Automatic segmentation of brain tissue based on improvedfuzzy c means clustering algorithm
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.
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.
Cite this paper
nullMiao, 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.
nullMiao, 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
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[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