bluuebunny/crossref_metadata_2025 huggingface dataset:
EEG functional network properties related to visually induced unrecognized spatial disorientation
Authors: Li Yan , Chen Yuanyuan , Lv Xiaoning , Yang Yang , Li Jing , Hao Chenru , Li Minggao , Pu Fang
Published: August 2015

Abstract

Unrecognized spatial disorientation (SD) which is intimately linked with brain cognitive function is always a fatal issue for the safety of pilots. To explore its effects on human brain cognitive functions, electroencephalography (EEG) functional network analysis methods were adopted to examine topological changes in the connection of cognitive regions when experiencing unrecognized SD. Twelve male pilots participated in the study. They were subjected to a SD scene, namely visual rotation, which evoked unrecognized SD. For the main EEG frequency intervals, the phase lag index (PLI) and normalized mutual information (NMI) were calculated to quantify the EEG data. Then weighted connectivity networks were constructed and their properties were characterized in terms of an average clustering coefficient and global efficiency. A T-test was performed to compare PLI, NMI and network measures under unrecognized SD and non-SD conditions. It indicated a weak functional connectivity level in the theta band under unrecognized SD based on the significant decrease of mean values of PLI and NMI (p<0.05). Meanwhile, both the average clustering coefficient and global efficiency in the theta band reduced under the unrecognized SD condition. The decrease of the average clustering coefficient and global efficiency demonstrates a lack of small-world characteristics and a decline in processing efficiency of brain cognitive regions. All the experimental results show that unrecognized SD may have a negative effect on brain functional networks in the theta band.
Nonholonomic mobile system control by combining EEG-based BCI with ANFIS
Authors: Yu Weiwei , Feng Huashan , Feng Yangyang , Madani Kurosh , Sabourin Christophe
Published: August 2015

Abstract

Motor imagery EEG-based BCI has advantages in the assistance of human control of peripheral devices, such as the mobile robot or wheelchair, because the subject is not exposed to any stimulation and suffers no risk of fatigue. However, the intensive training necessary to recognize the numerous classes of data makes it hard to control these nonholonomic mobile systems accurately and effectively. This paper proposes a new approach which combines motor imagery EEG with the Adaptive Neural Fuzzy Inference System. This approach fuses the intelligence of humans based on motor imagery EEG with the precise capabilities of a mobile system based on ANFIS. This approach realizes a multi-level control, which makes the nonholonomic mobile system highly controllably without stopping or relying on sensor information. Also, because the ANFIS controller can be trained while performing the control task, control accuracy and efficiency is increased for the user. Experimental results of the nonholonomic mobile robot verify the effectiveness of this approach.
The use of kurtosis de-noising for EEG analysis of patients suffering from Alzheimer’s disease
Authors: Wang Gang , Shepherd Simon J. , Beggs Clive B. , Rao Nini , Zhang Ying
Published: August 2015

Abstract

The use of electroencephalograms (EEGs) to diagnose and analyses Alzheimer’s disease (AD) has received much attention in recent years. The sample entropy (SE) has been widely applied to the diagnosis of AD. In our study, nine EEGs from 21 scalp electrodes in 3 AD patients and 9 EEGs from 3 age-matched controls are recorded. The calculations show that the kurtoses of the AD patients’ EEG are positive and much higher than that of the controls. This finding encourages us to introduce a kurtosis-based de-noising method. The 21-electrode EEG is first decomposed using independent component analysis (ICA), and second sort them using their kurtoses in ascending order. Finally, the subspace of EEG signal using back projection of only the last five components is reconstructed. SE will be calculated after the above de-noising preprocess. The classifications show that this method can significantly improve the accuracy of SE-based diagnosis. The kurtosis analysis of EEG may contribute to increasing the understanding of brain dysfunction in AD in a statistical way.
A study of sleep staging based on a sample entropy analysis of electroencephalogram
Authors: Li Hui , Peng Cheng , Ye Datian
Published: August 2015

Abstract

In this paper we report a detection method for different sleep stages and it is based on a single-channel electroencephalogram (EEG) system. The system is simple and can be easily setup in homes to perform sleep EEG recording, overnight sleep EEG automatic staging, and sleep quality evaluation. EEG data of 14 sleeping subjects were recorded through the entire night. All subjects were within the age group of 20-30 years and having no significant sleep disorders. To analyze the EEG data, it is segmented into equal time intervals. This is followed by calculation of Sample Entropy (SampEn) for each section, and the SampEn’s statistical characteristics, such as the median, upper quartile, lower quartile and inter-quartile range. The sleep data were divided into training group (7 cases) and test group (7 cases). Sleep stages’ quantitative ranges of training group referring to ZEO results were extracted and the quantization range used to sleep staging EEG data. Both the training group and test group results were close to ZEO results. It suggested that the statistical characteristics of Sample Entropy could be used as a criterion for sleep staging and evaluation.
Detecting diffuse axonal injury in rat brainstems by diffusion tensor imaging and AQP4 expression
Authors: Zheng Wenbin , Ma Chunlin , Kong Lingmei , Chen Xiran , Fan Wenyao
Published: August 2015

