bluuebunny/crossref_metadata_2025 huggingface dataset:
Deformable image registration of CT images for automatic contour propagation in radiation therapy
Authors: Wu Qian , Cao Ruifen , Pei Xi , Jia Jing , Hu Liqin
Published: August 2015

Abstract

Radiotherapy treatment plan may be replanned due the changes of tumors and organs at risk (OARs) during the treatment. Deformable image registration (DIR) based Computed Tomography (CT) contour propagation in the routine clinical setting is expected to reduce time needed for necessary manual tumors and OARs delineations and increase the efficiency of replanning. In this study, a DIR method was developed for CT contour propagation. Prior structure delineations were incorporated into Demons DIR, which was represented by adding an intensity matching term of the delineated tissues pairs to the energy function of Demons. The performance of our DIR was evaluated with five clinical head-and-neck and five lung cancer cases. The experimental results verified the improved accuracy of the proposed registration method compared with conventional registration and Demons DIR.
A novel approach for arrhythmia diagnosis: Self-adaptive and distribution-free mode
Authors: Li Fenghuan , Zheng Dequan , Zhao Tiejun
Published: August 2015

Abstract

Arrhythmia diagnosis is very significant to ensure human health. In this paper, a new model is developed for arrhythmia diagnosis. A salient feature of the algorithm is a synergistic combination of statistical and fuzzy set-based techniques. It is distribution-free and is realized in an unsupervised mode. Arrhythmia diagnosis is viewed as a certain statistical hypothesis testing. ‘Abnormal’ is typically a much complex concept, so it can be described with the technology of fuzzy sets which bring a facet of robustness to the overall scheme and play an important role in the successive step of hypothesis testing. Intensive fuzzification is engaged in parameters determination which is self-adaptive and no parameter needs to be specified by the user. The algorithm is validated with a number of experiments, which prove its effectiveness for arrhythmia diagnosis.
Analysis of the sleep quality of elderly people using biomedical signals
Authors: Moreno-Alsasua L. , Garcia-Zapirain B. , Mendez-Zorrilla A.
Published: August 2015

Abstract

This paper presents a technical solution that analyses sleep signals captured by biomedical sensors to find possible disorders during rest. Specifically, the method evaluates electrooculogram (EOG) signals, skin conductance (GSR), air flow (AS), and body temperature. Next, a quantitative sleep quality analysis determines significant changes in the biological signals, and any similarities between them in a given time period. Filtering techniques such as the Fourier transform method and IIR filters process the signal and identify significant variations. Once these changes have been identified, all significant data is compared and a quantitative and statistical analysis is carried out to determine the level of a person’s rest. To evaluate the correlation and significant differences, a statistical analysis has been calculated showing correlation between EOG and AS signals (p=0,005), EOG, and GSR signals (p=0,037) and, finally, the EOG and Body temperature (p=0,04). Doctors could use this information to monitor changes within a patient.
The detection of long-range correlations of operation force and sEMG with multifractal detrended fluctuation analysis
Authors: Li Fan , Li Dongxu , Wang Chunhui , Chen Shanguang , Lv Ming , Wang Miao
Published: August 2015

Abstract

This paper explores the application of multifractal detrended fluctuation analysis (MF-DFA) on the nonlinear characteristics of correlation between operation force and surface electromyography (sEMG), which is an applied frontier of human neuromuscular system activity. We established cross-correlation functions between the signal of force and four typical sEMG time-frequency domain index sequences (force-sEMG cross-correlation sequences), and dealt with the sequences with MF-DFA. In addition, we demonstrated that the force-sEMG cross-correlation sequences have strong statistical self-similarity and the fractal characteristic of the signal spectrum is similar to 1/f noise or fractional Brownian motion.
A study on locating the sonic source of sinusoidal magneto-acoustic signals using a vector method
Authors: Zhang Shunqi , Zhou Xiaoqing , Ma Ren , Yin Tao , Liu Zhipeng
Published: August 2015

Abstract

Methods based on the magnetic-acoustic effect are of great significance in studying the electrical imaging properties of biological tissues and currents. The continuous wave method, which is commonly used, can only detect the current amplitude without the sound source position. Although the pulse mode adopted in magneto-acoustic imaging can locate the sonic source, the low measuring accuracy and low SNR has limited its application. In this study, a vector method was used to solve and analyze the magnetic-acoustic signal based on the continuous sine wave mode. This study includes theory modeling of the vector method, simulations to the line model, and experiments with wire samples to analyze magneto-acoustic (MA) signal characteristics. The results showed that the amplitude and phase of the MA signal contained the location information of the sonic source. The amplitude and phase obeyed the vector theory in the complex plane. This study sets a foundation for a new technique to locate sonic sources for biomedical imaging of tissue conductivity. It also aids in studying biological current detecting and reconstruction based on the magneto-acoustic effect.
Anticorrelated networks in resting-state fMRI-BOLD data
Authors: Liu Yadong , Huang Liangming , Li Ming , Zhou Zongtan , Hu Dewen
Published: August 2015

