A Novel Distributed Multitask Fuzzy Clustering Algorithm for Automatic MR Brain Image Segmentation.
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 aux...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 5 |
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| Autores principales: | , , , , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136129212&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136129212 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: May2019 vid: 43 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136129212 136129212 136129212 10.1007/s10916-019-1245-1 136129212 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Novel Distributed Multitask Fuzzy Clustering Algorithm for Automatic MR Brain Image Segmentation. aug: au: Jiang, Yizhang Zhao, Kaifa Xia, Kaijian Xue, Jing Zhou, Leyuan Ding, Yang Qian, Pengjiang affil: School of Digital Media, Jiangnan University, 1800 Lihu Avenue, 214122, Wuxi, Jiangsu, People's Republic of China sug: subj: Brain Pathology Magnetic Resonance Imaging Methods Clustering Algorithms Artificial Intelligence Utilization Image Processing, Computer Assisted Methods Human Diagnosis, Computer Assisted Methods Machine Learning Descriptive Statistics Brain Anatomy and Histology Comparative Studies ab: 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. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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