Adaptive Independent Subspace Analysis of Brain Magnetic Resonance Imaging Data

Ke, Qiao; Zhang, Jiangshe; Wei, Wei; Damaševičius, Robertas; Woźniak, Marcin

doi:10.1109/access.2019.2893496

Cited by 67 publications

(29 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Brain imaging analyses have been widely used to explore the mechanisms of AD [ 8 , 9 , 10 ] and improve the accuracy of AD diagnosis [ 11 , 12 ]. Because the brain is a highly complex system and brain signals have complex nonlinear dynamics, there has been increasing interest in complexity analyses by using brain imaging data such as electroencephalograms (EEG), magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI) [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

Sun

Wang

Niu

et al. 2020

Entropy

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is a degenerative brain disease with a high and irreversible incidence. In recent years, because brain signals have complex nonlinear dynamics, there has been growing interest in studying complex changes in the time series of brain signals in patients with AD. We reviewed studies of complexity analyses of single-channel time series from electroencephalogram (EEG), magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI) in AD and determined future research directions. A systematic literature search for 2000–2019 was performed in the Web of Science and PubMed databases, resulting in 126 identified studies. Compared to healthy individuals, the signals from AD patients have less complexity and more predictable oscillations, which are found mainly in the left parietal, occipital, right frontal, and temporal regions. This complexity is considered a potential biomarker for accurately responding to the functional lesion in AD. The current review helps to reveal the patterns of dysfunction in the brains of patients with AD and to investigate whether signal complexity can be used as a biomarker to accurately respond to the functional lesion in AD. We proposed further studies in the signal complexities of AD patients, including investigating the reliability of complexity algorithms and the spatial patterns of signal complexity. In conclusion, the current review helps to better understand the complexity of abnormalities in the AD brain and provide useful information for AD diagnosis.

show abstract

Section: Introductionmentioning

confidence: 99%

Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

Sun

Wang

Niu

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…When the sparsity is above 40 the successful rates of four methods drop rapidly. Our method is a little higher and more stable than the other three methods [5,9,16,18,21,26,28,32].…”

Section: Figurementioning

confidence: 81%

Recovery of a Compressed Sensing CT Image Using a Smooth Re-weighted Function- Regularized Least-Squares Algorithm

Zhou¹,

Wei²,

Woźniak³

et al. 2019

ITC

Self Cite

View full text Add to dashboard Cite

It is challenging to recover the required compressed CT (Computed Tomography, CT) image, which is got by transferred through the internet or is stored in a signal library after being compressed. We present a recovery method for compressed sensing CT images. At present, minimizing 0-norm, 1-norm and p-norm is used to recover compressed sensing signals. However, sometimes 0-norm is an NP problem, 1-norm has no solution in theory and p-norm is not a convex function. We introduce a recovery method of compressed sensing signal based on regularized smooth convex optimization. In order to avoid solving the non-convex optimization problems and no solution condition, a convex function is designed as the objective function of optimization to fit 0-norm of signal and a fast iterative shrinkage-thresholding algorithm is proposed to find solution with the convergence speed is quadratic convergence. Experimental results show that our method has a sound recovery effect and is well suitable for processing big data of compressed CT images.

show abstract

“…The work presented by Bellver et al [5] used VGG-16 architecture as the base network in their work. Other work [11,16,29,32,[35][36] has used two-dimensional (2D) U-Net, which is designed mainly for medical image segmentation.…”

Section: Basic Conceptsmentioning

confidence: 99%

Liver Tumor Segmentation in CT Scans Using Modified SegNet

Almotairi

Kareem

Aouf

et al. 2020

Sensors

123

View full text Add to dashboard Cite

The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients’ death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder–decoder is named SegNet, and consists of a hierarchical correspondence of encode–decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase.

show abstract

Adaptive Independent Subspace Analysis of Brain Magnetic Resonance Imaging Data

Cited by 67 publications

References 30 publications

Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer’s Disease: A Review

Recovery of a Compressed Sensing CT Image Using a Smooth Re-weighted Function- Regularized Least-Squares Algorithm

Liver Tumor Segmentation in CT Scans Using Modified SegNet

Contact Info

Product

Resources

About