Abstract

In this work, abundant anticorrelated networks were successfully detected in rest-stating fMRI-BOLD data from 20 subjects. Spatial independent component analysis (sICA) method was applied at both individual and group levels. At the individual level, for each subject, 30 independent components (IC) were estimated, and each IC was transformed using Z-score mapping. The voxels with >5 and < -5 Z-score were denoted as positive signals (PS) and negative signals (NS) respectively. The correlation coefficients between the mean time series of the PS and NS voxels were computed; if the calculated coefficients were <-0.3, the PS and NS voxels were considered to form an anticorrelated PS-NS network. It was found that 36.5% of the ICs contained an anticorrelated PS-NS network. The spatiotemporal patterns of most PS-NS networks varied from subject to subject, but three networks displaying spatial patterns were comparably consistent among different subjects. For group-level analysis, no anticorrelated PS-NS networks were detected. Our results suggest that future investigations adopt a broader approach for negative BOLD signal characterization. Combined consideration of PS and NS systems help to better elucidate hemodynamic and neuronal brain behavior and further develop understanding of neural mechanisms of brain information processing.
An improved cerebral vessel extraction method for MRA images
Authors: Zou Hua , Zhang Wen , Wang Qian
Published: August 2015

Abstract

To improve the speed and accuracy of cerebral vessel extraction, a fast and robust method is proposed in this paper. First, volume data are divided into sub-volumes by using octree, and at the same time invalid volume data are eliminated. Second, fuzzy connectedness is introduced to achieve fast cerebral vessel segmentation from 3D MRA Images. The values of gradient and Laplacian transformation are then calculated to improve the accuracy of the distance field. Last, the center of gravity is utilized to refine the initial centerline to make it closer to the actual centerline of the vessel cavity. The experiment demonstrates that the proposed method can effectively improve the speed and precision of centerline extraction.
Automatic detection of regions of interest in breast ultrasound images based on local phase information
Authors: Wang Xin , Guo Yi , Wang Yuanyuan
Published: August 2015

Abstract

Due to the inherent speckling and low contrast of ultrasonic images, the accurate and efficient location of regions of interest (ROIs) is still a challenging task for breast ultrasound (BUS) computer-aided diagnosis (CAD) systems. In this paper, a fully automatic and efficient ROI generation approach is proposed. First, a BUS image is preprocessed to improve image quality. Second, a phase of max-energy orientation (PMO) image of the preprocessed image is calculated. Otsu’s threshold selection method is then used to binarize the preprocessed image, the phase image and the composed image obtained by adding and normalizing the set of two images. Finally, a region selection algorithm is developed to select the true tumor region from these three binary images before generating a final ROI. The method was validated on a BUS database with 168 cases (81 benign and 87 malignant); the accuracy, average precision rate and average recall rate are calculated and compared with conventional method. The results indicate that the proposed method is more accurate and efficient in locating ROIs.
Automatic brain MR image denoising based on texture feature-based artificial neural networks
Authors: Chang Yu-Ning , Chang Herng-Hua
Published: August 2015

Abstract

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.
Pathological brain detection based on wavelet entropy and Hu moment invariants
Authors: Zhang Yudong , Wang Shuihua , Sun Ping , Phillips Preetha
Published: August 2015

Abstract

With the aim of developing an accurate pathological brain detection system, we proposed a novel automatic computer-aided diagnosis (CAD) to detect pathological brains from normal brains obtained by magnetic resonance imaging (MRI) scanning. The problem still remained a challenge for technicians and clinicians, since MR imaging generated an exceptionally large information dataset. A new two-step approach was proposed in this study. We used wavelet entropy (WE) and Hu moment invariants (HMI) for feature extraction, and the generalized eigenvalue proximal support vector machine (GEPSVM) for classification. To further enhance classification accuracy, the popular radial basis function (RBF) kernel was employed. The 10 runs of k-fold stratified cross validation result showed that the proposed “WE + HMI + GEPSVM + RBF” method was superior to existing methods w.r.t. classification accuracy. It obtained the average classification accuracies of 100%, 100%, and 99.45% over Dataset-66, Dataset-160, and Dataset-255, respectively. The proposed method is effective and can be applied to realistic use.
An image-enhancement method based on variable-order fractional differential operators
Authors: Xu Mengjia , Yang Jinzhu , Zhao Dazhe , Zhao Hong
Published: August 2015

