3D vasculature segmentation using localized hybrid level-set method

Background: Intensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image. Methods: This paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. The local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the preset global threshold based method, the use of automatically calculated local thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images. Results: Experiments carried out on the segmentation of 3D vessel images demonstrate the strengths of using locally specified dynamic thresholds in our level-set method. Furthermore, both qualitative comparison and quantitative validations have been performed to evaluate the effectiveness of our proposed model. Conclusions: Experimental results and validations demonstrate that our proposed model can achieve more promising segmentation results than the original hybrid method does

A Light Spot Humanoid Motion Paradigm Modulated by the Change of Brightness to Recognize the Stride Motion Frequency

The steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) usually has the advantages of high information transfer rate (ITR) and no need for training. However, low frequencies, such as the human stride motion frequency, cannot easily induce SSVEP. To solve this problem, a light spot humanoid motion paradigm modulated by the change of brightness was designed in this study. The characteristics of the brain response to the motion paradigm modulated by the change of brightness were analyzed for the first time. The results showed that the designed paradigm could induce not only the high flicker frequency but also the modulation frequencies between the change of brightness and the motion in the primary visual cortex. Thus, the stride motion frequency can be recognized through the modulation frequencies by using the designed paradigm. Also, in an online experiment, this paradigm was employed to control a lower limb robot to achieve same frequency stimulation, which meant that the visual stimulation frequency was the same as the motion frequency of the robot. Also, canonical correlation analysis (CCA) was used to distinguish three different stride motion frequencies. The average accuracies of the classification in three walking speeds using the designed paradigm with the same and different high frequencies reached 87 and 95% respectively. Furthermore, the angles of the knee joint of the robot were obtained to demonstrate the feasibility of the electroencephalograph (EEG)-driven robot with same stimulation

pT1-2 gastric cancer with lymph node metastasis predicted by tumor morphologic features on contrast-enhanced computed tomography

PURPOSETo investigate the value of tumor morphologic features of pT1-2 gastric cancer (GC) on contrast-enhanced computed tomography (CT) in assessing lymph node metastasis (LNM) with reference to histopathological results.METHODSEighty-six patients seen from October 2017 to April 2019 with pT1‐2 GC proven by histopathology were included. Tumor volume and CT densities were measured in the plain scan and the portal-venous phase (PVP), and the percent enhancement was calculated. The correlations between tumor morphologic features and the N stages were analyzed. The diagnostic capability of tumor volume and enhancement features in predicting the LN status of pT1-2 GCs was further investigated using receiver operating characteristic (ROC) analysis.RESULTSTumor volume, CT density in the PVP, and tumor percent enhancement in the PVP correlated significantly with the N stage (rho: 0.307, 0.558, and 0.586, respectively). Tumor volumes were significantly lower in the LNM− group than in the LNM+ group (14.4 mm3 vs. 22.6 mm3, P = 0.004). The differences between the LNM− and LNM+ groups in the CT density in the PVP and the percent enhancement in the PVP were also statistically significant (68.00 HU vs. 87.50 HU, P < 0.001; and 103.06% vs. 179.19%, P < 0.001, respectively). The area under the ROC curves for identifying the LNM+ group was 0.69 for tumor volume and 0.88 for percent enhancement in the PVP, respectively. The percent enhancement in the PVP of 145.2% and tumor volume of 17.4 mL achieved good diagnostic performance in determining LNM+ (sensitivity: 71.4%, 82.1%; specificity: 91.4%, 58.6%; and accuracy: 84.9%, 66.3%, respectively).CONCLUSIONTumor volume and percent enhancement in the PVP of pT1-2 GC could improve the diagnostic accuracy of LNM and would be helpful in image surveillance of these patients

Topic segmentation model based on ATNLDA and co-occurrence theory and its application in stem cell field

