Gain Field Correction Fast Fuzzy c-Means Algorithm for Segmenting Magnetic Resonance Images

Author:

Song Jingjing,Zhao Qingjie,Wang Yuanquan,Tian Jie

Publisher

Springer Berlin Heidelberg

Reference11 articles.

1. Ahmed, M.N., Yamany, S.M., et al.: A Modified Fuzzy C-Means Algorithm For Bias Field Estimation and Segmentation of MRI Data. IEEE Transactions on Medical Imaging 21(3), 193–199 (2002)

2. He, R., Datta, S., et al.: Adaptive FCM with contextual constrains for segmentation of multi-spectral MRI. In: Proceeding of the 26th Annual International Conference of the IEEE EMBS, vol. 1, pp. 1660–1663 (2004)

3. Szilágyi, L., Benyó, Z., Szilágyi, S.M.: Brain Image Segmentation for Virtual Endoscopy. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS, vol. 1, pp. 1730–1732 (2004)

4. Chen, W., Giger, M.L.: A fuzzy c-means (FCM) based algorithm for intenity inhomogeneity correction and segmentation of MR images. IEEE International Symposium on Biomedical Imaging, 1307–1310 (2004)

5. Zhao, Y., Li, M.: A modified fuzzy c-means algorithm for segmentation of MRI. In: Fifth International Conference on Computational Intelligence and Multimedia Application, pp. 391–395 (2003)

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Quantification of Alzheimer’s Disease Brain Tissue Volume by an Enhanced Possibilistic Clustering Technique Based on Bias-Corrected Fuzzy Initialization;2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC);2017-12

2. Performance Analysis of Fuzzy C-Means Clustering Methods for MRI Image Segmentation;Procedia Computer Science;2016

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