Abstract
An adaptive fuzzy c-means (FCM) clustering algorithm is explored for segmentation of three-dimensional multi-spectral MR images. This algorithm takes into consideration of both noise and three-dimensional intensity non-uniformity. This algorithm models the intensity non-uniformity of MR images as a gain field or bias field that slowly varies in space, which is approximated by a linear combination of smooth basis functions made up of polynomials with different orders. The contextual constraints are included by introducing a regularization term into the cost function of FCM. The regularization term is a measure of aggregation of local voxels that tend to overcome the noise in voxel labeling. We present our scheme both for bias and gain fields, with special attention is paid to robust estimation of the bias field.
Original language | English (US) |
---|---|
Pages (from-to) | 1660-1663 |
Number of pages | 4 |
Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
Volume | 26 III |
State | Published - 2004 |
Externally published | Yes |
Event | Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States Duration: Sep 1 2004 → Sep 5 2004 |
Keywords
- Adaptive
- Bias field
- Contextual Constraints
- FCM
- MRI
- Multi-spectral
- Segmentation
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics