The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data
Abstract
In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
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Citations
Cites
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668698 · Cited by 4,731
Cited by
A Review of Methods for Correction of Intensity Inhomogeneity in MRI
Vovk, U., Pernus, F., & Likar, B. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.891486 · Cited by 851
Automated medical image segmentation techniques
Sharma, N., & Aggarwal, L. (2010) · Journal of Medical Physics · doi:10.4103/0971-6203.58777 · Cited by 754
Lei, T., Jia, X., Zhang, Y., He, L., Meng, H., & Nandi, A. K. (2018) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2018.2796074 · Cited by 482
Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation
Lei, T., Jia, X., Zhang, Y., Liu, S., Meng, H., & Nandi, A. K. (2019) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2018.2889018 · Cited by 369
A review of Methods for Bias Correction in Medical Images
Song, S., Zheng, Y., & He, Y. (2017) · Biomedical Engineering Review · doi:10.18103/bme.v3i1.1550 · Cited by 62
Metadata
- Authors
- Ahmed, M. N., Yamany, S. M., Mohamed, N., Farag, A. A., & Moriarty, T.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2002-03
- Volume
- 21
- Issue
- 3
- Pages
- 193-199
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2115242586
- Cited by
- 1,792
- References
- 1
- FWCI
- 26.58
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ahmed, M. N., Yamany, S. M., Mohamed, N., Farag, A. A., & Moriarty, T. (2002). A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/42.996338
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