Abstract

In this study, we develop a new algorithm based on fractional operators of variable-order in order to enhance image quality. First, three kinds of popular high-order discrete formulas are adopted to obtain the coefficients, and subsequently, a mask optimization method for selecting the fractional order adaptively is applied to construct a variable-order fractional differential mask along with the coefficients generated from the first step. We carry out experiments on OCT thoracic aorta images and some nature images with low contrast and noise, demonstrating that the high-order discrete method leads to significantly better performance in enhancing the edge information nonlinearly compared to the standard first-order discrete method. Moreover, the optimized mask with variable-order of the fractional derivative not only can preserve the edge information of the processed images adequately, but it also effectively suppresses the noise in the smooth area.
Automatic calculation and visualization of nuclear density in whole slide images of hepatic histological sections
Authors: Nagase Akito , Takahashi Masanobu , Nakano Masayuki
Published: August 2015

Abstract

An automatic method for estimating the density of cell nuclei in whole slide images (WSI’s) of hepatic histological sections is presented. The method can be applied to hematoxylin-eosin (H&E) stained sections with slight histological atypism, such as early well-differentiated hepatocellular carcinoma (ewHCC). It is shown that measured nuclear density is affected by the nuclear size due to fragments of nuclei. This size-dependent problem has been solved by estimating the standard nuclear area for each image patch. The method extracts typical nuclei, which are used to automatically adjust the parameters, including the standard nuclear area. The method is robust for variations in contrast, color, and nuclear size. 40 image patches sampled from 20 WSI’s of surgical sections were used for accuracy evaluation. The mean absolute percentage error of estimated nuclear densities was 8.2%. It was also confirmed that the distributions of nuclear density were successfully estimated and visualized for all 20 WSI’s. The computation time for a WSI of typical surgical section (754 mm2, about 1,280,000 nuclei) was about 57 minutes on a PC.
Image manifold revealing for breast lesion segmentation in DCE-MRI
Authors: Hu Liang , Cheng Zhaoning , Wang Manning , Song Zhijian
Published: August 2015

Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used for breast lesion differentiation. Manual segmentation in DCE-MRI is difficult and open to viewer interpretation. In this paper, an automatic segmentation method based on image manifold revealing was introduced to overcome the problems of the currently used method. First, high dimensional datasets were constructed from a dynamic image series. Next, an embedded image manifold was revealed in the feature image by nonlinear dimensionality reduction technique. In the last stage, k-means clustering was performed to obtain final segmentation results. The proposed method was applied in actual clinical cases and compared with the gold standard. Statistical analysis showed that the proposed method achieved an acceptable accuracy, sensitivity, and specificity rates.
Fast iterative reconstruction for helical pinhole SPECT imaging
Authors: Huang Po-Chia , Hsu Ching-Han
Published: August 2015

Abstract

Pinhole SPECT for small animal has become a routine procedure in many applications of molecular biology and pharmaceutical development. There is an increasing demand in the whole body imaging of lab animals. A simple and direct solution is to scan the object along a helical trajectory, similar to a helical CT scan. The corresponding acquisition time can be greatly reduced, while the over-lapping and gap between consecutive bed positions can be avoided. However, helical pinhole SPECT inevitably leads to the tremendous increase in computational complexity when the iterative reconstruction algorithms are applied. We suggest a novel voxel-driven (VD) system model which can be integrated with geometric symmetries from helical trajectory for fast iterative image reconstruction. Such a model construction can also achieve faster calculation and lower storage requirement of the system matrix. Due to the independence among various symmetries, it permits parallel coding to further boost computation efficiency of forward/backward projection. From phantom study, the results also indicate that the proposed VD model can adequately model the helical pinhole SPECT scanner with manageable storage size of system matrix and clinically acceptable computation loading of reconstruction.
Static imaging of the electrical impedance tomography on cylinder physical phantom
Authors: Liu Ruigang , Fu Feng , You Fusheng , Shi Xuetao , Dong Xiuzhen
Published: August 2015

Abstract

Static imaging of the electrical impedance tomography can obtain the absolute electrical conductivity distribution at one section of the subject. The test is performed on a cylinder physical phantom in which slim rectangle, hollow cylinder, small rectangle or three cylinders are selected to simulate complex conductivity perturbation objects. The measurement data is obtained by a data acquisition system with 32 compound electrodes. A group of static images of conductivity distribution in the cylinder phantom are reconstructed by the modified Newton-Raphson algorithm with two kinds of regularization methods. The results show correct position, size, conductivity difference, and similar shape of the perturbation objects in the images.
Sparse coded image super-resolution using K-SVD trained dictionary based on regularized orthogonal matching pursuit
Authors: Sajjad Muhammad , Mehmood Irfan , Baik Sung Wook
Published: August 2015

