Community detection methods have been used in computer, sociology, physics, biology, and brain information science areas. Many methods are based on the optimization of modularity. The algorithm proposed by Blondel et ...Community detection methods have been used in computer, sociology, physics, biology, and brain information science areas. Many methods are based on the optimization of modularity. The algorithm proposed by Blondel et al. (Blondel V D, Guillaume J L, Lambiotte R and Lefebvre E 2008 J. Star. Mech. 10 10008) is one of the most widely used methods because of its good performance, especially in the big data era. In this paper we make some improvements to this algorithm in correctness and performance. By tests we see that different node orders bring different performances and different community structures. We find some node swings in different communities that influence the performance. So we design some strategies on the sweeping order of node to reduce the computing cost made by repetition swing. We introduce a new concept of overlapping degree (OV) that shows the strength of connection between nodes. Three improvement strategies are proposed that are based on constant OV, adaptive OV, and adaptive weighted OV, respectively. Experiments on synthetic datasets and real datasets are made, showing that our improved strategies can improve the performance and correctness.展开更多
Sign recognition is important for identifying benign and malignant nodules. This paper proposes a new sign recognition method based on image retrieval for lung nodules. First, we construct a deep learning framework to...Sign recognition is important for identifying benign and malignant nodules. This paper proposes a new sign recognition method based on image retrieval for lung nodules. First, we construct a deep learning framework to extract semantic features that can effectively represent sign information. Second, we translate the high-dimensional image features into compact binary codes with principal component analysis (PCA) and supervised hashing. Third, we retrieve similar lung nodule images with the presented adaptive-weighted similarity calculation method. Finally, we recognize nodule signs from the retrieval results, which can also provide decision support for diagnosis of lung lesions. The proposed method is validated on the publicly available databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and lung computed tomography (CT) imaging signs (LISS). The experimental results demonstrate our retrieval method substantially improves retrieval performance compared with those using traditional Hamming distance, and the retrieval precision can achieve 87.29%when the length of hash code is 48 bits. The entire recognition rate on the basis of the retrieval results can achieve 93.52%. Moreover, our method is also effective for real-life diagnosis data.展开更多
Depression is closely linked to the morphology and functional abnormalities of multiple brain regions; however, its topological structure throughout the whole brain remains unclear. We col- lected resting-state functi...Depression is closely linked to the morphology and functional abnormalities of multiple brain regions; however, its topological structure throughout the whole brain remains unclear. We col- lected resting-state functional MRI data from 36 first-onset unmedicated depression patients and 27 healthy controls. The resting-state functional connectivity was constructed using the Auto- mated Anatomical Labeling template with a partial correlation method. The metrics calculation and statistical analysis were performed using complex network theory. The results showed that both depressive patients and healthy controls presented typical small-world attributes. Compared with healthy controls, characteristic path length was significantly shorter in depressive patients, suggesting development toward randomization. Patients with depression showed apparently abnormal node attributes at key areas in cortical-striatal-pallidal-thalamic circuits. In addition, right hippocampus and right thalamus were closely linked with the severity of depression. We se- lected 270 local attributes as the classification features and their P values were regarded as criteria for statistically significant differences. An artificial neural network algorithm was applied for classification research. The results showed that brain network metrics could be used as an effec- tive feature in machine learning research, which brings about a reasonable application prospect for brain network metrics. The present study also highlighted a significant positive correlation between the importance of the attributes and the intergroup differences; that is, the more sig- nificant the differences in node attributes, the stronger their contribution to the classification. Experimental findings indicate that statistical significance is an effective quantitative indicator of the selection of brain network metrics and can assist the clinical diagnosis of depression.展开更多
Brain structure and cognitive function change in the temporal lobe, hippocampus, and prefrontal cortex of patients with mild cognitive impairment and Alzheimer's disease, and brain network-connection strength, networ...Brain structure and cognitive function change in the temporal lobe, hippocampus, and prefrontal cortex of patients with mild cognitive impairment and Alzheimer's disease, and brain network-connection strength, network efficiency, and nodal attributes are abnormal. However, existing research has only analyzed the differences between these patients and normal controls. In this study, we constructed brain networks using resting-state functional MRI data that was extracted from four populations (nor- mal controls, patients with early mild cognitive impairment, patients with late mild cognitive impairment, and patients with Alzheimer's disease) using the Alzheimer's Disease Neuroimaging Initiative data set. The aim was to analyze the characteristics of resting-state functional neural networks, and to observe mild cognitive impairment at different stages before the transformation to Alzheimer's disease. Results showed that as cognitive deficits increased across the four groups, the shortest path in the rest- ing-state functional network gradually increased, while clustering coefficients gradually decreased. This evidence indicates that dementia is associated with a decline of brain network efficiency. In addi- tion, the changes in functional networks revealed the progressive deterioration of network function across brain regions from healthy elderly adults to those with mild cognitive impairment and AIz- heimer's disease. The alterations of node attributes in brain regions may reflect the cognitive functions in brain regions, and we speculate that early impairments in memory, hearing, and language function can eventually lead to diffuse brain injury and other cognitive impairments.展开更多
