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Automatic segmentation of brain tissue based on improvedfuzzy c means clustering algorithm

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DOI: 10.4236/jbise.2011.42014    4,747 Downloads   9,075 Views   Citations

ABSTRACT

In medical images, exist often a lot of noise, the noise will seriously affect the accuracy of the segmentation results. The traditional standard fuzzy c-means(FCM) algorithm in image segmentation do not taken into account the relationship the adjacent pixels, which leads to the standard fuzzy c-means(FCM) algorithm is very sensitive to noise in the image. Proposed improvedfuzzy c-means(FCM) algorithm, taking both the local and non-local information into the standard fuzzy c-means(FCM) clustering algorithm. The ex-periment results can show that the improved algorithm can achieve better effect than other methods of brain tissue segmentation.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Miao, Z. , Lin, X. and Liu, C. (2011) Automatic segmentation of brain tissue based on improvedfuzzy c means clustering algorithm. Journal of Biomedical Science and Engineering, 4, 100-104. doi: 10.4236/jbise.2011.42014.

References

[1] Buades, A., Coll, B. and Morel, J.M. (2005) A non-local algorithm for image denoising. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2, 60-65.
[2] Buades, A., Coll, B. and Morel, J.M. (2004) On image denoising methods. Technical Report 2004-15, CMLA.
[3] Garnett, R., Huegerich, T., Chui, C. and He, W.J. (2005) A universal noise removal algorithm with an impulse detector. IEEE Transactions on Image Processing, 14, 1747-1754. doi:10.1109/TIP.2005.857261
[4] Johnston, B., Atkins, M.S., Mackiewich, B., et al. (1996) Segmetation of multiple selerosis lesions in intensity corrected multispectral MRI. IEEE Transactions on Medical Imaging, 15, 154-169. doi:10.1109/42.491417
[5] Suri, J.S., Singh, S. and Reden, L. (2002) Computer vision and pattern recognition techniques for 2D and 3D MR cerebral cortical segmentation (Part I): A state-of- the-art review. Pattern Analysis & Application, 5, 46-76. doi:10.1007/s100440200005
[6] Suri, J.S., Singh, S. and Reden, L. (2002) Fusion of region and boundary/surface-based computer vision and pattern recognition techniques for 2D and 3D MR cerebral cortical segmentation (Part II): A state-of-the-art review. Pattern Analysis & Application, 5, 77-98. doi:10.1007/s100440200006
[7] Kapur, J.N., Sahoo, P.K. and Wong, A.K.C. (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics and Image Processing, 29, 273-285. doi:10.1016/0734-189X(85)90125-2
[8] Abutaleb, A.S. (1989) Automatic thresholding of gray- level pictures using two-dimension entropy. Computer Vision, Graphics and Image Processing, 47, 22-32.
[9] Styner, M., Brechbuhler, C. and Szckely, G. (2000) Parametric estimate of intensity inhomogeneities applied to MRI. IEEE Transactions on Medical Imaging, 19, 153-165. doi:10.1109/42.845174
[10] Bezdek J. (1980) A convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2, l-8. doi:10.1109/TPAMI.1980.4766964

  
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