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A robust FCM algorithm for image segmentation based on spatial information and total variation

Akbari, H ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/IranianMVIP.2015.7397532
  3. Publisher: IEEE Computer Society
  4. Abstract:
  5. Image segmentation with clustering approach is widely used in biomedical application. Fuzzy c-means (FCM) clustering is able to preserve the information between tissues in image, but not taking spatial information into account, makes segmentation results of the standard FCM sensitive to noise. To overcome the above shortcoming, a modified FCM algorithm for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster by smoothing it by Total Variation (TV) denoising. The proposed algorithm is evaluated with accuracy index in performing it on artificial synthesized images, and the results show the superior accuracy compared to some other state of the art FCM-based segmentation methods
  6. Keywords:
  7. FCM ; Algorithms ; Brain mapping ; Clustering algorithms ; Computer vision ; Image processing ; Magnetic resonance imaging ; Medical applications ; Biomedical applications ; Fuzzy C means clustering ; Segmentation methods ; Segmentation results ; Spatial informations ; Spatial neighborhoods ; Total variation ; Total variation denoising ; Image segmentation
  8. Source: 9th Iranian Conference on Machine Vision and Image Processing, 18 November 2015 through 19 November 2015 ; Volume 2016-February , 2015 , Pages 180-184 ; 21666776 (ISSN) ; 9781467385398 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/7397532