The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function netw...The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging(f MRI)network. Finally, the result shows that the best model only is based on anatomical distance.展开更多
In the last two decades,neuroimaging techniques have made quite a splash in not only our general understanding of healthy brain working mechanisms but also in gaining a better understanding of cognitive system alterat...In the last two decades,neuroimaging techniques have made quite a splash in not only our general understanding of healthy brain working mechanisms but also in gaining a better understanding of cognitive system alterations in brain disorders,such as Alzheimer’s disease(AD),Parkinson’s disease(PD)and schizophrenia(SZ),bipolar disorder(BD),etc.展开更多
It remains a challenging task to segment images that are distorted by noise and intensity inhomogeneity.To overcome these problems, in this paper, we present a novel region-based active contour model based on local in...It remains a challenging task to segment images that are distorted by noise and intensity inhomogeneity.To overcome these problems, in this paper, we present a novel region-based active contour model based on local intensity information and a kernel metric. By introducing intensity information about the local region, the proposed model can accurately segment images with intensity inhomogeneity. To enhance the model's robustness to noise and outliers, we introduce a kernel metric as its objective functional. To more accurately detect boundaries, we apply convex optimization to this new model, which uses a weighted total-variation norm given by an edge indicator function. Lastly, we use the split Bregman iteration method to obtain the numerical solution. We conducted an extensive series of experiments on both synthetic and real images to evaluate our proposed method, and the results demonstrate significant improvements in terms of efficiency and accuracy, compared with the performance of currently popular methods.展开更多
Brain asymmetry,involving structural and functional differencesbetween the two hemispheres,is a major organizational principle ofthe human brain.The structural and functional connectivity withineach hemisphere defines...Brain asymmetry,involving structural and functional differencesbetween the two hemispheres,is a major organizational principle ofthe human brain.The structural and functional connectivity withineach hemisphere defines the hemispheric network or connectome.Elucidating left-right differences of the hemispheric network providesopportunities for brain asymmetry exploration.This review examinesthe asymmetry in the hemispheric white matter and functionalnetwork to assess health and brain disorders.In this article,the brain asymmetry in structural and functional connectivity includingnetwork topologies of healthy individuals,involving brain cognitivesystems and the development trend,is highlighted.Moreover,theabnormal asymmetry of the hemispheric network related to cognition changes in brain disorders,such as Alzheimer’s disease,schizophrenia,autism spectrum disorder,attention deficit hyperactivity disorder,and bipolar disorder,is presented.This review suggests that thehemispheric network is highly conserved for measuring human brain asymmetries and has potential in the study of the cognitivesystem and brain disorders.展开更多
To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scann...To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this pa- per. The proposed method consists of four main stages: image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, mid- dle, and bottom region of lung. Finally, corrosion and ex- pansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emis- sion tomography-computed tomography (PET-CT) data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and ac- curately.展开更多
基金Project supported by the Major State Basic Research Development Program of China (Grant Nos.2013CB329602 and 2012CB316303)the Science Research Foundation for the Returned Overseas Chinese Scholars,China (Grant No.2010-31)+1 种基金the International Collaborative Project of Shanxi Province,China (Grant No.2011081034)the National Natural Science Foundation of China (Grant Nos.61232010 and 61202215)
文摘Community detection methods have been used in computer, sociology, physics, biology, and brain information science areas. Many methods are based on the optimization of modularity. The algorithm proposed by Blondel et al. (Blondel V D, Guillaume J L, Lambiotte R and Lefebvre E 2008 J. Star. Mech. 10 10008) is one of the most widely used methods because of its good performance, especially in the big data era. In this paper we make some improvements to this algorithm in correctness and performance. By tests we see that different node orders bring different performances and different community structures. We find some node swings in different communities that influence the performance. So we design some strategies on the sweeping order of node to reduce the computing cost made by repetition swing. We introduce a new concept of overlapping degree (OV) that shows the strength of connection between nodes. Three improvement strategies are proposed that are based on constant OV, adaptive OV, and adaptive weighted OV, respectively. Experiments on synthetic datasets and real datasets are made, showing that our improved strategies can improve the performance and correctness.