Abstract

This study investigated the correlation between AQP4 expression and DTI in rat brainstems after diffuse axonal injuries (DAI). Forty rats were imaged before injury and reimaged at 3, 6, 12, 24 and 72 h post-injury. A control group of 8 rats was imaged and sacrificed for histology but not injured. After brain injury, AQP4 expression and ADC values in the brainstems increased gradually, reaching peak values at 24 h and 12 h, respectively. FA values decreased within 72 h. There was a negative correlation between ADC values and brainstem AQP4 expression at 12 h, and a positive correlation at 24 h or 72 h ([Formula: see text]), respectively. Changes in the ADC and FA values in the brainstems indicated brain edema and severe axonal injuries. The correlations between AQP4 expression and time-dependent ADC values aid in understanding brain edema development after DAI.
An empirical comparison of different LDA methods in fMRI-based brain states decoding
Authors: Xia Maogeng , Song Sutao , Yao Li , Long Zhiying
Published: August 2015

Abstract

Decoding brain states from response patterns with multivariate pattern recognition techniques is a popular method for detecting multivoxel patterns of brain activation. These patterns are informative with respect to a subject’s perceptual or cognitive states. Linear discriminant analysis (LDA) cannot be directly applied to fMRI data analysis because of the “few samples and large features” nature of functional magnetic resonance imaging (fMRI) data. Although several improved LDA methods have been used in fMRI-based decoding, little is known regarding the relative performance of different LDA classifiers on fMRI data. In this study, we compared five LDA classifiers using both simulated data with varied noise levels and real fMRI data. The compared LDA classifiers include LDA combined with PCA (LDA-PCA), LDA with three types of regularizations (identity matrix, diagonal matrix and scaled identity matrix) and LDA with optimal-shrinkage covariance estimator using Ledoit and Wolf lemma (LDA-LW). The results indicated that LDA-LW was the most robust to noises. Moreover, LDA-LW and LDA with scaled identity matrix showed better stability and classification accuracy than the other methods. LDA-LW demonstrated the best overall performance.
Study on the diagnostic system of scoliosis by using infrared camera
Authors: Jeong Jin-hyoung , Park Eun-jeong , Cho Chang-ok , Kim Yoon-jeong , Lee Sang-sik
Published: August 2015

Abstract

In this study, the radiation generated in the diagnosis of scoliosis, to solve the problems by using an infrared camera and an optical marker system that can diagnose scoliosis developed. System developed by the infrared camera attached to the optical spinal curvature is recognized as a marker to shoot the angle between the two optical markers are measured. Measurement of angle, we used the Cobb’s Angle method used in the diagnosis of spinal scoliosis. We developed a software to be able to output to the screen using an infrared camera to diagnose spinal scoliosis. Software is composed of camera output unit was manufactured in Labview, angle measurement unit, in Cobb’s Angle measurement unit. In the future, kyphosis, Hallux Valgus, such as the diagnosis of orthopedic disorders that require the use of a diagnostic system is expected case.
Computer-aided detection of lung nodules using outer surface features
Authors: Demir Önder , Yılmaz Çamurcu Ali
Published: August 2015

Abstract

In this study, a computer-aided detection (CAD) system was developed for the detection of lung nodules in computed tomography images. The CAD system consists of four phases, including two-dimensional and three-dimensional preprocessing phases. In the feature extraction phase, four different groups of features are extracted from volume of interests: morphological features, statistical and histogram features, statistical and histogram features of outer surface, and texture features of outer surface. The support vector machine algorithm is optimized using particle swarm optimization for classification. The CAD system provides 97.37% sensitivity, 86.38% selectivity, 88.97% accuracy and 2.7 false positive per scan using three groups of classification features. After the inclusion of outer surface texture features, classification results of the CAD system reaches 98.03% sensitivity, 87.71% selectivity, 90.12% accuracy and 2.45 false positive per scan. Experimental results demonstrate that outer surface texture features of nodule candidates are useful to increase sensitivity and decrease the number of false positives in the detection of lung nodules in computed tomography images.
Enhancement of objects in photoacoustic tomography using selective filtering
Authors: Shin Dong-ho , Yang Yoonseok , Song Chul-Gyu
Published: August 2015

Abstract

Here we developed a real-time photoacoustic tomography (PAT) imaging acquisition device based on the linear array transducer utilized on ultrasonic devices. Also, we produced a phantom including diverse contrast media and acquired PAT imaging as the light source wavelength was changing to see if the contrast media reacted. Indocyanine green showed the highest reaction around the 800-nm band, methylene blue demonstrated the same in the 750-nm band, and gold nanoparticle showed the same in the 700-nm band. However, in the case of superparamagnetic iron oxide, we observed not reaction within the wavelength bands used herein to obtain imaging. Moreover, we applied selective filtering to the acquired PAT imaging to remove noise from around and reinforce the object’s area. Consequentially, we could see the object area in the imaging was effectively detected and the image noise was removed.
Detection of Perlger-Huet anomaly based on augmented fast marching method and speeded up robust features
Authors: Sun Minglei , Yang Shaobao , Jiang Jinling , Wang Qiwei
Published: August 2015

