bluuebunny/crossref_metadata_2025_split huggingface dataset:
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.