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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Srivastava, A. Asati, A. Bhattacharya, M. |
| Copyright Year | 2010 |
| Description | Author affiliation: ABV Indian Institute of Information Technology and Management Gwalior, India (Bhattacharya, M.) || Department of Electronics and Communication Mualana Azad National Institute of Technology Bhopal, India (Srivastava, A.; Asati, A.) |
| Abstract | An Accurate, Fast and Noise-Adaptive segmentation of Brain MR Images for clinical Analysis is a challenging problem. An improved Hybrid Clustering Algorithm is presented here, which integrates the concept of recently popularized Rough Sets and that of Fuzzy Sets. The concept of lower and upper approximations of rough sets is incorporated to handle uncertainty, vagueness, and incompleteness in class definition. For making the segmentation robust to Noise and intensity in-homogeneity, the images are proposed to be pre-processed with a neighbourhood averaging spatial filter. To accelerate the segmentation process, a novel Suppressed Rough Fuzzy C-Means model is presented in which a membership suppression mechanism has been implemented, which creates competition among clusters to speed-up the clustering process. The effectiveness of the presented algorithm along with comparison with other related algorithm has been demonstrated on a set of MR and CT scan images. The results using MRI data show that our method provides better results compared to standard Fuzzy C-Means based algorithms and other modified similar techniques. |
| Starting Page | 416 |
| Ending Page | 421 |
| File Size | 589750 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424483068 |
| e-ISBN | 9781424483075 |
| DOI | 10.1109/BIBM.2010.5706602 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Magnetic resonance imaging Medical Images Noise Clustering algorithms Rough sets Image Segmentation Rough Sets Fuzzy C-Means Approximation methods Indexes Clustering |
| Content Type | Text |
| Resource Type | Article |
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