Abstract

Pelger-Huet anomaly (PHA) and Pseudo Pelger-Huet anomaly (PPHA) are neutrophil with abnormal morphology. They have the bilobed or unilobed nucleus and excessive clumping chromatin. Currently, detection of this kind of cell mainly depends on the manual microscopic examination by a clinician, thus, the quality of detection is limited by the efficiency and a certain subjective consciousness of the clinician. In this paper, a detection method for PHA and PPHA is proposed based on karyomorphism and chromatin distribution features. Firstly, the skeleton of the nucleus is extracted using an augmented Fast Marching Method (AFMM) and width distribution is obtained through distance transform. Then, caryoplastin in the nucleus is extracted based on Speeded Up Robust Features (SURF) and a K-nearest-neighbor (KNN) classifier is constructed to analyze the features. Experiment shows that the sensitivity and specificity of this method achieved 87.5% and 83.33%, which means that the detection accuracy of PHA is acceptable. Meanwhile, the detection method should be helpful to the automatic morphological classification of blood cells.
An efficient sampling algorithm for uncertain abnormal data detection in biomedical image processing and disease prediction
Authors: Liu Fei , Zhang Xi , Jia Yan
Published: August 2015

Abstract

In this paper, we propose a computer information processing algorithm that can be used for biomedical image processing and disease prediction. A biomedical image is considered a data object in a multi-dimensional space. Each dimension is a feature that can be used for disease diagnosis. We introduce a new concept of the top ([Formula: see text]) outlier. It can be used to detect abnormal data objects in the multi-dimensional space. This technique focuses on uncertain space, where each data object has several possible instances with distinct probabilities. We design an efficient sampling algorithm for the top ([Formula: see text]) outlier in uncertain space. Some improvement techniques are used for acceleration. Experiments show our methods’ high accuracy and high efficiency.
MEMS-based system and image processing strategy for epiretinal prosthesis
Authors: Xia Peng , Hu Jie , Qi Jin , Gu Chaochen , Peng Yinghong
Published: August 2015

Abstract

Retinal prostheses have the potential to restore some level of visual function to the patients suffering from retinal degeneration. In this paper, an epiretinal approach with active stimulation devices is presented. The MEMS-based processing system consists of an external micro-camera, an information processor, an implanted electrical stimulator and a microelectrode array. The image processing strategy combining image clustering and enhancement techniques was proposed and evaluated by psychophysical experiments. The results indicated that the image processing strategy improved the visual performance compared with direct merging pixels to low resolution. The image processing methods assist epiretinal prosthesis for vision restoration.
Robust boundary detection of left ventricles on ultrasound images using ASM-level set method
Authors: Zhang Yaonan , Gao Yuan , Li Hong , Teng Yueyang , Kang Yan
Published: August 2015

Abstract

Level set method has been widely used in medical image analysis, but it has difficulties when being used in the segmentation of left ventricular (LV) boundaries on echocardiography images because the boundaries are not very distinguish, and the signal-to-noise ratio of echocardiography images is not very high. In this paper, we introduce the Active Shape Model (ASM) into the traditional level set method to enforce shape constraints. It improves the accuracy of boundary detection and makes the evolution more efficient. The experiments conducted on the real cardiac ultrasound image sequences show a positive and promising result.
Automatic extraction of 3D anatomical feature curves of hip bone models reconstructed from CT images
Authors: Liu Hao , Qian Hongbo , Zhao Jianning
Published: August 2015

Abstract

3D anatomical feature curves (AFC) on bone models reconstructed from CT/MRI images are important in some fields, such as preoperative planning, intra-operative navigation, patient-specific prosthesis design, etc. Interactive extraction of feature curves on patient-specific bone models is time-consuming, has low repeatability and accuracy. This paper presents a computer graphics method to automatically extract AFC from 3D hip bone models reconstructed from CT images. A DCSS (direct curvature scale space)-based technique is firstly used to extract anatomical feature points (AFP) in every contour, using anatomical structure information as prior knowledge so that AFP are extracted and only extracted. Then, corresponding AFP are linked in different contours and AFC is generated. AFC obtained by our method were compared with those interactively extracted by three surgeons, which showed that our method is feasible (Dice coefficient: 0.94; Average symmetric surface distance: 3.97 mm). The method was also applied to identify anatomical landmarks, which showed that our method is superior to the curvature-based methods that fail to identify landmark regions or have too many redundant regions, which results in failures to subsequently label landmark regions using pre-defined spatial adjacency matrices.
Non-model segmentation of brain glioma tissues with the combination of DWI and fMRI signals
Authors: Lu Min , Zhang Xiaojie , Zhang Mingyu , Chen Hongyan , Dou Weibei , Li Shaowu , Dai Jianping
Published: August 2015