Abstract

Image super-resolution (SR) plays a vital role in medical imaging that allows a more efficient and effective diagnosis process. Usually, diagnosing is difficult and inaccurate from low-resolution (LR) and noisy images. Resolution enhancement through conventional interpolation methods strongly affects the precision of consequent processing steps, such as segmentation and registration. Therefore, we propose an efficient sparse coded image SR reconstruction technique using a trained dictionary. We apply a simple and efficient regularized version of orthogonal matching pursuit (ROMP) to seek the coefficients of sparse representation. ROMP has the transparency and greediness of OMP and the robustness of the L1-minization that enhance the dictionary learning process to capture feature descriptors such as oriented edges and contours from complex images like brain MRIs. The sparse coding part of the K-SVD dictionary training procedure is modified by substituting OMP with ROMP. The dictionary update stage allows simultaneously updating an arbitrary number of atoms and vectors of sparse coefficients. In SR reconstruction, ROMP is used to determine the vector of sparse coefficients for the underlying patch. The recovered representations are then applied to the trained dictionary, and finally, an optimization leads to high-resolution output of high-quality. Experimental results demonstrate that the super-resolution reconstruction quality of the proposed scheme is comparatively better than other state-of-the-art schemes.
Geometric distortion evaluation using a multi-orientated water-phantom at 0.2 T MRI
Authors: Jao Jochi , Cheng Mingye , Lee Hsintien , Ting Julius , Chen Pochou
Published: August 2015

Abstract

Magnetic Resonance Imaging (MRI) has been one of the most revolutionary medical imaging modalities in the past three decades. It has been recognized as a potential technique in the clinical diagnosis of diseases as well as tumor differentiation. Although MRI has now become the preferred choice in many clinical examinations, there are some drawbacks, which still limit its applications. One of the crucial issues of MRI is the geometric distortion caused by magnetic field inhomogeneity and susceptibility effects. The farther the lesion from the center of a magnetic field (off-center field), the more severe the distortion becomes, especially in low-field MRI. Hence, it might hinder the diagnosis and characterization of lesions in the presence of field inhomogeneity. In this study, an innovative multi-orientated water-phantom was used to evaluate the geometric distortion. The correlations between the level of image distortion and the relative off-center positions, as well as the variation of signal intensities, were both investigated. The image distortion ratios of axial, coronal and sagittal images were calculated.
High spatiotemporal resolution fMRI using partial separability model
Authors: Shi Caiyun , Xie Guoxi , Zhang Xiaoyong , Su Shi , Zhang Yongqin , Zhang Lijuan , Qiu Bensheng , Liu Xin
Published: August 2015

Abstract

Blood oxygenation level dependent functional MRI (BOLD fMRI) requires repeatedly scanning the same region to capture neuronal activities, so the sampling data is very sparse along the temporal direction, which offers an opportunity to accelerate the fMRI. In this paper, (k-t) space data is sparsely acquired and reconstructed for BOLD fMRI using a partial separability (PS) model with a [Formula: see text]-norm constraint. The proposed approach achieves a high temporal resolution of 200 ms without compromising spatial resolution (3.5 × 3.5 × 4.0 mm3). A simulation based on the EPI data with the right finger tapping experiment demonstrates that the proposed method can realize high spatiotemporal fMRI with accurate reconstruction of the activation regions from highly undersampled data. Meanwhile, preliminary in vivo experiment results also demonstrate the potential application of the proposed method in fMRI.
Electroencephalogram assessment of mental fatigue in visual search
Authors: Fan Xiaoli , Zhou Qianxiang , Liu Zhongqi , Xie Fang
Published: August 2015

Abstract

Mental fatigue is considered to be a contributing factor responsible for numerous road accidents and various medical conditions and the efficiency and performance could be impaired during fatigue. Hence, determining how to evaluate mental fatigue is very important. In the present study, ten subjects performed a long-term visual search task with electroencephalogram recorded, and self-assessment and reaction time (RT) were combined to verify if mental fatigue had been induced and were also used as confirmatory tests for the proposed measures. The changes in relative energy in four wavebands ([Formula: see text], and β), four ratio formulas [Formula: see text], and [Formula: see text], and Shannon’s entropy (SE) were compared and analyzed between the beginning and end of the task. The results showed that a significant increase occurred in alpha activity in the frontal, central, posterior temporal, parietal, and occipital lobes, and a dip occurred in the beta activity in the pre-frontal, inferior frontal, posterior temporal, and occipital lobes. The ratio formulas clearly increased in all of these brain regions except the temporal region, where only [Formula: see text] changed obviously after finishing the 60-min visual search task. SE significantly increased in the posterior temporal, parietal, and occipital lobes. These results demonstrate some potential indicators for mental fatigue detection and evaluation, which can be applied in the future development of countermeasures to fatigue.
A study of stereo microscope measurements based on interpolated feature matching
Authors: Wang Yigang , Jiang Gangyi , Yu Mei , Fan Shengli , Deng Jing
Published: August 2015

