ArticleJournal of medical systems2019
A Novel Distributed Multitask Fuzzy Clustering Algorithm for Automatic MR Brain Image Segmentation.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Segmentation of Brain Tissues from MRI Images Using Multitask Fuzzy Clustering Algorithm.Journal of healthcare engineering · 2023Article
- Pseudo-Label-Assisted Self-Organizing Maps for Brain Tissue Segmentation in Magnetic Resonance Imaging.Journal of digital imaging · 2022Article
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- Closed-Loop Phase-Dependent Vibration Stimulation Improves Motor Imagery-Based Brain-Computer Interface Performance.Frontiers in neuroscience · 2021Article
- A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View FuzzyFrontiers in neuroscience · 2021Article
- Sleep Quality Detection Based on EEG Signals Using Transfer Support Vector Machine Algorithm.Frontiers in neuroscience · 2021Article
- Multimodal Medical Supervised Image Fusion Method by CNN.Frontiers in neuroscience · 2021Article
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- Fuzzy System Based Medical Image Processing for Brain Disease Prediction.Frontiers in neuroscience · 2021Article
- Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI.Journal of healthcare engineering · 2021Article
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Authors and funding
7 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.