Guopeng Zhang

Impaired Frontal-Basal Ganglia Connectivity in Adolescents with Internet Addiction

Understanding the neural basis of poor impulse control in Internet addiction (IA) is important for understanding the neurobiological mechanisms of this syndrome. The current study investigated how neuronal pathways implicated in response inhibition were affected in IA using a Go-Stop paradigm and functional magnetic resonance imaging (fMRI). Twenty-three control subjects aged 15.2 ± 0.5 years (mean ± S.D.) and eighteen IA subjects aged 15.1 ± 1.4 years were studied. Effective connectivity within the response inhibition network was quantified using (stochastic) dynamic causal modeling (DCM). The results showed that the indirect frontal-basal ganglia pathway was engaged by response inhibition in healthy subjects. However, we did not detect any equivalent effective connectivity in the IA group. This suggests the IA subjects fail to recruit this pathway and inhibit unwanted actions. This study provides a clear link between Internet addiction as a behavioral disorder and aberrant connectivity in the response inhibition network.

Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography

PurposeDifferentiation of colon lesions according to underlying pathology, e.g., neoplastic and non-neoplastic lesions, is of fundamental importance for patient management. Image intensity-based textural features have been recognized as useful biomarker for the differentiation task. In this paper, we introduce texture features from higher-order images, i.e., gradient and curvature images, beyond the intensity image, for that task.MethodsBased on the Haralick texture analysis method, we introduce a virtual pathological model to explore the utility of texture features from high-order differentiations, i.e., gradient and curvature, of the image intensity distribution. The texture features were validated on a database consisting of 148 colon lesions, of which 35 are non-neoplastic lesions, using the support vector machine classifier and the merit of area under the curve (AUC) of the receiver operating characteristics.ResultsThe AUC of classification was improved from 0.74 (using the image intensity alone) to 0.85 (by also considering the gradient and curvature images) in differentiating the neoplastic lesions from non-neoplastic ones, e.g., hyperplastic polyps from tubular adenomas, tubulovillous adenomas and adenocarcinomas.ConclusionsThe experimental results demonstrated that texture features from higher-order images can significantly improve the classification accuracy in pathological differentiation of colorectal lesions. The gain in differentiation capability shall increase the potential of computed tomography colonography for colorectal cancer screening by not only detecting polyps but also classifying them for optimal polyp management for the best outcome in personalized medicine.

Characterization of Texture Features of Bladder Carcinoma and the Bladder Wall on MRI: Initial Experience

RATIONALE AND OBJECTIVES The purpose of this study was to determine textural features that show a significant difference between carcinomatous tissue and the bladder wall on magnetic resonance imaging (MRI) and explore the feasibility of using them to differentiate malignancy from the normal bladder wall as an initial step for establishing MRI as a screening modality for the noninvasive diagnosis of bladder cancer. MATERIALS AND METHODS Regions of interest (ROIs) were manually placed on foci of bladder cancer and uninvolved bladder wall in 22 patients and on the normal bladder wall of 23 volunteers to calculate 40 known textural features. Statistical analysis was applied to determine the difference in these features in bladder cancer versus uninvolved bladder wall versus normal bladder wall of volunteers. The significantly different features were then analyzed using a support vector machine (SVM) classifier to determine their accuracy in differentiating malignancy from the bladder wall. RESULTS Thirty-three of 40 features show significant differences between bladder cancer and the bladder wall. Nine of 40 features were significantly different in uninvolved bladder wall of patients versus normal bladder wall of volunteers. Further study indicates that seven of these 33 features were significantly different between uninvolved bladder wall of patients with early cancer and that of volunteers, whereas 15 of 33 features were different between that of patients with advanced cancer and normal wall. With the testing dataset consisting of ROIs acquired from patients, the classification accuracy using 33 textural features fed into the SVM classifier was 86.97%. CONCLUSION The initial experience demonstrates that texture features are sensitive to reveal the differences between bladder cancer and the bladder wall on MRI. The different features can be used to develop a computer-aided system for the evaluation of the entire bladder wall.

