A non-invasive method has been developed to study and analyse the critical geometric characteristics of living human skulls based on CT scan images. The images were obtained from patients who admitted hospital needed a head diagnosis. A-P length breadth whole skull, thickness frontal, parietal occipital bones 3000 analysed in this study. results have shown that average thicknesses (in mm) 6.58, 5.37 7.56, respectively, for male; 7.48, 5.58 8.17, female. skull 175.81 145.35, male 170.61...

Disruptions in brain connectivity have been widely reported Alzheimer's disease (AD). Morphometric similarity (MS) mapping provides a new way of estimating structural by interregional correlation T1WI- and DTI-derived parameters within individual brains. Here, we aimed to identify AD-related MS changing patterns genes related the changes further explored molecular cellular mechanism underlying AD. Both 3D-T1WI DTI data 106 AD patients well-matched healthy elderly individuals from ADNI...

Mapping gene expression profiles to neuroimaging phenotypes in the same anatomical space provides opportunities discover molecular substrates for human brain functional properties. Here, we aimed identify cell-type-specific modules associated with regional homogeneity (ReHo) of spontaneous activity and their associations disorders. Fourteen were consistently ReHo three datasets, five which showed (one neuron-endothelial module, one neuron astrocyte module two microglial modules) independent...

We propose a novel fast iterative thresholding algorithm for image compressive sampling (CS) recovery using three existing denoisers—i.e., TV (total variation), wavelet, and BM3D (block-matching 3D filtering) denoisers. Through the use of recently introduced plug-and-play prior approach, we turn these denoisers into CS solvers. Thus, our method can jointly utilize global nonlocal sparsity images. The former is captured by wavelet maintaining entire consistency; while latter characterized...

Image recovery from compressive sensing (CS) measurement data, especially noisy data has always been challenging due to its implicit ill-posed nature, thus, seek a domain where signal can exhibit high degree of sparsity and design an effective algorithm have drawn increasingly more attention. Among various sparsity-based models, structured or group often leads powerful reconstruction techniques. In this paper, we propose novel entropy-based for CS enhance image through learning the residual....

Abstract Disruptions of brain connectivity have been widely reported in Alzheimer's disease (AD). Morphometric similarity (MS) mapping provides a new way estimating structural by inter-regional correlation T1WI and DTI derived parameters within individual brains. Here, we aimed to identify AD-related MS changing patterns genes related the changes further explore molecular cellular mechanism underlying AD. Both 3D-T1WI data 106 AD patients well-matched healthy elders from ADNI database were...

Compressive sensing (CS) has proven to be an efficient technique for accelerating magnetic resonance imaging (MRI) acquisition through breaking the Nyquist sampling limit. However, CS measurements are often corrupted by noise in process, which greatly reduces quality of reconstructed images and deteriorates performance follow-up diagnosis tasks. In this paper, we propose a novel iterative shrinkage-thresholding (IST) method based on enhanced Laplacian-scaled shrinkage operation robust CS-MRI...

Deep neural networks have achieved the most outstanding performance in compressed sensing magnetic resonance imaging (CS-MRI) reconstruction by learning potential structures of images from a large number training samples. However, required data comprising hundreds subjects are usually rare. In this article, we remedy problem transferring easy-to-get deep Gaussian denoisers trained with natural for artifact reduction iterative recovery process without use full-sampled MRI data. To end, first...

Deep neural networks have shown great potential in various low-level vision tasks, leading to several state-of-the-art image denoising techniques. Training a deep network supervised fashion usually requires the collection of number examples and consumption significant amount time. However, training samples is very difficult for some application scenarios, such as full-sampled data magnetic resonance imaging satellite remote sensing imaging. In this paper, we overcome problem lack by using an...

As a basic parameter of rock, the rock bridge angle plays an important role in maintaining stability masses. To study size effect on uniaxial compressive strength rocks, this paper adopts principle regression analysis and combines numerical simulation to carry out relevant research. The research results indicate that: (1) decreases with increase angle, showing power function relationship; (2) tends stabilize when is greater than 350 mm, significant effect. (3) fluctuation coefficient...

Due to the natural conditions and other reasons, new energy accommodation capacity of Jiangxi Province has always been poor. In recent years, actively carried out reform construction electricity market system achieved a series achievements. Based on entropy weight method TOPSIS method, this article comprehensively evaluates in three years from perspectives power generation, transmission, storage, load. The evaluate results prove that Province's improved takes efforts.

This paper presents a variant of the iterative shrinkage-thresholding (IST) algorithm, called backtracking-based adaptive IST (BAIST), for image compressive sensing (CS) reconstruction. For increasing iterations, usually yields smoothing solution and runs into prematurity. To add back more details, BAIST method backtracks to previous noisy using L2 norm minimization, i.e., minimizing Euclidean distance between current ones. Through this modification, achieves superior performance while...

Nonlocal methods have shown great potential in many image restoration tasks including compressive sensing (CS) reconstruction through use of self-similarity prior. However, they are still limited recovering fine-scale details and sharp features, when rich repetitive patterns cannot be guaranteed; moreover the CS measurements corrupted. In this paper, we propose a novel recovery algorithm that combines nonlocal sparsity with local global prior, which soften complement assumption for irregular...

Existing model-based or data-driven methods have achieved a high-quality reconstruction in compressive sensing magnetic resonance imaging (CS-MRI). However, most are designed for specific type of sampling mask rate while ignoring the existence external noise, resulting poor robustness. In this work, we propose probabilistic method based on Laplacian scale mixture (LSM) modeling and denoising approximate message passing (D-AMP) algorithm to address issue. Sparse coefficients similar packed...

Image denoising based on deep learning has made more extensive development by using a large amount of data for network training, however, it is difficult to obtain clean images without noise in actual scenarios, leading the emergence self-supervised technique. In this step, we raise two questions: how improve performance and adaptive method various networks. response these issues, propose image scheme which generates training pairs subsampling noisy twice, combines U-Net ResNet form an...

To modify the security of Plus-Minus 1 algorithm used in JPEG steganography (J-PM1), a new method is proposed based on adaptive flipping probability estimation. First, least square matching(LSM) introduced to calculate each coefficient by minimizing difference between original distribution (i.e. histogram) and that stego image. Second, plus or minus (PM1) operation performed host signal according probabilities. Thus embedded bits could be more imperceptible great extent. Experimental results...