bluuebunny/crossref_metadata_2025_split huggingface dataset:
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.