Xiantong Zhen | The University of Sheffield (original) (raw)

Xiantong Zhen

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Papers by Xiantong Zhen

Research paper thumbnail of Human action representation using pyramid correlogram of oriented gradients on motion history images

International Journal of Computer Mathematics, 2011

The representation of human actions in video sequences is one of the key steps in action classifi... more The representation of human actions in video sequences is one of the key steps in action classification and recognition, performances of which are greatly dependent on the distinctiveness and robustness of the descriptors used for representation. In this paper, a novel descriptor, named pyramid correlogram of oriented gradients (PCOG), is presented for feature representation. PCOG, combined with the motion history images, captures both shape and spatial layout of the motion and therefore gives more effective and powerful representation for human actions and can be used for the detection and recognition of a variety of actions. Experiments on challenging action data sets show that PCOG performs significantly better than the histogram of oriented gradients both as a global descriptor and as a local descriptor.

Research paper thumbnail of Effects of BDNF Val66Met polymorphism on brain metabolism in Alzheimerʼs disease

Research paper thumbnail of Cortical thickness is associated with different apolipoprotein E genotypes in healthy elderly adults

Neuroscience Letters, 2010

Research paper thumbnail of Tracking progression from mild cognitive impairment to Alzheimer's disease using multivariate biomarkers and pattern classification

Alzheimers & Dementia, 2011

molecules. The interaction of both labelled donor and acceptor molecules induced a fluorescence a... more molecules. The interaction of both labelled donor and acceptor molecules induced a fluorescence at 665nm proportional to the sAPP alpha level. A recombinant protein was used as standard. We investigated the CSF from 30 patients with AD, and 30 controls. CSFs were considered as AD profile according to tau protein, phosphorylated tau and Aß 1-42 results, as previously described (1). Excluded patients on the basis of CSF markers levels were considered as controls. Results: The test did not recognize recombinant sAPPß protein and Aß1-42 peptide. Sample dilutions (1/5, 1/10, 1/20) allowed to verify the response linearity. The test detected 3 ng/ml of sAPP alpha. 5 ml of sample was sufficient for the CSF quantification. Using this test, we could observed a significant increase of sAPP alpha in the CSF of 30 AD patients comparatively to 30 controls (AD:530 6 36 ng/ml; controls: 393 6 28 ng/ml; p < 0.01). Interestingly, sAPP alpha was significantly correlated with Aß levels in control CSFs only (r2 :0.31; p < 0.001). No significant correlations were observed between tau or phosphorylated tau and sAPP alpha levels in CSF. Conclusions: We developed a very sensitive test for the quantification of sAPP alpha levels in human CSF. Using this test, we observed an increase of sAPP alpha level in the CSF of AD patients in absence of correlation with other markers contrary to that observed in controls. It will be interesting to investigate a larger population of AD and Mild Cognitive Impairment patients to study the putative correlations of sAPP alpha levels with the biomarkers and parameters of the disease. 1: DumurgierJ et al, (2010) NeurobiolDis. 40: 456-459

Research paper thumbnail of Identification of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Multivariate Predictors

PLOS One, 2011

Prediction of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of m... more Prediction of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of major interest in AD research. A large number of potential predictors have been proposed, with most investigations tending to examine one or a set of related predictors. In this study, we simultaneously examined multiple features from different modalities of data, including structural magnetic resonance imaging (MRI) morphometry, cerebrospinal fluid (CSF) biomarkers and neuropsychological and functional measures (NMs), to explore an optimal set of predictors of conversion from MCI to AD in an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. After FreeSurfer-derived MRI feature extraction, CSF and NM feature collection, feature selection was employed to choose optimal subsets of features from each modality. Support vector machine (SVM) classifiers were then trained on normal control (NC) and AD participants. Testing was conducted on MCIc (MCI individuals who have converted to AD within 24 months) and MCInc (MCI individuals who have not converted to AD within 24 months) groups. Classification results demonstrated that NMs outperformed CSF and MRI features. The combination of selected NM, MRI and CSF features attained an accuracy of 67.13%, a sensitivity of 96.43%, a specificity of 48.28%, and an AUC (area under curve) of 0.796. Analysis of the predictive values of MCIc who converted at different follow-up evaluations showed that the predictive values were significantly different between individuals who converted within 12 months and after 12 months. This study establishes meaningful multivariate predictors composed of selected NM, MRI and CSF measures which may be useful and practical for clinical diagnosis.