Abstract

For quantitative analysis of glioma, multimodal Magnetic Resonance Imaging (MRI) signals are required in combination to perform a complementary analysis of morphological, metabolic, and functional changes. Most of the morphological analyses are based on T1-weighted and T2-weighted signals, called traditional MRI. But more detailed information about tumorous tissues could not be explained. An information combination scheme of Diffusion-Weighted Imaging (DWI) and Blood-Oxygen-Level Dependent (BOLD) contrast Imaging is proposed in this paper. This is a non-model segmentation scheme of brain glioma tissues in a particular perspective of combining multi-parameters of DWI and BOLD contrast functional Magnetic Resonance Imaging (fMRI). Compared with traditional MRI, a promising advantage of our work is to provide an effective and adequate subdivision of the related pathological regions with glioma, by incorporating both knowledge of image graylevel and spatial structure. Furthermore, it is an automatic segmentation method without needs of parameter selection and model fitting for the extracted tissues. By the experiments in patients with glioma, the proposed method has achieved the average overlap ratios of 83.6% in the whole tumor region and 82.5% in the peritumoral edema region with the manual segmentation as “ground truth”.
A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation
Authors: Wang Rui , Zhou Yongquan , Zhao Chengyan , Wu Haizhou
Published: August 2015

Abstract

Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu’s objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu’s objective values and standard deviations.
Fully automatic scheme for measuring liver volume in 3D MR images
Authors: Le Trong-Ngoc , Bao Pham The , Huynh Hieu Trung
Published: August 2015

Abstract

In this paper, a fully automatic scheme for measuring liver volume in 3D MR images was developed. The proposed MRI liver volumetry scheme consisted of four main stages. First, the preprocessing stage was applied to T1-weighted MR images of the liver in the portal-venous phase to reduce noise. The histogram of the 3D image was determined, and the second-to-last peak of the histogram was calculated using a neural network. Thresholds, which are determined based upon the second-to-last peak, were used to generate a thresholding image. This thresholding image was refined using a gradient magnitude image. The morphological and connected component operations were applied to the refined image to generate the rough shape of the liver. A 3D geodesic-active-contour segmentation algorithm refined the rough shape in order to more precisely determine the liver boundaries. The liver volumes determined by the proposed automatic volumetry were compared to those manually traced by radiologists; these manual volumes were used as a “gold standard.” The two volumetric methods reached an excellent agreement. The Dice overlap coefficient and the average accuracy were 91.0 ±2.8% and 99.0 ±0.4%, respectively. The mean processing time for the proposed automatic scheme was 1.02 ±0.08 min (CPU: Intel, core i7, 2.8GHz), whereas that of the manual volumetry was 24.3 ±3.7 min (p < 0.001).
Sparse-view computed tomography image reconstruction via a combination of L1 and SL0 regularization
Authors: Qi Hongliang , Chen Zijia , Guo Jingyu , Zhou Linghong
Published: August 2015

Abstract

Low-dose computed tomography reconstruction is an important issue in the medical imaging domain. Sparse-view has been widely studied as a potential strategy. Compressed sensing (CS) method has shown great potential to reconstruct high-quality CT images from sparse-view projection data. Nonetheless, low-contrast structures tend to be blurred by the total variation (TV, L1-norm of the gradient image) regularization. Moreover, TV will produce blocky effects on smooth and edge regions. To overcome this limitation, this study has proposed an iterative image reconstruction algorithm by combining L1 regularization and smoothed L0 (SL0) regularization. SL0 is a smooth approximation of L0 norm and can solve the problem of L0 norm being sensitive to noise. To evaluate the proposed method, both qualitative and quantitative studies were conducted on a digital Shepp-Logan phantom and a real head phantom. Experimental comparative results have indicated that the proposed L1/SL0-POCS algorithm can effectively suppress noise and artifacts, as well as preserve more structural information compared to other existing methods.
FASART: An iterative reconstruction algorithm with inter-iteration adaptive NAD filter
Authors: Zhou Ziying , Li Yugang , Zhang Fa , Wan Xiaohua
Published: August 2015

Abstract

Electron tomography (ET) is an essential imaging technique for studying structures of large biological specimens. These structures are reconstructed from a set of projections obtained at different sample orientations by tilting the specimen. However, most of existing reconstruction methods are not appropriate when the data are extremely noisy and incomplete. A new iterative method has been proposed: adaptive simultaneous algebraic reconstruction with inter-iteration adaptive non-linear anisotropic diffusion (NAD) filter (FASART). We also adopted an adaptive parameter and discussed the step for the filter in this reconstruction method. Experimental results show that FASART can restrain the noise generated in the process of iterative reconstruction and still preserve the more details of the structure edges.
Novel irregular mesh tagging algorithm for wound synthesis on a 3D face
Authors: Lee Sangyong , Chin Seongah
Published: August 2015

Abstract

Recently, advanced visualizing techniques in computer graphics have considerably enhanced the visual appearance of synthetic models. To realize enhanced visual graphics for synthetic medical effects, the first step followed by rendering techniques involves attaching albedo textures to the region where a certain graphic is to be rendered. For instance, in order to render wound textures efficiently, the first step is to recognize the area where the user wants to attach a wound. However, in general, face indices are not stored in sequential order, which makes sub-texturing difficult. In this paper, we present a novel mesh tagging algorithm that utilizes a task for mesh traversals and level extension in the general case of a wound sub-texture mapping and a selected region deformation in a three-dimensional (3D) model. This method works automatically on both regular and irregular mesh surfaces. The approach consists of mesh selection (MS), mesh leveling (ML), and mesh tagging (MT). To validate our approach, we performed experiments for synthesizing wounds on a 3D face model and on a simulated mesh.
Diffusion tensor imaging to determine the potential motor network connectivity between the involved and non-involved hemispheres in stroke
Authors: Lee Min-Hee , Shin Yong-Il , Lee Sang Hyeon , Cha Young Joo , Kim Dong Youn , Han Bong Soo , You Sung H.
Published: August 2015