基金This work was supported in part by the National Natural Science Foundation of China under Grant No. 61373100, the Virtual Reality Technology and Systems National Key Laboratory of Open Foundation of China under Grant No. BUAA-VR-16KF-13 and the Shanxi Scholarship Council of China under Grant No. 2016-038.
文摘Sign recognition is important for identifying benign and malignant nodules. This paper proposes a new sign recognition method based on image retrieval for lung nodules. First, we construct a deep learning framework to extract semantic features that can effectively represent sign information. Second, we translate the high-dimensional image features into compact binary codes with principal component analysis (PCA) and supervised hashing. Third, we retrieve similar lung nodule images with the presented adaptive-weighted similarity calculation method. Finally, we recognize nodule signs from the retrieval results, which can also provide decision support for diagnosis of lung lesions. The proposed method is validated on the publicly available databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and lung computed tomography (CT) imaging signs (LISS). The experimental results demonstrate our retrieval method substantially improves retrieval performance compared with those using traditional Hamming distance, and the retrieval precision can achieve 87.29%when the length of hash code is 48 bits. The entire recognition rate on the basis of the retrieval results can achieve 93.52%. Moreover, our method is also effective for real-life diagnosis data.
基金supported by the National Natural Science Foundation of China,No.61070077,61170136,61373101,81171290the Natural Science Foundation of Shanxi Province in China,No.2010011020-2,2011011015-4+3 种基金Programs for Science and Technology Social Development of Shanxi Province,No.20130313012-2Science and Technology Projects by Shanxi Provincial Ed-ucation Ministry,No.20121003Youth Fund by Taiyuan University of Technology,No.2012L014Youth Team Fund by Taiyuan University of Technology,No.2013T047
文摘Depression is closely linked to the morphology and functional abnormalities of multiple brain regions; however, its topological structure throughout the whole brain remains unclear. We col- lected resting-state functional MRI data from 36 first-onset unmedicated depression patients and 27 healthy controls. The resting-state functional connectivity was constructed using the Auto- mated Anatomical Labeling template with a partial correlation method. The metrics calculation and statistical analysis were performed using complex network theory. The results showed that both depressive patients and healthy controls presented typical small-world attributes. Compared with healthy controls, characteristic path length was significantly shorter in depressive patients, suggesting development toward randomization. Patients with depression showed apparently abnormal node attributes at key areas in cortical-striatal-pallidal-thalamic circuits. In addition, right hippocampus and right thalamus were closely linked with the severity of depression. We se- lected 270 local attributes as the classification features and their P values were regarded as criteria for statistically significant differences. An artificial neural network algorithm was applied for classification research. The results showed that brain network metrics could be used as an effec- tive feature in machine learning research, which brings about a reasonable application prospect for brain network metrics. The present study also highlighted a significant positive correlation between the importance of the attributes and the intergroup differences; that is, the more sig- nificant the differences in node attributes, the stronger their contribution to the classification. Experimental findings indicate that statistical significance is an effective quantitative indicator of the selection of brain network metrics and can assist the clinical diagnosis of depression.