Research paper thumbnail of Human action representation using pyramid correlogram of oriented gradients on motion history images

International Journal of Computer Mathematics, 2011

The representation of human actions in video sequences is one of the key steps in action classifi... more The representation of human actions in video sequences is one of the key steps in action classification and recognition, performances of which are greatly dependent on the distinctiveness and robustness of the descriptors used for representation. In this paper, a novel descriptor, named pyramid correlogram of oriented gradients (PCOG), is presented for feature representation. PCOG, combined with the motion history images, captures both shape and spatial layout of the motion and therefore gives more effective and powerful representation for human actions and can be used for the detection and recognition of a variety of actions. Experiments on challenging action data sets show that PCOG performs significantly better than the histogram of oriented gradients both as a global descriptor and as a local descriptor.

Research paper thumbnail of Effects of BDNF Val66Met polymorphism on brain metabolism in Alzheimerʼs disease

Research paper thumbnail of Cortical thickness is associated with different apolipoprotein E genotypes in healthy elderly adults

Neuroscience Letters, 2010

Research paper thumbnail of Tracking progression from mild cognitive impairment to Alzheimer's disease using multivariate biomarkers and pattern classification

Alzheimers & Dementia, 2011

molecules. The interaction of both labelled donor and acceptor molecules induced a fluorescence a... more molecules. The interaction of both labelled donor and acceptor molecules induced a fluorescence at 665nm proportional to the sAPP alpha level. A recombinant protein was used as standard. We investigated the CSF from 30 patients with AD, and 30 controls. CSFs were considered as AD profile according to tau protein, phosphorylated tau and Aß 1-42 results, as previously described (1). Excluded patients on the basis of CSF markers levels were considered as controls. Results: The test did not recognize recombinant sAPPß protein and Aß1-42 peptide. Sample dilutions (1/5, 1/10, 1/20) allowed to verify the response linearity. The test detected 3 ng/ml of sAPP alpha. 5 ml of sample was sufficient for the CSF quantification. Using this test, we could observed a significant increase of sAPP alpha in the CSF of 30 AD patients comparatively to 30 controls (AD:530 6 36 ng/ml; controls: 393 6 28 ng/ml; p < 0.01). Interestingly, sAPP alpha was significantly correlated with Aß levels in control CSFs only (r2 :0.31; p < 0.001). No significant correlations were observed between tau or phosphorylated tau and sAPP alpha levels in CSF. Conclusions: We developed a very sensitive test for the quantification of sAPP alpha levels in human CSF. Using this test, we observed an increase of sAPP alpha level in the CSF of AD patients in absence of correlation with other markers contrary to that observed in controls. It will be interesting to investigate a larger population of AD and Mild Cognitive Impairment patients to study the putative correlations of sAPP alpha levels with the biomarkers and parameters of the disease. 1: DumurgierJ et al, (2010) NeurobiolDis. 40: 456-459

Research paper thumbnail of Identification of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Multivariate Predictors

PLOS One, 2011

Prediction of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of m... more Prediction of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of major interest in AD research. A large number of potential predictors have been proposed, with most investigations tending to examine one or a set of related predictors. In this study, we simultaneously examined multiple features from different modalities of data, including structural magnetic resonance imaging (MRI) morphometry, cerebrospinal fluid (CSF) biomarkers and neuropsychological and functional measures (NMs), to explore an optimal set of predictors of conversion from MCI to AD in an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. After FreeSurfer-derived MRI feature extraction, CSF and NM feature collection, feature selection was employed to choose optimal subsets of features from each modality. Support vector machine (SVM) classifiers were then trained on normal control (NC) and AD participants. Testing was conducted on MCIc (MCI individuals who have converted to AD within 24 months) and MCInc (MCI individuals who have not converted to AD within 24 months) groups. Classification results demonstrated that NMs outperformed CSF and MRI features. The combination of selected NM, MRI and CSF features attained an accuracy of 67.13%, a sensitivity of 96.43%, a specificity of 48.28%, and an AUC (area under curve) of 0.796. Analysis of the predictive values of MCIc who converted at different follow-up evaluations showed that the predictive values were significantly different between individuals who converted within 12 months and after 12 months. This study establishes meaningful multivariate predictors composed of selected NM, MRI and CSF measures which may be useful and practical for clinical diagnosis.

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