Abstract

Recently, digital stereo microscopes have been becoming increasingly popular in fields such as biomedicine and biotechnology. As such, being able to precisely match left/right feature pairs is critical, which is an important step in stereo matching. We hereby propose a dynamic interpolation method that combines two dimensional calibration and depth information to achieve stereo matching. In this experiment, we obtained the original scale-invariant feature transform feature point pairs and selected a pair of matching units. Based on the selected pair of matching elements and interpolated feature points shown in the left view of stereo microscope image, the dynamically interpolated feature points in the right view were developed to perform stereo measurements. The experimental results revealed that the proposed method can realize stereo microscopic measurements; results with high reliability can be achieved with a large quantity of matching elements.
Automatic segmentation in image stacks based on multi-constraint level-set evolution
Authors: Dai Ning , Liu Hao , Tang Yuehong , Zhao Jianning , Cheng Xiaosheng
Published: August 2015

Abstract

Contour extraction of image stacks is a basic task in medical modeling. The existing level-set methods usually suffer from some problems (e.g. serious errors around sharp features, incorrect split of topology and contour occlusions). This paper proposes a novel method of multi-constraint level-set evolution to avoid above-mentioned problems. Interpolation constraint and deviation constraint are added to evolution processin addition to existing constraints (such as edge points and areas). In order to prevent occlusions, it proposes a method of three-phase level-set evolution. The first phase obtains a rough contour according to selected edge points. The second phase applies an expanding LSE (level-set evolution). Missing edge points in the first phase are added when occlusions probably appear. In the third phase, occlusions are deleted and a refining evolution is implemented. As proved by final experiments, our method can steadily extract contours slice by slice when the shapes of previous contours (contours in the previous slice) are similar to current contours (contours in the current slice). Furthermore, there is no error propagation during the process of contour extraction.
Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD
Authors: Cao Peng , Liu Xiaoli , Bao Hang , Yang Jinzhu , Zhao Dazhe
Published: August 2015

Abstract

The false-positive reduction (FPR) is a crucial step in the computer aided detection system for the breast. The issues of imbalanced data distribution and the limitation of labeled samples complicate the classification procedure. To overcome these challenges, we propose oversampling and semi-supervised learning methods based on the restricted Boltzmann machines (RBMs) to solve the classification of imbalanced data with a few labeled samples. To evaluate the proposed method, we conducted a comprehensive performance study and compared its results with the commonly used techniques. Experiments on benchmark dataset of DDSM demonstrate the effectiveness of the RBMs based oversampling and semi-supervised learning method in terms of geometric mean (G-mean) for false positive reduction in Breast CAD.
Preliminary work of real-time ultrasound imaging system for 2-D array transducer
Authors: Li Xu , Yang Jiali , Ding Mingyue , Yuchi Ming
Published: August 2015

Abstract

Ultrasound (US) has emerged as a non-invasive imaging modality that can provide anatomical structure information in real time. To enable the experimental analysis of new 2-D array ultrasound beamforming methods, a pre-beamformed parallel raw data acquisition system was developed for 3-D data capture of 2D array transducer. The transducer interconnection adopted the row-column addressing (RCA) scheme, where the columns and rows were active in sequential for transmit and receive events, respectively. The DAQ system captured the raw data in parallel and the digitized data were fed through the field programmable gate array (FPGA) to implement the pre-beamforming. Finally, 3-D images were reconstructed through the devised platform in real-time.
A new approach to compensate the geometric distortion in the synthetic aperture ultrasonic imaging system
Authors: He Xiaonian , Liu Weixiang , Chen Siping , Qin Zhengdi
Published: August 2015

Abstract

In the field of ultrasonic imaging technology, the problem of geometric distortion is often encountered, especially in the ultrasonic near-field. In this study, a new approach is proposed to compensate for geometric distortion in the synthetic aperture ultrasonic imaging system. This approach is based on the synthetic aperture ultrasonic holographic B-scan (UHB) imaging system, which is a combination of ultrasonic holography based on the backward propagation principle and the conventional B-scan technique. To solve the geometric distortion problem, the operation of the spatial compression and resampling in the frequency domain are introduced. The main advantage of the approach is that the real holographic value can be calculated without distortion by using the spatial interpolation function after the spatial frequency compression. After the compensation for geometric distortion is performed, the synthetic aperture technique based on the backward propagation principle is then applied in the process of the two-dimensional numerical imaging reconstruction. Both the simulation and measurement experiment show that the approach is promising. The geometric distortion that is dependent on the wave front angle can be effectively compensated. The spatial resolution is practically uniform throughout the depth range and close to the theoretical limit in the experiments.
A framework design for the mHealth system for self-management promotion
Authors: Jia Guifeng , Yang Pan , Zhou Jie , Zhang Hengyi , Lin Chengyu , Chen Jin , Cai Guolong , Yan Jing , Ning Gangmin
Published: August 2015

