Abstract
Fuzzy clustering algorithm as a more successful segmentation algorithm has been successfully applied in the medical field. However, the traditional Fuzzy C-means clustering (FCM) algorithm has the disadvantages of time-consuming, noise-sensitive and non-consideration of neighborhood information in the segmented brain MRI (MRI), and proposes a corresponding solution to these problems. Firstly, Canny operator and morphological processing method is employed to extract the brain MRI of image contour information, reducing the image background brings a series of calculation problem. Secondly, before the FCM image segmentation, the adaptive adjustment of the weight coefficient in the neighborhood is realized by introducing the gradient information to achieve the purpose of eliminating the noise and reducing the initial value of the image objective function. With the experiment proved above, the robustness of the algorithm is improved and effectively shorten the calculation time in the case of constant accuracy.
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Acknowledgements
This work is supported by China National Key Technology Research and Development Program project with no. 2015BAH13F01 and Guangdong Province Key Laboratory of Popular High Performance Computers Research Program.
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Song, W., Du, X., Wang, Q., Lei, Y., Cai, W., Fei, X. (2018). Cerebral Apoplexy Image Segmentation Based on Gray Level Gradient FCM Algorithm. In: Hung, J., Yen, N., Hui, L. (eds) Frontier Computing. FC 2017. Lecture Notes in Electrical Engineering, vol 464. Springer, Singapore. https://doi.org/10.1007/978-981-10-7398-4_45
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DOI: https://doi.org/10.1007/978-981-10-7398-4_45
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