Abstract

Hemiparetic stroke is a common motor network disorder that affects a wide range of functional movements due to cortical and subcortical network lesions in stroke patients. Conventional magnetic resonance imaging (MRI) has been used to examine structural brain damage, but the integrity and connectivity of the whole brain are poorly understood. Hence, advanced neuroimaging with diffusion tensor imaging (DTI) has been developed to better localize fiber architecture and connectivity in the motor network or pathways that are responsible for motor impairments in hemiparetic stroke. To ascertain motor network connectivity between the involved and non-involved hemispheres in stroke patients, we analyzed the DTI data from all right hemiparetic stroke patients using fractional anisotropy (FA) and network parameters, including node degree and edge betweenness centrality (EBC). The FA values were substantially lower in the left hemisphere than the right hemisphere. Similarly, the node degree and EBC were significantly lower in the left hemisphere than the right hemisphere. The present brain network analysis may provide a useful neuropathway marker for accurate diagnosis and therapeutic intervention.
Lateral resolution enhancement using programmable phase modulator in optical coherence tomography
Authors: Shirazi Muhammad Faizan , Cho Nam Hyun , Jung Woonggyu , Kim Jeehyun
Published: August 2015

Abstract

Adaptive optics plays an important role in the correction of high-order aberrations to enhance lateral resolution. An OCT system coupled with a programmable phase modulator is designed to verify lateral resolution improvement. The Hamamatsu high-resolution, non-pixelized, optically addressed light modulator (PAL-SLM) PPM X7550 series is used to correct aberrations, utilizing a simple method based on phase information from OCT images. The advantages of this method are its high simplicity and low cost. A raster scanning technique is adopted to scan samples covered with scattering suspension. Metal lines covered with scattering suspension are imaged, and a respective correction is applied to remove the aberration. Results demonstrate an improvement in lateral resolution on metal grid samples.
Real-time photoacoustic tomograpghy using linear array probe and detection of line structure using Hough transform
Authors: Shin Seung-Won , Park Jaebyung , Shin Dong ho , Song Chul-Gyu , Kim Kyeong-Seop
Published: August 2015

Abstract

A real-time photoacoustic tomography (PAT) system is developed using a linear array probe and phantom images are acquired with a pattern of line structure. Moreover, it is attempted to detect line structures from the acquired images by Hough transform. This effort leads to the measurement of a process of magenta passing through a tube and acquisition of images at a speed of about 2 frame/sec. Besides, it is confirmed that the Hough transform applied on the acquired PAT images has the detection rate of about 50% for delineating a line structure.
A joint ROI extraction filter for computer aided lung nodule detection
Authors: Shi Zhenghao , Xu Binxin , Zhao Minghua , Zhao Jinwei , Wang Yinghui , Liu Yonghong , Zhang Ming , He Lifeng , Suzuki Kenji
Published: August 2015

Abstract

Extraction of regions of interest plays an important rule in computer aided lung nodules detection. However, because of the complex background and structure, accurate and robust extraction of ROIs in medical image still remains a problem. Aim at this problem, a two-stage operations joint filter: Hessian- LoB, is proposed. The first stage is blobs (which being taken as candidate ROIs) detection and the second stage is ROIs extraction. In the first stage, the derivatives of a Hessian matrix at multiple scales are convolved with input images to localize blobs. Then in the second stage, Laplacian of bilateral filter ( LoB) is convolved with the detected blobs to extract the final ROIs. Experiments show that the proposed filter can deal with images with noise and low brightness contrast, and is effectively in ROI extraction for lung nodule detection.
Signal reconstruction of the slow wave and spike potential from electrogastrogram
Authors: Qin Shujia , Ding Wei , Miao Lei , Xi Ning , Li Hongyi , Yang Chunmin
Published: August 2015

Abstract

The gastric slow wave and the spike potential can correspondingly represent the rhythm and the intensity of stomach motility. Because of the filtering effect of biological tissue, electrogastrogram (EGG) cannot measure the spike potential on the abdominal surface in the time domain. Thus, currently the parameters of EGG adopted by clinical applications are only the characteristics of the slow wave, such as the dominant frequency, the dominant power and the instability coefficients. The limitation of excluding the spike potential analyses hinders EGG from being a diagnosis to comprehensively reveal the motility status of the stomach. To overcome this defect, this paper a) presents an EGG reconstruction method utilizing the specified signal components decomposed by the discrete wavelet packet transform, and b) obtains a frequency band for the human gastric spike potential through fasting and postprandial cutaneous EGG experiments for twenty-five human volunteers. The results indicate the lower bound of the human gastric spike potential frequency is 0.96±0.20 Hz (58±12 cpm), and the upper bound is 1.17±0.23 Hz (70±14 cpm), both of which have not been reported before to the best of our knowledge. As an auxiliary validation of the proposed method, synchronous serosa-surface EGG acquisitions are carried out for two dogs. The frequency band results for the gastric spike potential of the two dogs are respectively 0.83–0.90 Hz (50–54 cpm) and 1.05–1.32 Hz (63–79 cpm). They lie in the reference range 50–80 cpm proposed in previous literature, showing the feasibility of the reconstruction method in this paper.
Binary particle swarm optimization for frequency band selection in motor imagery based brain-computer interfaces
Authors: Wei Qingguo , Wei Zhonghai
Published: August 2015