Abstract

Mobile health (mHealth) technology has been proposed to alleviate the lack of sufficient medical resources for personal healthcare. However, usage difficulties and compliance issues relating to this technology restrict the effect of mHealth system-supported self-management. In this study, an mHealth framework is introduced to overcome these drawbacks and improve the outcome of self-management. We implemented a set of ease of use principles in the mHealth design and employed the quantitative Fogg Behavior Model to enhance users’ execution ability. The framework was realized in a prototype design for the mHealth system, which consists of medical apparatuses, mobile applications and a health management server. The system is able to monitor the physiological status in an unconstrained manner with simplified operations, while supervising the healthcare plan. The results suggest that the present framework design is accessible for ordinary users and effective in improving users’ execution ability in self-management.
Fuzzy Naive Bayesian for constructing regulated network with weights
Authors: Zhou Xi Y. , Tian Xue W. , Lim Joon S.
Published: August 2015

Abstract

In the data mining field, classification is a very crucial technology, and the Bayesian classifier has been one of the hotspots in classification research area. However, assumptions of Naive Bayesian and Tree Augmented Naive Bayesian (TAN) are unfair to attribute relations. Therefore, this paper proposes a new algorithm named Fuzzy Naive Bayesian (FNB) using neural network with weighted membership function (NEWFM) to extract regulated relations and weights. Then, we can use regulated relations and weights to construct a regulated network. Finally, we will classify the heart and Haberman datasets by the FNB network to compare with experiments of Naive Bayesian and TAN. The experiment results show that the FNB has a higher classification rate than Naive Bayesian and TAN.
Simple LED spectrophotometer for analysis of color information
Authors: Kim Ji-Sun , Kim A-Hee , Oh Han-Byeol , Goh Bong-Jun , Lee Eun-Suk , Kim Jun-Sik , Jung Gu-In , Baek Jin-Young , Jun Jae-Hoon
Published: August 2015

Abstract

A spectrophotometer is the basic measuring equipment essential to most research activity fields requiring samples to be measured, such as physics, biotechnology and food engineering. This paper proposes a system that is able to detect sample concentration and color information by using LED and color sensor. Purity and wavelength information can be detected by CIE diagram, and the concentration can be estimated with purity information. This method is more economical and efficient than existing spectrophotometry, and can also be used by ordinary persons. This contribution is applicable to a number of fields because it can be used as a colorimeter to detect the wavelength and purity of samples.
An automatic and efficient pipeline for disease gene identification through utilizing family-based sequencing data
Authors: Song Dandan , Li Ning , Liao Lejian
Published: August 2015

Abstract

Due to the generation of enormous amounts of data at both lower costs as well as in shorter times, whole-exome sequencing technologies provide dramatic opportunities for identifying disease genes implicated in Mendelian disorders. Since upwards of thousands genomic variants can be sequenced in each exome, it is challenging to filter pathogenic variants in protein coding regions and reduce the number of missing true variants. Therefore, an automatic and efficient pipeline for finding disease variants in Mendelian disorders is designed by exploiting a combination of variants filtering steps to analyze the family-based exome sequencing approach. Recent studies on the Freeman-Sheldon disease are revisited and show that the proposed method outperforms other existing candidate gene identification methods.
Identification of 13 blood-based gene expression signatures to accurately distinguish tuberculosis from other pulmonary diseases and healthy controls
Authors: Huang Hai-Hui , Liu Xiao-Ying , Liang Yong , Chai Hua , Xia Liang-Yong
Published: August 2015

Abstract

Tuberculosis (TB), caused by infection with mycobacterium tuberculosis, is still a major threat to human health worldwide. Current diagnostic methods encounter some limitations, such as sample collection problem or unsatisfied sensitivity and specificity issue. Moreover, it is hard to identify TB from some of other lung diseases without invasive biopsy. In this paper, the logistic models with three representative regularization approaches including Lasso (the most popular regularization method), and [Formula: see text] (the method that inclines to achieve more sparse solution than Lasso) and Elastic Net (the method that encourages a grouping effect of genes in the results) adopted together to select the common gene signatures in microarray data of peripheral blood cells. As the result, 13 common gene signatures were selected, and sequentially the classifier based on them is constructed by the SVM approach, which can accurately distinguish tuberculosis from other pulmonary diseases and healthy controls. In the test and validation datasets of the blood gene expression profiles, the generated classification model achieved 91.86% sensitivity and 93.48% specificity averagely. Its sensitivity is improved 6%, but only 26% gene signatures used compared to recent research results. These 13 gene signatures selected by our methods can be used as the basis of a blood-based test for the detection of TB from other pulmonary diseases and healthy controls.
A novel framework for analyzing somatic copy number aberrations and tumor subclones for paired heterogeneous tumor samples
Authors: Xia Hong , Li Ao , Yu Zhenhua , Liu Xiaocheng , Feng Huanqing
Published: August 2015

