Abstract
Artificial intelligence algorithms have been used in a wide range of applications in clinical aided diagnosis, such as automatic MR image segmentation and seizure EEG signal analyses. In recent years, many machine learning-based automatic MR brain image segmentation methods have been proposed as auxiliary methods of medical image analysis in clinical treatment. Nevertheless, many problems regarding precise medical images, which cannot be effectively utilized to improve partition performance, remain to be solved. Due to the poor contrast in grayscale images, the ambiguity and complexity of MR images, and individual variability, the performance of classic algorithms in medical image segmentation still needs improvement. In this paper, we introduce a distributed multitask fuzzy c-means (MT-FCM) clustering algorithm for MR brain image segmentation that can extract knowledge common among different clustering tasks. The proposed distributed MT-FCM algorithm can effectively exploit information common among different but related MR brain image segmentation tasks and can avoid the negative effects caused by noisy data that exist in some MR images. Experimental results on clinical MR brain images demonstrate that the distributed MT-FCM method demonstrates more desirable performance than the classic signal task method.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grants 61702225 and 61772241, by the Natural Science Foundation of Jiangsu Province under Grant BK20160187, by the 2018 Six Talent Peaks Project of Jiangsu Province under Grant XYDXX-127, by the Science and technology demonstration project of social development of Wuxi under Grant WX18IVJN002,by the Youth Foundation of the Commission of Health and Family Planning of Wuxi under Grant Q201654, and by the Jiangsu Committee of Health under Grant H2018071.
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Jiang, Y., Zhao, K., Xia, K. et al. A Novel Distributed Multitask Fuzzy Clustering Algorithm for Automatic MR Brain Image Segmentation. J Med Syst 43, 118 (2019). https://doi.org/10.1007/s10916-019-1245-1
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DOI: https://doi.org/10.1007/s10916-019-1245-1