Abstract

A brain-computer interface (BCI) enables people suffering from affective neurological diseases to communicate with the external world. Common spatial pattern (CSP) is an effective algorithm for feature extraction in motor imagery based BCI systems. However, many studies have proved that the performance of CSP depends heavily on the frequency band of EEG signals used for the construction of covariance matrices. The use of different frequency bands to extract signal features may lead to different classification performances, which are determined by the discriminative and complementary information they contain. In this study, the broad frequency band (8-30 Hz) is divided into 10 sub-bands of band width 4 Hz and overlapping 2 Hz. Binary particle swarm optimization (BPSO) is used to find the best sub-band set to improve the performance of CSP and subsequent classification. Experimental results demonstrate that the proposed method achieved an average improvement of 6.91% in cross-validation accuracy when compared to broad band CSP.
The application of EMD in activity recognition based on a single triaxial accelerometer
Authors: Liao Mengjia , Guo Yi , Qin Yajie , Wang Yuanyuan
Published: August 2015

Abstract

Activities recognition using a wearable device is a very popular research field. Among all wearable sensors, the accelerometer is one of the most common sensors due to its versatility and relative ease of use. This paper proposes a novel method for activity recognition based on a single accelerometer. To process the activity information from accelerometer data, two kinds of signal features are extracted. Firstly, five features including the mean, the standard deviation, the entropy, the energy and the correlation are calculated. Then a method called empirical mode decomposition (EMD) is used for the feature extraction since accelerometer data are non-linear and non-stationary. Several time series named intrinsic mode functions (IMFs) can be obtained after the EMD. Additional features will be added by computing the mean and standard deviation of first three IMFs. A classifier called Adaboost is adopted for the final activities recognition. In the experiments, a single sensor is separately positioned in the waist, left thigh, right ankle and right arm. Results show that the classification accuracy is 94.69%, 86.53%, 91.84% and 92.65%, respectively. These relatively high performances demonstrate that activities can be detected irrespective of the position by reducing problems such as the movement constrain and discomfort.
A novel method of diagnosing premature ventricular contraction based on sparse auto-encoder and softmax regression
Authors: Yang Jianli , Bai Yang , Li Guojun , Liu Ming , Liu Xiuling
Published: August 2015

Abstract

Premature ventricular contraction (PVC) is one of the most serious arrhythmias. Without early diagnosis and proper treatment, PVC can result in significant complications. In this paper, a novel feature extraction method based on a sparse auto-encoder (SAE) and softmax regression (SR) classifier was used to differentiate PVCs from other common Non-PVC rhythms, including normal sinus (N), left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contraction (APC), and paced beat (PB) rhythms. The proposed method was analyzed using 40 ECG records obtained from the MIT-BIH Arrhythmia Database. The proposed method exhibited an overall accuracy of 99.4%, with a PVC recognition sensitivity and positive predictability of 97.9% and 91.8%, respectively.
A novel T wave cancellation method based on MAP estimation for P wave extraction
Authors: Wei Chang-An , Dai Huhe
Published: August 2015

Abstract

P wave and T wave in human-body electrocardiogram (ECG) signals often fuse together when atrial premature contract (APC) occurs. P waves within the fused signals are valuable for the measurement of P wave parameters as well as diagnosis of supra-ventricular arrhythmias. However, the problem of extracting P wave from the fused signals is seldom addressed. In this study, a novel T wave cancellation method for P wave extraction based on maximum a posteriori (MAP) estimation is proposed. In order to accurately cancel the T wave within the fused signal, T wave and the timing point of T wave peak are estimated simultaneously. The estimated timing point of T wave peak is used as alignment reference point for T wave subtraction. Simulation results show that the proposed method outperform the traditional T wave cancellation method in terms of both normalized mean square error and cross-correlation index. The results for real ECGs with APC demonstrate that the extracted P waves using the proposed method are more similar to the non-overlapping P waves in terms of morphology than the ones using the traditional T wave cancellation method.
Patient prognosis based on feature extraction, selection and classification of EEG periodic activity
Authors: Sánchez-González Alain , García-Zapirain Begoña , Maestro Saiz Iratxe , Yurrebaso Santamaría Izaskun
Published: August 2015

