Computer Science ›› 2017, Vol. 44 ›› Issue (12): 260-265.doi: 10.11896/j.issn.1002-137X.2017.12.047

Special Issue: Medical Imaging

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MR Brain Image Segmentation Method Based on Wavelet Transform Image Fusion Algorithm and Improved FCM Clustering

GENG Yan-ping, GUO Xiao-ying, WANG Hua-xia, CHEN Lei and LI Xue-mei   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Concerning the problems that many image segmentation algorithms based on fuzzy C mean (FCM) are sensitive to noise and contour segmentation is not clear,an improved algorithm based on wavelet image fusion and FCM clustering algorithm was proposed.And it is applied to MR medical image segmentation successfully.In the first stage of the image segmentation system,the Haar wavelet multi-resolution characteristics were used to maintain spatial information between pixels.In the second stage,wavelet image fusion algorithm was adopted to fuse the obtained multi-resolution image and original image,thus to enhance the clarity of processed images and to reduce noise.In the third stage,FCM technology was used for image segmentation.Experiments on BrainWeb datasets show that compared with the current algorithms,the proposed algorithm has higher segmentation accuracy and robustness to noise,and the processing time is not obviously increased.

Key words: MR brain image segmentation,Wavelet image fusion,Fuzzy C-means clustering,Robustness

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