Qingqiang Wu

Co-word analysis of the trends in stem cells field based on subject heading weighting

In this paper, co-word analysis is used to analyze the evolvement in stem cell field. Articles in the stem cell journals are downloaded from PubMed for analysis. Terms selection is one of the most important steps in co-word analysis, so the useless and the general subject headings are removed firstly, and then the major subject headings and minor subject headings are weighted respectively. Then, improved information entropy is exploited to select the subject headings with the experts consulting. Hierarchical cluster analysis is used to cluster the subject headings and the strategic diagram is formed to analyze the evolutionary trends in the stem cell field.

Microarray data classification based on ensemble independent component selection

Independent component analysis (ICA) has been widely deployed to the analysis of microarray datasets. Although it was pointed out that after ICA transformation, different independent components (ICs) are of different biological significance, the IC selection problem is still far from fully explored. In this paper, we propose a genetic algorithm (GA) based ensemble independent component selection (EICS) system. In this system, GA is applied to select a set of optimal IC subsets, which are then used to build diverse and accurate base classifiers. Finally, all base classifiers are combined with majority vote rule. To show the validity of the proposed method, we apply it to classify three DNA microarray data sets involving various human normal and tumor tissue samples. The experimental results show that our ensemble method obtains stable and satisfying classification results when compared with several existing methods.

Topic evolution based on LDA and HMM and its application in stem cell research

This paper analyses topic segmentation based on the LDA (Latent Dirichlet Allocation) model, and performs the topic segmentation and topic evolution of stem cell research literatures in PubMed from 2001 to 2012 by combining the HMM (Hidden Markov Model) and co-occurrence theory. Stem cell research topics were obtained with LDA and expert judgements made on these topics to test the feasibility of the model classification. Further, the correlation between topics was analysed. HMM was used to predict the trend evolution of topics over various years, and a time series map was used to visualize the evolutional relationships among the stem cell topics.

3D vasculature segmentation using localized hybrid level-set method

BackgroundIntensity 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.MethodsThis 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.ResultsExperiments 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.ConclusionsExperimental results and validations demonstrate that our proposed model can achieve more promising segmentation results than the original hybrid method does.

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

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.

LDA-based model for topic evolution mining on text

A text mining model for topical evolutionary analysis was proposed through a text latent semantic analysis process on textual data. Analyzing topic evolution through tracking the topic different trends over time. Using the LDA model for the corpus and text to get the topics, and then using Clarity algorithm to measure the similarity of topics in order to identify topic mutation and discover the topic hidden in the text. Experiments show that the proposed model can discover meaningful topical evolution.

An improved hash-based RFID two-way security authentication protocol and application in remote education

With the increasingly expanding application of RFID technology, the security and privacy issues of RFID system has also been a widespread concern. The existing RFID security protocol can’t simultaneously meet the requirements of low-cost, low computing, high efficiency and high security. In this paper, through the analysis of common principles and shortcomings of RFID security protocols based on Hash Function, we improve the security protocols, so as to achieve the objectives of two-way authentication. This paper theoretically proves the protocol security through BAN logic. And the new protocols can effectively solve the security and privacy problems such as replay attack, fake attack, location privacy, anonymous tags and so on. The paper proposes a kind of authentication and communication security mechanism that makes full use of the functions of the conditional Access Module (CAM) which exist in the original remote education system and when authenticating the nodes of the system, we use the proposed security scheme. Analysis showed that the mechanism is safe, reliable, strong compatibility, economic and applicable.

An implicit skeleton-based method for the geometry reconstruction of vasculatures

Due to the high complexity of vascular system network, the geometry reconstruction of vasculatures from raw medical datasets remains a very challenging task. In this paper, we present a novel skeleton-based method for the geometry reconstruction of vascular structures from standard 3D medical datasets. With the proposed techniques, the geometry of vascular structures with high level of smoothness and accuracy can be reconstructed from the raw medical datasets. The experimental results and comparison with other techniques demonstrate that our method can achieve faithful and smooth vascular structures. In addition, quantitative validation has been conducted to evaluate the accuracy and smoothness of the reconstructed vessel geometry based on the proposed method.

K-State automaton burst detection model based on KOS: Emerging trends in cancer field

Burst topic detection aims to extract rapidly emerging topics from large volumes of text streams, including scientific literature. Currently there are several burst models and detection algorithms based on different burst definitions, which share the common deficiency that semantic information of topics is not taken into consideration, which results in noisy bursts in identified burst topics. In this paper, a K-state automaton burst detection model based on a KOS (knowledge organization system) is proposed and applied in detecting emerging trends and burst topics in the cancer field. Experiments showed that the K-state automaton burst detection model can better represent the variety of bursts and detect burst concepts with maximal confidence. Furthermore, the application of KOS in the process of concept extraction could effectively remove noisy concepts and enhance the accuracy of identifying burst concepts.

The extraction of vascular axis based on signed distance function

This paper presents a simple and fast algorithm to extract the skeleton of vascular structures from segmented vessel datasets. Our algorithm is based on a step by step approach to move a small volume of interest along the vessel tree. With the introduction of Signed Distance Function (SDF), the moving sphere along the vessel tree can easily and automatically detect bifurcations and predict the location of next axis point. Some experiments have been carried out to demonstrate the strengths of our proposed method.