Abstract

Application of the Next generation sequencing (NGS) technology has demonstrated that most tumor samples exhibit intra-tumor heterogeneity. Here we proposed SAPPH (Somatic Aberrations Prediction for Paired Heterogeneous tumor samples), as a new method for estimating tumor somatic copy number aberrations as well as inferring tumor subclone proportions from heterogeneous tumor sequencing data. This method is based on CBS and local proportion clustering strategy. When SAPPH is applied on simulated tumor samples, the agreement between the results analyzed by SAPPH and the sequencing signals suggests that SAPPH can find the solution to best fit the signal distributions. We benchmark the performance of SAPPH and show that it outperforms existing method in estimating tumor copy number aberrations.
Classification of imbalanced bioinformatics data by using boundary movement-based ELM
Authors: Cheng Ke , Chen Qingfang , Yang Xibei , Gao Shang , Yu Hualong
Published: August 2015

Abstract

To address the imbalanced classification problem emerging in Bioinformatics, a boundary movement-based extreme learning machine (ELM) algorithm called BM-ELM was proposed. BM-ELM tries to firstly explore the prior information about data distribution by condensing all training instances into the one-dimensional feature space corresponding to the original output in ELM, and then on the transformed space, to find the optimal moving distance of the classification hyperplane by estimating the probability density distributions of the instances in different classes. Experimental results on four real imbalanced bioinformatics classification data sets indicated that the proposed BM-ELM algorithm outperforms some traditional bias correction algorithms due to it can greatly improve the sensitivity of the classification results with small loss of specificity as possible. Also, BM-ELM algorithm has presented better performance than the widely used support vector machine (SVM) classifier. The algorithm can be widely popularized in various large-scale bioinformatics applications.
An effective fuzzy kernel clustering analysis approach for gene expression data
Authors: Sun Lin , Xu Jiucheng , Yin Jiaojiao
Published: August 2015

Abstract

Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approach to fuzzy kernel clustering analysis (FKCA) that identifies desired cluster number and obtains more steady results for gene expression data. First of all, to optimize characteristic differences and estimate optimal cluster number, Gaussian kernel function is introduced to improve spectrum analysis method (SAM). By combining subtractive clustering with max-min distance mean, maximum distance method (MDM) is proposed to determine cluster centers. Then, the corresponding steps of improved SAM (ISAM) and MDM are given respectively, whose superiority and stability are illustrated through performing experimental comparisons on gene expression data. Finally, by introducing ISAM and MDM into FKCA, an effective improved FKCA algorithm is proposed. Experimental results from public gene expression data and UCI database show that the proposed algorithms are feasible for cluster analysis, and the clustering accuracy is higher than the other related clustering algorithms.
Analysis of vascular endothelial dysfunction genes and related pathways in obesity through systematic bioinformatics
Authors: Zhang Hui , Wang Jing , Sun Ling , Xu Qiuqin , Hou Miao , Ding Yueyue , Huang Jie , Chen Ye , Cao Lei , Zhang Jianmin , Qian Weiguo , Lv Haitao
Published: August 2015

Abstract

Obesity has become an increasingly serious health problem and popular research topic. It is associated with many diseases, especially cardiovascular disease (CVD)-related endothelial dysfunction. This study analyzed genes related to endothelial dysfunction and obesity and then summarized their most significant signaling pathways. Genes related to vascular endothelial dysfunction and obesity were extracted from a PubMed database, and analyzed by STRING, DAVID, and Gene-Go Meta-Core software. 142 genes associated with obesity were found to play a role in endothelial dysfunction in PubMed. A significant pathway (Angiotensin system maturation in protein folding and maturation) associated with obesity and endothelial dysfunction was explored. The genes and the pathway explored may play an important role in obesity. Further studies about preventing vascular endothelial dysfunction obesity should be conducted through targeting these loci and pathways.
Preliminary testing for the Markov property of the fifteen chromatin states of the Broad Histone Track
Authors: Lee Kyung-Eun , Park Hyun-Seok
Published: August 2015

Abstract

Epigenetic computational analyses based on Markov chains can integrate dependencies between regions in the genome that are directly adjacent. In this paper, the BED files of fifteen chromatin states of the Broad Histone Track of the ENCODE project are parsed, and comparative nucleotide frequencies of regional chromatin blocks are thoroughly analyzed to detect the Markov property in them. We perform various tests to examine the Markov property embedded in a frequency domain by checking for the presence of the Markov property in the various chromatin states. We apply these tests to each region of the fifteen chromatin states. The results of our simulation indicate that some of the chromatin states possess a stronger Markov property than others. We discuss the significance of our findings in statistical models of nucleotide sequences that are necessary for the computational analysis of functional units in noncoding DNA.
A hybrid ensemble method based on double disturbance for classifying microarray data
Authors: Chen Tao , Xue Huifeng , Hong Zenglin , Cui Man , Zhao Hui
Published: August 2015

