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Fuzzy Clustering with Multi-Resolution Bilateral Filtering for Medical Image Segmentation

Fuzzy Clustering with Multi-Resolution Bilateral Filtering for Medical Image Segmentation

Kai Xiao, Jianli Li, Shuangjiu Xiao, Haibing Guan, Fang Fang, Aboul Ella Hassanien
Copyright: © 2013 |Volume: 3 |Issue: 4 |Pages: 13
ISSN: 2156-177X|EISSN: 2156-1761|EISBN13: 9781466635609|DOI: 10.4018/ijfsa.2013100104
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MLA

Xiao, Kai, et al. "Fuzzy Clustering with Multi-Resolution Bilateral Filtering for Medical Image Segmentation." IJFSA vol.3, no.4 2013: pp.47-59. http://doi.org/10.4018/ijfsa.2013100104

APA

Xiao, K., Li, J., Xiao, S., Guan, H., Fang, F., & Hassanien, A. E. (2013). Fuzzy Clustering with Multi-Resolution Bilateral Filtering for Medical Image Segmentation. International Journal of Fuzzy System Applications (IJFSA), 3(4), 47-59. http://doi.org/10.4018/ijfsa.2013100104

Chicago

Xiao, Kai, et al. "Fuzzy Clustering with Multi-Resolution Bilateral Filtering for Medical Image Segmentation," International Journal of Fuzzy System Applications (IJFSA) 3, no.4: 47-59. http://doi.org/10.4018/ijfsa.2013100104

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Abstract

Although fuzzy c-means (FCM) algorithm and some of its variants have been extensively widely used in unsupervised medical image segmentation applications in recent years, they more or less suffer from either noise sensitivity or loss of details, which always is a key point to medical image processing. This paper presents a novel FCM variation method that is suitable for medical image segmentation. The proposed method, typically by incorporating multi-resolution bilateral filter which is combined with wavelet thresholding, provides the following advantages: (1) it is less sensitive to both high- and low-frequency noise and removes spurious blobs and noisy spots, (2) it yields more homogeneous clustering regions, and (3) it preserves detail, thus significantly improving clustering performance. By the use of synthetic and multiple-feature magnetic resonance (MR) image data, the experimental results and quantitative analyses suggest that, compared to other fuzzy clustering algorithms, the proposed method further enhances the robustness to noisy images and capacity of detail preservation.

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