Abstract

Periodic activity in electroencephalography (PA-EEG) is shown as comprising a series of repetitive wave patterns that may appear in different cerebral regions and are due to many different pathologies. The diagnosis based on PA-EEG is an arduous task for experts in Clinical Neurophysiology, being mainly based on other clinical features of patients. Considering this difficulty in the diagnosis it is also very complicated to establish the prognosis of patients who present PA-EEG. The goal of this paper is to propose a method capable of determining patient prognosis based on characteristics of the PA-EEG activity. The approach, based on a parallel classification architecture and a majority vote system has proven successful by obtaining a success rate of 81.94% in the classification of patient prognosis of our database.
Speckle noise reduction in ultrasound images using a discrete wavelet transform-based image fusion technique
Authors: Choi Hyun Ho , Lee Ju Hwan , Kim Sung Min , Park Sung Yun
Published: August 2015

Abstract

Here, the speckle noise in ultrasonic images is removed using an image fusion-based denoising method. To optimize the denoising performance, each discrete wavelet transform (DWT) and filtering technique was analyzed and compared. In addition, the performances were compared in order to derive the optimal input conditions. To evaluate the speckle noise removal performance, an image fusion algorithm was applied to the ultrasound images, and comparatively analyzed with the original image without the algorithm. As a result, applying DWT and filtering techniques caused information loss and noise characteristics, and did not represent the most significant noise reduction performance. Conversely, an image fusion method applying SRAD-original conditions preserved the key information in the original image, and the speckle noise was removed. Based on such characteristics, the input conditions of SRAD-original had the best denoising performance with the ultrasound images. From this study, the best denoising technique proposed based on the results was confirmed to have a high potential for clinical application.
Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network
Authors: Hwang Yoo Na , Lee Ju Hwan , Kim Ga Young , Jiang Yuan Yuan , Kim Sung Min
Published: August 2015

Abstract

This paper focuses on the improvement of the diagnostic accuracy of focal liver lesions by quantifying the key features of cysts, hemangiomas, and malignant lesions on ultrasound images. The focal liver lesions were divided into 29 cysts, 37 hemangiomas, and 33 malignancies. A total of 42 hybrid textural features that composed of 5 first order statistics, 18 gray level co-occurrence matrices, 18 Law’s, and echogenicity were extracted. A total of 29 key features that were selected by principal component analysis were used as a set of inputs for a feed-forward neural network. For each lesion, the performance of the diagnosis was evaluated by using the positive predictive value, negative predictive value, sensitivity, specificity, and accuracy. The results of the experiment indicate that the proposed method exhibits great performance, a high diagnosis accuracy of over 96% among all focal liver lesion groups (cyst vs. hemangioma, cyst vs. malignant, and hemangioma vs. malignant) on ultrasound images. The accuracy was slightly increased when echogenicity was included in the optimal feature set. These results indicate that it is possible for the proposed method to be applied clinically.
Enhance contrast in PCA based beamformers using smoothing kernel
Authors: Aliabadi Saeid , Yu Jinhua , Wang Yuanyuan
Published: August 2015

Abstract

The contrast and resolution have trade-off in medical ultrasound imaging. Most of adaptive beamformer can enhance the imaging resolution significantly but not improve the contrast at the same time. The principal component analysis (PCA) based beamformers such as the eigenspace-based minimum variance (ESBMV) beamformer provide a good imaging resolution. Neighbors of the focal point include the common noise, interface and signal components. Echo signal of the neighbor points can be used to suppress the noise and extract the signal component of the focal point. Based on this idea, in order to improve the quality of PCA based beamformers both in the imaging contrast and resolution, a novel beamforming method is proposed. This proposed beamformer utilizes a kernel to select neighbor points. The number of eigenvectors is estimated by using any PCA method. Then the number of selected eigenvectors for each focal point is compared with the number of selected eigenvectors of its neighbor points and is changed to a new value. The selected eigenvectors of the covariance matrix is used to construct the signal subspace. The estimated signal subspace is projected onto the minimum variance (MV) weight vector to calculate the desire weight vector. Results of experiments show that the proposed beamformer can improve the imaging contrast significantly while keeping the resolution quality similar to ESBMV beamformer.
Fast axial and lateral displacement estimation in myocardial elastography based on RF signals with predictions
Authors: Zhang Yaonan , Sun Tingting , Teng Yueyang , Li Hong , Kang Yan
Published: August 2015

Abstract

Myocardial elastography (ME) is a strain imaging technique used to diagnose myocardial diseases. Axial and lateral displacement calculations are pre-conditions of strain image acquisition in ME. W.N. Lee et al. proposed a normalized cross-correlation (NCC) and recorrelation method to obtain both axial and lateral displacements in ME. However, this method is not noise-resistant and of high computational cost. This paper proposes a predicted fast NCC algorithm based on W.N. Lee’s method, with the additions of sum-table NCC and a displacement prediction algorithm, to obtain efficient and accurate axial and lateral displacements. Compared to experiments based on the NCC and recorrelation methods, the results indicate that the proposed NCC method is much faster (predicted fast NCC method, 69.75s for a 520×260 image; NCC and recorrelation method, 1092.25s for a 520×260 image) and demonstrates better performance in eliminating decorrelation noise (SNR of the axial and lateral strain using the proposed method, 5.87 and 1.25, respectively; SNR of the axial and lateral strain using the NCC and recorrelation method, 1.48 and 1.09, respectively).
Scattered radiation doses absorbed by technicians at different distances from X-ray exposure: Experiments on prosthesis
Authors: Chiang Hsien-Wen , Liu Ya-Ling , Chen Tou-Rong , Chen Chun-Lon , Chiang Hsien-Jen , Chao Shin-Yu
Published: August 2015

