Automated fuzzy-connectedness-based segmentation in extraction of multiple sclerosis lesions

Advances in Soft Computing

Volumn 47, Issue , 2008, Pages 149-156

  Kawa, Jacek ^a   Pietka, Ewa ^a  

^a SILESIAN UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 58149217174     PISSN: 16153871     EISSN: 18600794     Source Type: Book Series    
DOI: 10.1007/978-3-540-68168-7_15     Document Type: Article

References (11)

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4. Sajja, B.R., Datta, S., He, R., Narayana, P.A.: A unified approach for lesion segmentation on MRI of multiple sclerosis. In: Proceedings of the 26th Annual International Conference of the EMBS, vol. 1, pp. 1778-1781 (2004)
5. Kawa, J., Pietka, E.: Kernelized fuzzy c-means method in fast segmentation of demyelination plaques in multiple sclerosis. In: Proceedings of the 29th Annual International Conference of the EMBS. IEEE Computer Society Press, Los Alamitos (2007)
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8. Kawa, J., Pietka, E.: Kernelized Fuzzy c-means method in segmentation of demyelination plaques in multiple sclerosis. In: Int, X.I. (ed.) XI Int. Conference on Medical Informatics and Technologies - MIT 2006, Skalmierski, Gliwice, pp. 20-27 (2006)
9. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
10. Dice, L.: Measures of the amount of ecological association between species. J. Ecology 26, 297-302 (1945)
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