Anisotropic mean shift based fuzzy c-means segmentation of skin lesions

5th International Conference on Soft Computing as Transdisciplinary Science and Technology, CSTST '08 - Proceedings

Volumn , Issue , 2008, Pages 438-443

  Zhou, Huiyu ^a   Schaefer, Gerald ^b   Sadka, Abdul ^a   Celebi, M Emre ^c  

^a BRUNEL UNIVERSITY   (United Kingdom)

^b ASTON UNIVERSITY   (United Kingdom)

^c Louisiana State University   (United States)

Author keywords

Dermoscopy; Fuzzy c means; Image segmentation; Mean shift; Melanoma; Skin cancer

Indexed keywords

DERMOSCOPY; FUZZY C MEAN; FUZZY C-MEANS; MEAN SHIFT; SKIN CANCERS;

CLUSTERING ALGORITHMS; COMPUTATIONAL EFFICIENCY; COPYING; DERMATOLOGY; DIAGNOSIS; DIGITAL IMAGE STORAGE; FUZZY CLUSTERING; FUZZY SYSTEMS; ONCOLOGY; SOFT COMPUTING; VECTOR QUANTIZATION;

IMAGE SEGMENTATION;

EID: 77951610795     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1456223.1456313     Document Type: Conference Paper

References (15)

1. M. Ahmed, S. Yamany, N. Mohamed, A. Farag, and T. Moriaty. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imaging, 21:193-199, 2002.
2. G. Argenziano, H. Soyer, and V. De Giogri. Dermoscopy: A Tutorial. EDRE Medical Publishing & New Media, 2002.
3. D. Betsekas. Nonlinear Programming. Athena Scientific, 1995.
4. J. Bexdek. A convergence theorem for the fuzzy isodata clustering algorithms. IEEE Trans. Pattern Anal. Machine Intell., 2:1-8, 1980.
5. M. Binder, M. Schwarz, A. Winkler, A. Steiner, A. Kaider, K. Wolff, and H. Pehamberger. Epiluminescence microscopy. a useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. Arch Dermatol, 131(3), 1995.
6. T. Cheng, D. Goldgof, and L. Hall. Fast fuzzy clustering. Fuzzy sets and systems, 93:49-56, 1998.
7. K. Chuang, S. Tzeng, H. Chen, J. Wu, and T. Chen. Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imag. and Graph., 30:9-15, 2006.
8. D. Comaniciu and P. Meer. Mean shift analysis and applications. In Proc. of IEEE Conf. on Comput. Vis., pages 1197-1203, 1999.
9. D. Comaniciu and P. Meer. Mean shift: a robust approach toward feature space analysis. IEEE Trans. on Patt. Analys. Mach. Intell., 24:603-619, 2002.
10. R. Hu and L. Hathaway. On efficiency of optimization in fuzzy c-means. Neural, Parallel & Scientific Computations, 10:141-156, 2002.
11. A. Jemal, R. Siegel, E. Ward, Y. Hao, J. Xu, T. Murray, and M. Thun. Cancer statistics, 2008. CA Cancer J Clin, 58(2):71-96, 2008.
12. K. Steiner, M. Binder, M. Schemper, K. Wolff, and P. H. Statistical evaluation of epiluminescence dermoscopy criteria for melanocytic pigmented lesions. J Am Acad Dermatol, 29(4):581-588, 1993.
13. L. Szilágyi, Z. Benyo, S. Szilagyii, and H. Adam. Mr train image segmentation using an enhanced fuzzy c-means algorithm. In 25th Annual Information Conf. of IEEE EMBS, pages 17-21, 2003.
14. J. Wang, B. Thiesson, Y. Xu, and M. Cohen. Image and video segmentation by anisotropic kernel mean shift. In Proc. European Conference on Computer Vision, pages 238-249, 2004.
15. A. Wismüller, F. Vietze, J. Behrends, A. Meyer-Baese, M. Reiser, and H. Ritter. Fully automated biomedical image segmentation by self-organized model adaptation. Neural Netw., 17(8-9):1327-1344, 2004. (Pubitemid 39539545)