A Novel Distributed Multitask Fuzzy Clustering Algorithm for Automatic MR Brain Image Segmentation

• Published in: Journal of medical systems Vol. 43; no. 5; pp. 118 - 9
• Main Authors: Jiang, Yizhang, Zhao, Kaifa, Xia, Kaijian, Xue, Jing, Zhou, Leyuan, Ding, Yang, Qian, Pengjiang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2019; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Brain; Clustering; Cybernetics; Distributed Analytics and Deep Learning in Health Care; EEG; Electroencephalography; Health Informatics; Health Sciences; Image & Signal Processing; Image analysis; Image contrast; Image processing; Image segmentation; Learning algorithms; Machine learning; Magnetic resonance imaging; Markov analysis; Medical imaging; Medicine; Medicine & Public Health; Neuroimaging; Statistics for Life Sciences; Medical image; Image segmentation; MR brain image; Distributed multitask fuzzy clustering
• ISSN: 0148-5598; 1573-689X; 1573-689X
• 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.