An adaptive sparse Bayesian model combined with probabilistic label fusion for multiple sclerosis lesion segmentation in brain MRI-Reference-Cited by-同舟云学术

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An adaptive sparse Bayesian model combined with probabilistic label fusion for multiple sclerosis lesion segmentation in brain MRI

Published:2020-04 Issue: Volume:105 Page:695-704
ISSN:0167-739X
Container-title:Future Generation Computer Systems
language:en
Short-container-title:Future Generation Computer Systems

Author:

Wang Jingjing,Liu Meiru,Zhang Chunhui,Xu Huaqiang,Zhang Liren,Zhao Yuefeng

Funder

Shandong University of Science and Technology, China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Reference40 articles.

1. Brain structure segmentation in the presence of multiple sclerosis lesions;González-Villà;NeuroImage Clin.,2019

2. Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging;Danelakis;Comput. Med. Imaging Graph.,2018

3. Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches;Lladó;Inform. Sci.,2012

4. Applying the 2017 McDonald diagnostic criteria for multiple sclerosis;Petzold;Lancet Neurol.,2018

5. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging;García-Lorenzo;Med. Image Anal.,2013

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3. Low-contrast lesion segmentation in advanced MRI experiments by time-domain Ricker-type wavelets and fuzzy 2-means;Applied Intelligence;2022-03-12

4. Distribution-based imaging for multiple sclerosis lesion segmentation using specialized fuzzy 2-means powered by Nakagami transmutations;Applied Soft Computing;2021-09

5. Automatic detection of multiple sclerosis lesions using Mask R‐CNN on magnetic resonance scans;IET Image Processing;2020-12

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