To extract region of interests （ROI） in brain magnetic resonance imaging （MRI） with more than two objects and improve the segmentation accuracy, a hybrid model of a kemel-based fuzzy c-means （KFCM） clustering al...To extract region of interests （ROI） in brain magnetic resonance imaging （MRI） with more than two objects and improve the segmentation accuracy, a hybrid model of a kemel-based fuzzy c-means （KFCM） clustering algorithm and Chan-Vese （CV） model for brain MRI segmentation is proposed. The approach consists of two succes- sive stages. Firstly, the KFCM is used to make a coarse segmentation, which achieves the automatic selection of initial contour. Then an improved CV model is utilized to subdivide the image. Fuzzy membership degree from KFCM clus- tering is incorporated into the fidelity term of the 2-phase piecewise constant CV model to obtain accurate multi-object segmentation. Experimental results show that the proposed model has advantages both in accuracy and in robustness to noise in comparison with fuzzy c-means （FCM） clustering, KFCM, and the hybrid model of FCM and CV on brain MRI segmentation.展开更多
A novel method based on machine learning is developed to estimate event-related potentials from single trial electroencephalography. This paper builds a basic framework using classification and an optimization model b...A novel method based on machine learning is developed to estimate event-related potentials from single trial electroencephalography. This paper builds a basic framework using classification and an optimization model based on this framework for estimating event-related potentials. Then the SingleTrialEM algorithm is derived by introducing a logistic regression model, which could be obtained by training before SingleTrialEM is used, to instantiate the optimization model. The simulation tests demonstrate that the proposed method is correct and solid. The advantage of this method is verified by the comparison between this method and the Woody filter in simulation tests. Also, the cognitive test results are consistent with the conclusions of cognitive science.展开更多
The orchestrated expression of thousands of genes gives rise to the complexity of the human brain.However,the structures governing these myriad gene-gene interactions remain unclear.By analyzing transcription data fro...The orchestrated expression of thousands of genes gives rise to the complexity of the human brain.However,the structures governing these myriad gene-gene interactions remain unclear.By analyzing transcription data from more than 2000 sites in six human brains,we found that pairwise interactions between genes,without considering any higher-order interactions,are sufficient to predict the transcriptional pattern of the genome for individual brain regions and the transcriptional profile of the entire brain consisting of more than 200 areas.These findings suggest a quadratic complexity of transcriptional patterns in the human brain,which is much simpler than expected.In addition,using a pairwise interaction model,we revealed that the strength of gene-gene interactions in the human brain gives rise to the nearly maximal number of transcriptional clusters,which may account for the functional and structural richness of the brain.展开更多
基金Supported by National Natural Science Foundation of China (No. 60872065)
文摘To extract region of interests （ROI） in brain magnetic resonance imaging （MRI） with more than two objects and improve the segmentation accuracy, a hybrid model of a kemel-based fuzzy c-means （KFCM） clustering algorithm and Chan-Vese （CV） model for brain MRI segmentation is proposed. The approach consists of two succes- sive stages. Firstly, the KFCM is used to make a coarse segmentation, which achieves the automatic selection of initial contour. Then an improved CV model is utilized to subdivide the image. Fuzzy membership degree from KFCM clus- tering is incorporated into the fidelity term of the 2-phase piecewise constant CV model to obtain accurate multi-object segmentation. Experimental results show that the proposed model has advantages both in accuracy and in robustness to noise in comparison with fuzzy c-means （FCM） clustering, KFCM, and the hybrid model of FCM and CV on brain MRI segmentation.
基金supported by the National Natural Science Foundation of China (Grant No. 30670669)National Basic Research Program of China (Grant No. 2007CB947703)+1 种基金Natural Science Foundation of Fujian Province (Grant No. 2011J01344)Science and Technology Development Foundation of Fuzhou University (Grant No. 2009-XQ-25)
文摘A novel method based on machine learning is developed to estimate event-related potentials from single trial electroencephalography. This paper builds a basic framework using classification and an optimization model based on this framework for estimating event-related potentials. Then the SingleTrialEM algorithm is derived by introducing a logistic regression model, which could be obtained by training before SingleTrialEM is used, to instantiate the optimization model. The simulation tests demonstrate that the proposed method is correct and solid. The advantage of this method is verified by the comparison between this method and the Woody filter in simulation tests. Also, the cognitive test results are consistent with the conclusions of cognitive science.
基金the National Key Research and Development Program of China(2017YFA0105203)the National Natural Science Foundation of China(81671855)+1 种基金Strategic Priority Research Program of the Chinese Academy of Sciences(XDB32040200)Beijing Academy of Artificial Intelligence,and Beijing Advanced Discipline Fund。
文摘The orchestrated expression of thousands of genes gives rise to the complexity of the human brain.However,the structures governing these myriad gene-gene interactions remain unclear.By analyzing transcription data from more than 2000 sites in six human brains,we found that pairwise interactions between genes,without considering any higher-order interactions,are sufficient to predict the transcriptional pattern of the genome for individual brain regions and the transcriptional profile of the entire brain consisting of more than 200 areas.These findings suggest a quadratic complexity of transcriptional patterns in the human brain,which is much simpler than expected.In addition,using a pairwise interaction model,we revealed that the strength of gene-gene interactions in the human brain gives rise to the nearly maximal number of transcriptional clusters,which may account for the functional and structural richness of the brain.
