. 461-472, 2002.
4. G. Karmakar and L. Dooley, "A generic fuzzy rule based image segmentation algorithm," Pattern Recognition Letters, vol. 23, no. 10, pp. 1215-1227, 2002.
5. W. Tao, J. Tian, and J. Liu, "Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm," Pattern Recognition Letters, vol. 24, no. 16, pp. 3069-3078, 2003.
6. L. Cinque, G. Foresti, and L. Lombardi, "A clustering fuzzy approach for image segmentation," Pattern Recognition, vol. 37, no. 9, pp. 1797-1807, 2004.
7. D. Zhang and S. Chen, "A novel kernelized fuzzy c-means algorithm with application in medical image segmentation," Artificial Intelligence in Medicine, vol. 32, no. 1, pp. 37-50, 2004.
8. W. Cai, S. Chen, and D. Zhang, "Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation," Pattern Recognition, vol. 40, no. 3, pp. 825-838, 2007.
9. K. Chuang, H. Tzeng, S. Chen, J. Wu, and T. Chen, "Fuzzy c-means clustering with spatial information for image segmentation," Computerized Medical Imaging and Graphics, vol. 30, no. 1, pp. 9-15, 2006.
10. A. Doncescu, J. Aguilar-Martin, and J. Atine, "Image color segmentation using the fuzzy tree algorithm T-LAMDA," Fuzzy sets and systems, vol. 158, no. 3, pp. 230-238, 2007.
11. Z. Yang, F. Chung, and W. Shitong, "Robust fuzzy clustering-based image segmentation," Applied Soft Computing, vol. 9, no. 1, pp. 80-84, 2009.
12. Y. Zhuge, J. Udupa, and P. Saha, "Vectorial scale-based fuzzy-connected image segmentation," Computer Vision and Image Understanding, vol. 101, no. 3, pp. 177-193, 2006.
13. W. Dou, S. Ruan, Y. Chen, D. Bloyet, and J. 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.
14. L. Ma and R. Staunton, "A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns," Pattern recognition, vol. 40, no. 11, pp. 3005-3011, 2007.
15. Y. Han and P. Shi, "An improved ant colony algorithm for fuzzy clustering in image segmentation," Neurocomputing, vol. 70, no. 4-6, pp. 665-671, 2007.
16. Y. Hata and S. Kobashi, "Fuzzy segmentation of endorrhachis in magnetic resonance images and its fuzzy maximum intensity projection," Applied Soft Computing, vol. 9, no. 3, pp. 1156-1169, 2009.
17. J. Fan, M. Han, and J. Wang, "Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation," Pattern Recognition, vol. 42, no. 11, pp. 2527-2540, 2009.
18. J. Foo, G. Miyano, T. Lobe, and E. Winer, "Three-dimensional segmentation of tumors from CT image data using an adaptive fuzzy system," Computers in biology and medicine, vol. 39, no. 10, pp. 869-878, 2009.
19. F. Di Martino, V. Loia, and S. Sessa, "A segmentation method for images compressed by fuzzy transforms," Fuzzy Sets and Systems, vol. 161, no. 1, pp. 56-74, 2010.
20. K. Kasabov, "On-line learning, reasoning, rule extraction and aggregation in locally optimized evolving fuzzy neural networks," Neurocomputing, vol. 41, no. 1-4, pp. 25-45, 2001.
21. M. Futschik, A. Reeve, and N. Kasabov, "Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue," Artificial Intelligence in Medicine, vol. 28, no. 2, pp. 165-189, 2003.
22. V. Rivas, J. Merelo, I. Rojas, G. Romero, P. Castillo, and J. Carpio, "Evolving two-dimensional fuzzy systems," Fuzzy sets and systems, vol. 138, no. 2, pp. 381-398, 2003.
23. P. Angelov, "A fuzzy controller with evolving structure," Information Sciences, vol. 161, no. 1-2, pp. 21-35, 2004.
24. P. Chang, C. Liu, and Y. Wang, "A hybrid model by clustering and evolving fuzzy rules for sales decision supports in printed circuit board industry," Decision Support Systems, vol. 42, no. 3, pp. 1254-1269, 2006.
25. P. Chang, Y. Wang, and C. Liu, "The development of a weighted evolving fuzzy neural network for PCB sales forecasting," Expert Systems with Applications, vol. 32, no. 1, pp. 86-96, 2007.
26. H. Rong, N. Sundararajan, G. Huang, and P. Saratchandran, "Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and prediction," Fuzzy sets and systems, vol. 157, no. 9, pp. 1260-1275, 2006.
27. C. Xydeas, P. Angelov, S. Chiao, and M. Reoullas, "Advances in classification of EEG signals via evolving fuzzy classifiers and dependant multiple HMMs," Computers in biology and medicine, vol. 36, no. 10, pp. 1064-1083, 2006.
28. C. Lin and Y. Xu, "A self-adaptive neural fuzzy network with group-based symbiotic evolution and its prediction applications," Fuzzy Sets and Systems, vol. 157, no. 8, pp. 1036-1056, 2006.
29. J. de Barros and A. Dexter, "On-line identification of computationally undemanding evolving fuzzy models," Fuzzy sets and systems, vol. 158, no. 18, pp. 1997-2012, 2007.
30. R. Linden and A. Bhaya, "Evolving fuzzy rules to model gene expression," Biosystems, vol. 88, no. 1-2, pp. 76-91, 2007.
31. P. Angelov, E. Lughofer, and X. Zhou, "Evolving fuzzy classifiers using different model architectures," Fuzzy Sets and Systems, vol. 159, no. 23, pp. 3160-3182, 2008.
32. P. Phan and T. Gale, "Direct adaptive fuzzy control with a self-structuring algorithm," Fuzzy Sets and Systems, vol. 159, no. 8, pp. 871-899, 2008.
33. H. Du and N. Zhang, "Application of evolving Takagi-Sugeno fuzzy model to nonlinear system identification," Applied Soft Computing, vol. 8, no. 1, pp. 676-686, 2008.
34. R. Lai, C. Fan, W. Huang, and P. Chang, "Evolving and clustering fuzzy decision tree for financial time series data forecasting," Expert Systems with Applications, vol. 36, no. 2, pp. 3761-3773, 2009.
35. E. Lughofer, "On-line evolving image classifiers and their application to surface inspection," Image and Vision Computing, vol. 28, no. 7, pp. 1065-1079, 2010.
36. C. Juang, Y. Lin, and C. Tu, "A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing," Fuzzy Sets and Systems, vol. 161, no. 19, pp. 2552-2568, 2010.