School of Digital Media, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu, 214122, People's Republic of China.
Changshu No.1 people's hospital, Changshu, Jiangsu, 215500, People's Republic of China.
J Med Syst. 2019 Mar 25;43(5):118. doi: 10.1007/s10916-019-1245-1.
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.
人工智能算法已广泛应用于临床辅助诊断，例如自动磁共振图像分割和癫痫脑电信号分析。近年来，许多基于机器学习的自动磁共振脑图像分割方法已被提出作为临床治疗中医学图像分析的辅助方法。然而，许多关于精确医学图像的问题仍然需要解决，这些问题无法有效地用于提高分割性能。由于灰度图像对比度差、磁共振图像的模糊性和复杂性以及个体变异性，经典算法在医学图像分割中的性能仍需改进。在本文中，我们引入了一种分布式多任务模糊 c 均值（MT-FCM）聚类算法，用于磁共振脑图像分割，可以提取不同聚类任务之间的共同知识。所提出的分布式 MT-FCM 算法可以有效地利用不同但相关的磁共振脑图像分割任务之间的信息共性，并避免某些磁共振图像中存在的噪声数据的负面影响。对临床磁共振脑图像的实验结果表明，分布式 MT-FCM 方法的性能优于经典的信号任务方法。
