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| DTI Image Segmentation Algorithm Based on the Improved Spatial Fuzzy Clustering |
| Liu Xuyu, Zhang Xiangfen*, Ma Yan, Li Chuanjiang, Yang Yanqin |
| College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234,China |
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Abstract Aiming to resolve the problems of initial clustering selection randomness and noise sensitivity of fuzzy C means algorithm, this paper proposed an image segmentation algorithm based on the improved spatial fuzzy clustering to segment the DTI image of human brain. In this paper, we used the local density kernel function and the center distance function to select the initial clustering center accurately, which not only solved the problem of clustering effect instability caused by random selection of cluster center, but also made the objective function converge quickly, and improved the segmentation efficiency. Moreover, the proposed algorithm reduced the influence on the segmentation result caused by image noise and human factors by integrating normal distribution spatial information into fuzzy membership function. We segmented DTI data of human brain with the proposed method, FCM and SFCM to evaluate the clustering effect of the algorithm. In the experiments, following data were employed, including segmented 58 cases of DTI data provided by the University of Minnesota Biomedical Functional Imaging and Nerve Engineering Laboratory, 3 cases of FA parameter images, and 6 cases of iterative noisy human brain DTI images. Results show that the segmentation coefficient of proposed algorithm reached 0.9841. In the same image, the algorithm obtained the most improvement of 20.2% than FCM on the partition coefficient, and the most decline of 19.8% than SFCM on the partition entropy; The average number of iterations of the algorithm was 32, which is significantly lower than 52 of FCM and 76 of SFCM. Therefore, the algorithm can segment the important target accurately and quickly, and the segmentation results are insensitive to image noise.
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Received: 21 July 2017
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Corresponding Authors:
E-mail: xiangfen@shnu.edu.cn
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