Type-2 kernelized fuzzy c-means algorithm based on the uncertain width of Gaussian kernel with applications in MR image segmentation
- 10.2991/ic3me-15.2015.259How to use a DOI?
- KFCM; Type-2 fuzzy sets; Gaussian kernel; MR image.
While fuzzy c-means is a popular soft clustering method, its effectiveness is largely limited to spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to address this problem by mapping data with nonlinear relationships to appropriate feature spaces. Kernel width is crucial for effective kernel clustering. Unfortunately, for most applications, it is not easy to find the right width. To design and manage the uncertainty for kernel width, we propose a type-2 kernelized fuzzy c-means algorithm (T2KFCM). We extend the type-1 fuzzy sets of membership to interval type-2 fuzzy sets using two widths and which creates a footprint of uncertainty for the membership. Experiments on MR (Magnetic Resonance) image are given that compare kernelized FCM (KFCM) with T2KFCM. The results show that T2KFCM compares favorably to both of the previous models.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Qinli Zhang AU - Yajun Bi AU - Zhigang Gong PY - 2015/08 DA - 2015/08 TI - Type-2 kernelized fuzzy c-means algorithm based on the uncertain width of Gaussian kernel with applications in MR image segmentation BT - Proceedings of the 3rd International Conference on Material, Mechanical and Manufacturing Engineering PB - Atlantis Press SP - 1351 EP - 1354 SN - 2352-5401 UR - https://doi.org/10.2991/ic3me-15.2015.259 DO - 10.2991/ic3me-15.2015.259 ID - Zhang2015/08 ER -