Resting-state electroencephalogram relevance state recognition of Parkinson's disease based on dynamic weighted symbolic mutual information and k-means clustering / 生物医学工程学杂志

At present, the incidence of Parkinson's disease (PD) is gradually increasing. This seriously affects the quality of life of patients, and the burden of diagnosis and treatment is increasing. However, the disease is difficult to intervene in early stage as early monitoring means are limited. Aiming to find an effective biomarker of PD, this work extracted correlation between each pair of electroencephalogram (EEG) channels for each frequency band using weighted symbolic mutual information and k-means clustering. The results showed that State1 of Beta frequency band ( P = 0.034) and State5 of Gamma frequency band ( P = 0.010) could be used to differentiate health controls and off-medication Parkinson's disease patients. These findings indicated that there were significant differences in the resting channel-wise correlation states between PD patients and healthy subjects. However, no significant differences were found between PD-on and PD-off patients, and between PD-on patients and healthy controls. This may provide a clinical diagnosis reference for Parkinson's disease.

Thalamus segmentation based on resting-state functional magnetic resonance imaging / 国际生物医学工程杂志

Objective To obtain an accurate and effective method for thalamus segmentation based on resting-state functional magnetic resonance imaging (fMRI).Methods Based on the fact that resting-state fMRI technique examined spatial synchronization of spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals indirectly reflect the neuronal and synaptic activity,the in-thalamus BOLD signal correlations were calculated,and then the k-means clustering algorithm was applied to obtain functional connectivity-based thalamus segmentation.Results The thalamus was divided into seven regions.Voxels within the same region were highly correlated with each other.The segmentation result was similar to that divided by functional connectivity between thalamus and the cerebral cortex.Conclusions Resting-state fMRI could provide not only the functional connectivity network between cortical and subcortical brain regions,but also the functional characteristics of thalamus.Segmentation algorithm using only internal information of thalamus shows lower computational complexity and higher processing speed than that based on the functional connectivity between thalamus and the cerebral cortex.

Harris Operator and K-means Clustering-based Facial Features Localization on Infrared Images / 航天医学与医学工程

Objective To develop an image analyzing procedure for automatic localization of facial features on infrared images.Methods An unsupervised local and global features extraction method was adopted for the localization of facial features of frontal view face image.First,a threshold was used to segment the image into foreground and background,and the nose was localized by considering the symmetry of the face.Second,Harris operator was adopted to detect interest points in a rectangular area covering all the facial features,and then local maximum of the interest points were detected.And finally,K-means clustering method was used to cluster the points and obtain the facial features localization.Results The experimental result of 100 images demonstrated that the procedure could automatically localize eyes,nose,mouth,and distinguish the feature areas.Conclusion The proposed infrared image analyzing procedure based on Harris operator and K-means clustering can be used to locate facial features on infrared image more rapidly and reliablely.