A new 3D texture feature based computer-aided diagnosis approach to differentiate pulmonary nodules

To distinguish malignant pulmonary nodules from benign ones is of much importance in computer-aided diagnosis of lung diseases. Compared to many previous methods which are based on shape or growth assessing of nodules, this proposed three-dimensional (3D) texture feature based approach extracted fifty kinds of 3D textural features from gray level, gradient and curvature co-occurrence matrix, and more derivatives of the volume data of the nodules. To evaluate the presented approach, the Lung Image Database Consortium public database was downloaded. Each case of the database contains an annotation file, which indicates the diagnosis results from up to four radiologists. In order to relieve partial-volume effect, interpolation process was carried out to those volume data with image slice thickness more than 1mm, and thus we had categorized the downloaded datasets to five groups to validate the proposed approach, one group of thickness less than 1mm, two types of thickness range from 1mm to 1.25mm and greater than 1.25mm (each type contains two groups, one with interpolation and the other without). Since support vector machine is based on statistical learning theory and aims to learn for predicting future data, so it was chosen as the classifier to perform the differentiation task. The measure on the performance was based on the area under the curve (AUC) of Receiver Operating Characteristics. From 284 nodules (122 malignant and 162 benign ones), the validation experiments reported a mean of 0.9051 and standard deviation of 0.0397 for the AUC value on average over 100 randomizations.

Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI

PurposeThis study aims to determine the three-dimensional (3D) texture features extracted from intensity and high-order derivative maps that could reflect textural differences between bladder tumors and wall tissues, and propose a noninvasive, image-based strategy for bladder tumor differentiation preoperatively.MethodsA total of 62 cancerous and 62 wall volumes of interest (VOI) were extracted from T2-weighted MRI datasets of 62 patients with pathologically confirmed bladder cancer. To better reflect heterogeneous distribution of tumor tissues, 3D high-order derivative maps (the gradient and curvature maps) were calculated from each VOI. Then 3D Haralick features based on intensity and high-order derivative maps and Tamura features based on intensity maps were extracted from each VOI. Statistical analysis and recursive feature elimination-based support vector machine classifier (RFE-SVM) was proposed to first select the features with significant differences and then obtain a more predictive and compact feature subset to verify its differentiation performance.ResultsFrom each VOI, a total of 58 texture features were derived. Among them, 37 features showed significant inter-class differences (

Efficient 3D texture feature extraction from CT images for computer-aided diagnosis of pulmonary nodules

P\le 0.01

A dimension reduction strategy for improving the efficiency of computer-aided detection for CT colonography

P≤0.01). With 29 optimal features selected by RFE-SVM, the classification results namely the sensitivity, specificity, accuracy and area under the curve (AUC) of the receiver operating characteristics were 0.9032, 0.8548, 0.8790 and 0.9045, respectively. By using synthetic minority oversampling technique to augment the sample number of each group to 200, the sensitivity, specificity, accuracy an AUC value of the feature selection-based classification were improved to 0.8967, 0.8780, 0.8874 and 0.9416, respectively.ConclusionsOur results suggest that 3D texture features derived from intensity and high-order derivative maps can better reflect heterogeneous distribution of cancerous tissues. Texture features optimally selected together with sample augmentation could improve the performance on differentiating bladder carcinomas from wall tissues, suggesting a potential way for tumor noninvasive staging of bladder cancer preoperatively.

A feasibility study of high order volumetric texture features for computer aided diagnosis of polyps via CT colonography

Objective: To find an effective way for quantitative evaluation on wall thickness variation of human bladder with/without bladder tumor, a novel pipeline of thickness measurement and analysis for magnetic resonance (MR) cystography is proposed. Methods: After the acquisition of volumetric bladder images with a high-resolution T2-weighted 3-D sequence, the inner and outer borders of the bladder wall were segmented simultaneously by a coupled directional level-set method. Then, the bladder wall thickness (BWT) was estimated using the Laplacian method. To reducing the influence of individual variation and urine filling on wall thickness, a thickness normalization using Z-score is performed. Finally, a parametric surface mapping strategy was applied to map thickness distribution onto a unified sphere surface, for quantitative intra- and intersubject comparison between bladders of different shapes. Results: The proposed pipeline was tested with a database composed of MR bladder images acquired from 20 volunteers and 20 patients with bladder cancer. The results indicate that the thickness normalization step using Z-score makes the quantitative comparison of wall thickness quite possible and there is a significant difference on BWT between patients and volunteers. Using the proposed pipeline, we established a thickness template for a normal bladder wall based on dataset of all volunteers. Conclusion: As a first attempt to establish a general pipeline for bladder wall analysis, the presented work provides an effective way to achieve the goal of evaluating the entire bladder wall for detection and diagnosis of abnormality. In addition, it can be easily extended to quantitative analyses of other bladder features, such as, intensity-based or texture features.