基金sponsored by the National Natural Science Foundation of China,No.61070077,61170136,61373101the Natural Science Foundation of Shanxi Province,No.2011011015-4Beijing Postdoctoral Science Foundation,No.Q6002020201201
文摘Brain structure and cognitive function change in the temporal lobe, hippocampus, and prefrontal cortex of patients with mild cognitive impairment and Alzheimer's disease, and brain network-connection strength, network efficiency, and nodal attributes are abnormal. However, existing research has only analyzed the differences between these patients and normal controls. In this study, we constructed brain networks using resting-state functional MRI data that was extracted from four populations (nor- mal controls, patients with early mild cognitive impairment, patients with late mild cognitive impairment, and patients with Alzheimer's disease) using the Alzheimer's Disease Neuroimaging Initiative data set. The aim was to analyze the characteristics of resting-state functional neural networks, and to observe mild cognitive impairment at different stages before the transformation to Alzheimer's disease. Results showed that as cognitive deficits increased across the four groups, the shortest path in the rest- ing-state functional network gradually increased, while clustering coefficients gradually decreased. This evidence indicates that dementia is associated with a decline of brain network efficiency. In addi- tion, the changes in functional networks revealed the progressive deterioration of network function across brain regions from healthy elderly adults to those with mild cognitive impairment and AIz- heimer's disease. The alterations of node attributes in brain regions may reflect the cognitive functions in brain regions, and we speculate that early impairments in memory, hearing, and language function can eventually lead to diffuse brain injury and other cognitive impairments.
基金the National Natural Science Foundation of China(Nos.6117013661373101+3 种基金61472270 and61402318)the Natural Science Foundation of Shanxi(No.2014021022-5)the Special/Youth Foundation of Taiyuan University of Technology(No.2012L014)the Youth Team Fund of Taiyuan University of Technology(Nos.2013T047 and 2013T048)
文摘The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging(f MRI)network. Finally, the result shows that the best model only is based on anatomical distance.
文摘In the last two decades,neuroimaging techniques have made quite a splash in not only our general understanding of healthy brain working mechanisms but also in gaining a better understanding of cognitive system alterations in brain disorders,such as Alzheimer’s disease(AD),Parkinson’s disease(PD)and schizophrenia(SZ),bipolar disorder(BD),etc.
基金supported by the National Natural Science Foundation of China(No.61472270)
文摘It remains a challenging task to segment images that are distorted by noise and intensity inhomogeneity.To overcome these problems, in this paper, we present a novel region-based active contour model based on local intensity information and a kernel metric. By introducing intensity information about the local region, the proposed model can accurately segment images with intensity inhomogeneity. To enhance the model's robustness to noise and outliers, we introduce a kernel metric as its objective functional. To more accurately detect boundaries, we apply convex optimization to this new model, which uses a weighted total-variation norm given by an edge indicator function. Lastly, we use the split Bregman iteration method to obtain the numerical solution. We conducted an extensive series of experiments on both synthetic and real images to evaluate our proposed method, and the results demonstrate significant improvements in terms of efficiency and accuracy, compared with the performance of currently popular methods.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.62176177,61906130)the National Key R&D Program of China(Grant No.2018AAA0102604)+1 种基金the Natural Science Foundation of Shanxi(Grant No.20210302123112)the Research Project Supported by Shanxi Scholarship Council of China(Grant No.2021-039).
文摘Brain asymmetry,involving structural and functional differencesbetween the two hemispheres,is a major organizational principle ofthe human brain.The structural and functional connectivity withineach hemisphere defines the hemispheric network or connectome.Elucidating left-right differences of the hemispheric network providesopportunities for brain asymmetry exploration.This review examinesthe asymmetry in the hemispheric white matter and functionalnetwork to assess health and brain disorders.In this article,the brain asymmetry in structural and functional connectivity includingnetwork topologies of healthy individuals,involving brain cognitivesystems and the development trend,is highlighted.Moreover,theabnormal asymmetry of the hemispheric network related to cognition changes in brain disorders,such as Alzheimer’s disease,schizophrenia,autism spectrum disorder,attention deficit hyperactivity disorder,and bipolar disorder,is presented.This review suggests that thehemispheric network is highly conserved for measuring human brain asymmetries and has potential in the study of the cognitivesystem and brain disorders.
文摘To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this pa- per. The proposed method consists of four main stages: image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, mid- dle, and bottom region of lung. Finally, corrosion and ex- pansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emis- sion tomography-computed tomography (PET-CT) data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and ac- curately.