Paper Info
Title
A Novel Distributed Multitask Fuzzy Clustering Algorithm for Automatic MR Brain Image Segmentation.
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.
Year
DOI
Venue
2019
10.1007/s10916-019-1245-1
Journal of medical systems
Keywords
Field
DocType
Distributed multitask fuzzy clustering,Image segmentation,MR brain image,Medical image
Fuzzy clustering,Aided diagnosis,Fuzzy logic,Algorithm,Image segmentation,Cluster analysis,Ambiguity,Medicine,Grayscale,Electroencephalography
Journal
Volume
Issue
ISSN
43
5
1573-689X
Citations 
PageRank 
References 
5
0.39
0
Authors
7
Name
Order
Citations
PageRank
Yizhang Jiang138227.24
Kaifa Zhao2193.65
Kaijian Xia350.39
Jing Xue4103.14
Leyuan Zhou573.13
Yang Ding651.40
Pengjiang Qian713311.25