Abstract

Microarray data has small samples and high dimension, and it contains a significant amount of irrelevant and redundant genes. This paper proposes a hybrid ensemble method based on double disturbance to improve classification performance. Firstly, original genes are ranked through reliefF algorithm and part of the genes are selected from the original genes set, and then a new training set is generated from the original training set according to the previously selected genes. Secondly, D bootstrap training subsets are produced from the previously generated training set by bootstrap technology. Thirdly, an attribute reduction method based on neighborhood mutual information with a different radius is used to reduce genes on each bootstrap training subset to produce new training subsets. Each new training subset is applied to train a base classifier. Finally, a part of the base classifiers are selected based on the teaching-learning-based optimization to build an ensemble by weighted voting. Experimental results on six benchmark cancer microarray datasets showed proposed method decreased ensemble size and obtained higher classification performance compared with Bagging, AdaBoost, and Random Forest.
A genetic filter for cancer classification on gene expression data
Authors: Kim Yong-Hyuk , Yoon Yourim
Published: August 2015

Abstract

We present a new genetic filter to identify a predictive gene subset for cancer-type classification on gene expression profiles. This approach pursues to not only maximize correlation between selected genes and cancer types but also minimize inter-correlation among selected genes. The proposed genetic filter was tested on well-known leukemia datasets, and significant improvement over previous work was obtained.
Multisite protein subcellular localization prediction based on entropy density
Authors: Zhao Qing , Wang Dong , Chen Yuehui , Qu Xumi
Published: August 2015

Abstract

Protein subcellular localization prediction is currently receiving much attention in the field of protein research. Many researchers make great efforts to study single-site protein subcellular localization, but the experimental data shows that many proteins can be found in two or more sub-cellular locations, prompting the study of multisite protein sub-cellular localization. This study utilized a Gpos-mPLOC data set and pseudo amino acid compositions, physicochemical properties of amino acid composition, and entropy density as three effective feature extraction methods. Then, these features were then placed in a multi-label k nearest neighbor classifier to predict subcellular protein locations. Experimental results verified that this approach provides a localization precision of 66.73% through the Jack-knife test.
Hematocrit estimation using online sequential extreme learning machine
Authors: Huynh Hieu Trung , Won Yonggwan , Kim Jinsul
Published: August 2015

Abstract

Hematocrit is a blood test that is defined as the volume percentage of red blood cells in the whole blood. It is one of the important indicators for clinical decision making and the most effective factor in glucose measurement using handheld devices. In this paper, a method for hematocrit estimation that is based upon the transduced current curve and the neural network is presented. The salient points of this method are that (1) the neural network is trained by the online sequential extreme learning machine (OS-ELM) in which the devices can be still trained with new samples during the using process and (2) the extended features are used to reduce the number of current points which can save the battery power of devices and speed up the measurement process.
Inhibition of RhoA expression by adenovirus-mediated siRNA combined with TNF-α induced apoptosis of hepatocarcinoma cells
Authors: Yin Kun , Zhao Guihua , Huang Xiaodan , Gao Ge , Sun Hui , Wei Qingkuan , Liu Qiaoqiao , Li Mian , Xu Chao , Zhu Song , Ba Zaihua , Yan Ge
Published: August 2015

Abstract

Tumor necrosis factor-alpha (TNF- α) has been used as an effective treatment for Hepatocellular Carcinoma, however, inducing tumor cell apoptosis by TNF- α alone is still unsatisfactory. RhoA is highly expressed in hepatocarcinoma cells and can be activated by TNF- α. The activation of RhoA directly leads to a poor prognosis of HCC. Therefore, we propose to investigate the therapeutic effect of TNF- α together with RhoA siRNA. RhoA inhibition was accomplished by constructing a recombinant adenovirus that can efficiently express RhoA siRNA in HepG2 cells. The recombinant adenovirus AdshRNA-RhoA and AdU6-control were generated by adenovirus-mediated siRNA expression system. The inhibition effects were detected by RT-PCR in addition to immunoblot to quantify the decreased levels of RhoA expression, and the therapeutic effect for HCC was demonstrated by the proliferation and apoptosis ratios of HepG2 cells. The inhibition effects of RhoA by AdshRNA-RhoA were significant at both mRNA and protein levels: the transcription of RhoA mRNA decreased by 74.46%, and the expression of protein decreased by 76.48%. The proliferation rate of HepG2 cells detected by MTT showed that a treatment of AdshRNA-RhoA and TNF- α together could strengthen the suppression ability of TNF- α to HepG2 cells, resulting in approximately 14.2% more than those treated with only TNF- α. FCA and TUNEL assays results revealed that the combined treatment can induce apoptosis in approximately 52.14%-65% of the HepG2 cells, whereas this ratio in the TNF- α-alone group was only 21.91%-32%. Our results showed that AdshRNA-RhoA can efficiently enhance the TNF- α-induced apoptosis of hepatocarcinoma cells. This method might be a useful therapeutic route in HCC and other tumors.