The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
MRI Fuzzy Segmentation of Brain Tissue Using Neighborhood Attraction With Neural-Network Optimization
Abstract
Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. Unfortunately, MR images always contain a significant amount of noise caused by operator performance, equipment, and the environment, which can lead to serious inaccuracies with segmentation. A robust segmentation technique based on an extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed in this paper. A neighborhood attraction, which is dependent on the relative location and features of neighboring pixels, is shown to improve the segmentation performance dramatically. The degree of attraction is optimized by a neural-network model. Simulated and real brain MR images with different noise levels are segmented to demonstrate the superiority of the proposed technique compared to other FCM-based methods. This segmentation method is a key component of an MR image-based classification system for brain tumors, currently being developed. Index Terms-Improved fuzzy c-means clustering (IFCM), magnetic resonance imaging (MRI), neighborhood attraction, segmentation.
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Cites
Pattern Recognition with Fuzzy Objective Function Algorithms
Bezdek, J. C. (1981) · doi:10.1007/978-1-4757-0450-1 · Cited by 9,881
A possibilistic approach to clustering
Krishnapuram, R., & Keller, J. M. (1993) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.227387 · Cited by 2,496
Design and construction of a realistic digital brain phantom
Collins, D. L., Zijdenbos, A. P., Kollokian, V., Sled, J. G., Kabani, N. J., Holmes, C. J., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.712135 · Cited by 1,822
A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data
Ahmed, M. N., Yamany, S. M., Mohamed, N., Farag, A. A., & Moriarty, T. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/42.996338 · Cited by 1,792
Adaptive segmentation of MRI data
Wells, W. M., Grimson, W. E. L., Kikinis, R., & Jolesz, F. A. (1996) · IEEE Transactions on Medical Imaging · doi:10.1109/42.511747 · Cited by 1,277
Automated model-based tissue classification of MR images of the brain
Van Leemput, K., Maes, F., Vandermeulen, D., & Suetens, P. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.811270 · Cited by 1,024
Adaptive fuzzy segmentation of magnetic resonance images
Pham, D. L., & Prince, J. L. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.802752 · Cited by 805
MRI simulation-based evaluation of image-processing and classification methods
Kwan, R. K.-S., Evans, A. C., & Pike, G. B. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.816072 · Cited by 709
The Young Adult Human Brain: An MRI-based Morphometric Analysis
Filipek, P. A., Richelme, C., Kennedy, D. N., & Caviness, V. S. (1994) · Cerebral Cortex · doi:10.1093/cercor/4.4.344 · Cited by 651
On standardizing the MR image intensity scale
Ny�l, L. G., & Udupa, J. K. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199912)42:6<1072::aid-mrm11>3.0.co;2-m · Cited by 583
Cited by
Automated medical image segmentation techniques
Sharma, N., & Aggarwal, L. (2010) · Journal of Medical Physics · doi:10.4103/0971-6203.58777 · Cited by 754
Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.05.028 · Cited by 348
Medical image analysis with artificial neural networks
Jiang, J., Trundle, P., & Ren, J. (2010) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2010.07.003 · Cited by 338
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Thaha, M. M., Kumar, K. P. M., Murugan, B. S., Dhanasekeran, S., Vijayakarthick, P., & Selvi, A. S. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1416-0 · Cited by 244
Metadata
- Authors
- Shen, S., Sandham, W., Granat, M., & Sterr, A.
- Venue
- IEEE Transactions on Information Technology in Biomedicine
- Published
- 2005-09
- Volume
- 9
- Issue
- 3
- Pages
- 459-467
- ISSN
- 1089-7771 (ISSN-L)
- OpenAlex
- W2160172739
- Cited by
- 377
- References
- 19
- FWCI
- 17.95
- Type
- journal article
- Language
- en
- Open access
- closed
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