MRI brain tumor segmentation based on improved fuzzy c-means method

Wankai Deng; Wei Xiao; Chao Pan; Jianguo Liu

doi:10.1117/12.832577

30 October 2009 MRI brain tumor segmentation based on improved fuzzy c-means method

Wankai Deng, Wei Xiao, Chao Pan, Jianguo Liu

Proceedings Volume 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 74972N (2009) https://doi.org/10.1117/12.832577
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

This paper focuses on the image segmentation, which is one of the key problems in medical image processing. A new medical image segmentation method is proposed based on fuzzy c- means algorithm and spatial information. Firstly, we classify the image into the region of interest and background using fuzzy c means algorithm. Then we use the information of the tissues' gradient and the intensity inhomogeneities of regions to improve the quality of segmentation. The sum of the mean variance in the region and the reciprocal of the mean gradient along the edge of the region are chosen as an objective function. The minimum of the sum is optimum result. The result shows that the clustering segmentation algorithm is effective.

Citation Download Citation

Wankai Deng, Wei Xiao, Chao Pan, and Jianguo Liu "MRI brain tumor segmentation based on improved fuzzy c-means method", Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74972N (30 October 2009); https://doi.org/10.1117/12.832577

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