Abstract

This work aimed to investigate the spatial distribution of scattered radiation doses induced by exposure to the portable X-ray, the C-arm machine, and to simulate the radiologist without a shield of lead clothing, radiation doses absorbed by medical staff at 2 m from the central exposure point. Material and method: With the adoption of the Rando Phantom, several frequently X-rayed body parts were exposed to X-ray radiation, and the scattered radiation doses were measured by ionization chamber dosimeters at various angles from the patient. Assuming that the central point of the X-ray was located at the belly button, five detection points were distributed in the operation room at 1 m above the ground and 1-2 m from the central point horizontally. Results: The radiation dose measured at point B was the lowest, and the scattered radiation dose absorbed by the prosthesis from the X-ray’s vertical projection was 0.07 ±0.03 μGy, which was less than the background radiation levels. The Fluke biomedical model 660-5DE (400 cc) and 660-3DE (4 cc) ion chambers were used to detect air dose at a distance of approximately two meters from the central point. The AP projection radiation doses at point B was the lowest (0.07±0.03 μGy) and the radiation doses at point D was the highest (0.26±0.08 μGy) .Only taking the vertical projection into account, the radiation doses at point B was the lowest (0.52 μGy), and the radiation doses at point E was the highest (4 μGy).The PA projection radiation at point B was the lowest (0.36 μGy) and the radiation doses at point E was the highest(2.77 μGy), occupying 10-32% of the maximum doses. The maximum dose in five directions was nine times to the minimum dose. When the PX and the C-arm machine were used, the radiation doses at a distance of 2 m were attenuated to the background radiation level. The radiologist without a lead shield should stand at point B of patient’s feet. Accordingly, teaching materials on radiation safety for radiological interns and clinical technicians were formulated.
Optimization of the imaging quality of 64-slice CT acquisition protocol using Taguchi analysis: A phantom study
Authors: Pan Lung Fa , Erdene Erdenetsetseg , Chen Chun Chi , Pan Lung Kwang
Published: August 2015

Abstract

In this study, the phantom imaging quality of 64-slice CT acquisition protocol was quantitatively evaluated using Taguchi. The phantom acrylic line group was designed and assembled with multiple layers of solid water plate in order to imitate the adult abdomen, and scanned with Philips brilliance CT in order to simulate a clinical examination. According to the Taguchi L8(27) orthogonal array, four major factors of the acquisition protocol were optimized, including (A) CT slice thickness, (B) the image reconstruction filter type, (C) the spiral CT pitch, and (D) the matrix size. The reconstructed line group phantom image was counted by four radiologists for three discrete rounds in order to obtain the averages and standard deviations of the line counts and the corresponding signal to noise ratios (S/N). The quantified S/N values were analyzed and the optimal combination of the four factor settings was determined to be comprised of (A) a 1-mm thickness, (B) a sharp filter type, (C) a 1.172 spiral CT pitch, and (D) a 1024×1024 matrix size. The dominant factors included the (A) filter type and the cross interaction between the filter type and CT slice thickness (A×B). The minor factors were determined to be (C) the spiral CT pitch and (D) the matrix size since neither was capable of yielding a 95% confidence level in the ANOVA test.
Variation of patient imaging doses with scanning parameters for linac-integrated kilovoltage cone beam CT
Authors: Liao Xiongfei , Wang Yunlai , Lang Jinyi , Wang Pei , Li Jie , Ge Ruigang , Yang Jack
Published: August 2015

Abstract

To evaluate the Elekta kilovoltage CBCT doses and the associated technical protocols with patient dosimetry estimation. Image guidance technique with cone-beam CT (CBCT) in radiation oncology on a daily basis can deliver a significant dose to the patient. To evaluate the patient dose from LINAC-integrated kV cone beam CT imaging in image-guided radiotherapy. CT dose index (CTDI) were measured with PTW TM30009 CT ion chamber in air, in head phantom and body phantom, respectively; with different combinations of tube voltage, current, exposure time per frame, collimator and gantry rotation range. Dose length products (DLP) were subsequently calculated to account for volume integration effects. The CTDI and DLP were also compared to AcQSim™ simulator CT for routine clinical protocols. Both CTDIair and CTDIw depended quadratically on the voltage, while linearly on milliampere x seconds (mAs) settings. It was shown that CTDIw and DLP had very close relationship with the collimator settings and the gantry rotation ranges. Normalized CTDIw for Elekta XVI™ CBCT was lower than that of ACQSim simulator CT owing to its pulsed radiation output characteristics. CTDIw can be used to assess the patient dose in CBCT due to its simplicity for measurement and reproducibility. Regular measurement should be performed in QA & QC program. Optimal image parameters should be chosen to reduce patient dose during CBCT.