Natural Science Foundation of Fujian Province of China [2011J01360]; National Key Technology RD Program [2011BAH10B06-02]; Humanity and Social Science Youth foundation of Ministry of Education [11YJC870001, 11YJC870027]This paper describes the application of co-occurrence and latent Dirichlet allocation (LDA)-based topic analyses in stem cell-related literature research. On account of the deficiency of parameter estimation in LDA, this study integrated co-occurrence theory and clustering judgement indicators and constructed an ATNLDA (Auto Topic Number LDA) model for topic segmentation. Next, ATNLDA was used to determine the optimal topic number of stem cell research literatures from 2006 to 2011 in PubMed, which was then used for topic segmentation of research content in stem cell data set. After stem cell research topics were obtained, they were analysed in terms of topic label, topic research content and interrelation between topics. The results verified that application of ATNLDA in topic segmentation in stem cell literature research is effective and feasible. Current deficiencies of ATNLDA and future study plan were also discussed

Oil exploration data mining image processing

Conference Name:2012 7th International Conference on Computer Science and Education, ICCSE 2012. Conference Address: Melbourne, VIC, Australia. Time:July 14, 2012 - July 17, 2012.University of MelbourneIn this paper, an oil exploration software is designed, introduced and implementation. The software is based on the procedures of oil exploration and seismic prospecting. In the environment of Microsoft Visual Studio 2008 with the C++ programming language, the standard SEG-Y seismic data is successfully transferred into visible geological sectional view and histogram chart, which makes the geologists obtain a quick access to the idea of geophysics structures of the target field. With more than one kind of color schemes to be chosen, geologists can highlight different geological structure of the seismic data. Implementation shows good performance of the designed oil exploration data mining image processing software. 漏 2012 IEEE

GPU accelerating technique for rendering implicitly represented vasculatures

With the flooding datasets of medical Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), implicit modeling techniques are increasingly applied to reconstruct the human organs, especially the vasculature. However, displaying implicitly represented geometric objects arises heavy computational burden. In this study, a Graphics Processing Unit (GPU) accelerating technique was developed for high performance rendering of implicitly represented objects, especially the vasculatures. The experimental results suggested that the rendering performance was greatly enhanced via exploiting the advantages of modern GPUs. © 2014 - IOS Press and the authors. All rights reserved

Human action recognition based on kinematic similarity in real time.

Human action recognition using 3D pose data has gained a growing interest in the field of computer robotic interfaces and pattern recognition since the availability of hardware to capture human pose. In this paper, we propose a fast, simple, and powerful method of human action recognition based on human kinematic similarity. The key to this method is that the action descriptor consists of joints position, angular velocity and angular acceleration, which can meet the different individual sizes and eliminate the complex normalization. The angular parameters of joints within a short sliding time window (approximately 5 frames) around the current frame are used to express each pose frame of human action sequence. Moreover, three modified KNN (k-nearest-neighbors algorithm) classifiers are employed in our method: one for achieving the confidence of every frame in the training step, one for estimating the frame label of each descriptor, and one for classifying actions. Additional estimating of the frame's time label makes it possible to address single input frames. This approach can be used on difficult, unsegmented sequences. The proposed method is efficient and can be run in real time. The research shows that many public datasets are irregularly segmented, and a simple method is provided to regularize the datasets. The approach is tested on some challenging datasets such as MSR-Action3D, MSRDailyActivity3D, and UTD-MHAD. The results indicate our method achieves a higher accuracy

Deep Tunnel for Regulating Combined Sewer Overflow Pollution and Flood Disaster: A Case Study in Guangzhou City, China

The DongHaoChong (DHC) basin is located in the central city zone of Guangzhou City, China. Owing to the high density of buildings and low quality of the drainage pipe network in the city, diversion of rain and sewage is difficult. Waterlogging occurs frequently and combined sewer overflow (CSO) pollution is a serious problem during the rainy season. Therefore, a deep tunnel for the DongHaoChong basin has been planned and its construction is currently underway. An urban rainstorm model for the DongHaoChong basin was developed on the basis of the Storm Water Management Model (SWMM), and both the interception effect of CSO pollution and the degree of mitigation of flood were analyzed. Reasonable scenarios for the deep tunnel in terms of rainstorms with different design recurrence periods were evaluated. From the viewpoints of preventing rainstorm waterlogging disasters and protecting water quality in the region downstream of DongHaoChong River, the river flood control and drainage capacities of the region were improved to a 2-year rainstorm design recurrence period by the construction of the deep tunnel. Furthermore, the main pollutant load of the CSO is expected to be reduced by about 30%–40%