Computational and Mathematical Methods in Medicine The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. Eradication of Ebola Based on Dynamic Programming Wed, 25 May 2016 11:58:30 +0000 This paper mainly studies the eradication of the Ebola virus, proposing a scientific system, including three modules for the eradication of Ebola virus. Firstly, we build a basic model combined with nonlinear incidence rate and maximum treatment capacity. Secondly, we use the dynamic programming method and the Dijkstra Algorithm to set up M-S (storage) and several delivery locations in West Africa. Finally, we apply the previous results to calculate the total cost, production cost, storage cost, and shortage cost. Jia-Ming Zhu, Lu Wang, and Jia-Bao Liu Copyright © 2016 Jia-Ming Zhu et al. All rights reserved. Segmentation of White Blood Cell from Acute Lymphoblastic Leukemia Images Using Dual-Threshold Method Sun, 22 May 2016 10:07:49 +0000 We propose a dual-threshold method based on a strategic combination of RGB and HSV color space for white blood cell (WBC) segmentation. The proposed method consists of three main parts: preprocessing, threshold segmentation, and postprocessing. In the preprocessing part, we get two images for further processing: one contrast-stretched gray image and one H component image from transformed HSV color space. In the threshold segmentation part, a dual-threshold method is proposed for improving the conventional single-threshold approaches and a golden section search method is used for determining the optimal thresholds. For the postprocessing part, mathematical morphology and median filtering are utilized to denoise and remove incomplete WBCs. The proposed method was tested in segmenting the lymphoblasts on a public Acute Lymphoblastic Leukemia (ALL) image dataset. The results show that the performance of the proposed method is better than single-threshold approach independently performed in RGB and HSV color space and the overall single WBC segmentation accuracy reaches 97.85%, showing a good prospect in subsequent lymphoblast classification and ALL diagnosis. Yan Li, Rui Zhu, Lei Mi, Yihui Cao, and Di Yao Copyright © 2016 Yan Li et al. All rights reserved. Inverse Problem for Color Doppler Ultrasound-Assisted Intracardiac Blood Flow Imaging Sun, 22 May 2016 06:16:57 +0000 For the assessment of the left ventricle (LV), echocardiography has been widely used to visualize and quantify geometrical variations of LV. However, echocardiographic image itself is not sufficient to describe a swirling pattern which is a characteristic blood flow pattern inside LV without any treatment on the image. We propose a mathematical framework based on an inverse problem for three-dimensional (3D) LV blood flow reconstruction. The reconstruction model combines the incompressible Navier-Stokes equations with one-direction velocity component of the synthetic flow data (or color Doppler data) from the forward simulation (or measurement). Moreover, time-varying LV boundaries are extracted from the intensity data to determine boundary conditions of the reconstruction model. Forward simulations of intracardiac blood flow are performed using a fluid-structure interaction model in order to obtain synthetic flow data. The proposed model significantly reduces the local and global errors of the reconstructed flow fields. We demonstrate the feasibility and potential usefulness of the proposed reconstruction model in predicting dynamic swirling patterns inside the LV over a cardiac cycle. Jaeseong Jang, Chi Young Ahn, Jung-Il Choi, and Jin Keun Seo Copyright © 2016 Jaeseong Jang et al. All rights reserved. Basic Hand Gestures Classification Based on Surface Electromyography Thu, 19 May 2016 17:13:56 +0000 This paper presents an innovative classification system for hand gestures using 2-channel surface electromyography analysis. The system developed uses the Support Vector Machine classifier, for which the kernel function and parameter optimisation are conducted additionally by the Cuckoo Search swarm algorithm. The system developed is compared with standard Support Vector Machine classifiers with various kernel functions. The average classification rate of 98.12% has been achieved for the proposed method. Aleksander Palkowski and Grzegorz Redlarski Copyright © 2016 Aleksander Palkowski and Grzegorz Redlarski. All rights reserved. Development of Magnetorheological Resistive Exercise Device for Rowing Machine Wed, 18 May 2016 06:46:05 +0000 Training equipment used by professional sportsmen has a great impact on their sport performance. Most universal exercisers may help only to improve the general physical condition due to the specific kinematics and peculiar resistance generated by their loading units. Training of effective techniques and learning of psychomotor skills are possible only when exercisers conform to the movements and resistance typical for particular sports kinematically and dynamically. Methodology of developing a magnetorheological resistive exercise device for generating the desired law of passive resistance force and its application in a lever-type rowing machine are described in the paper. The structural parameters of a controllable hydraulic cylinder type device were found by means of the computational fluid dynamics simulation performed by ANSYS CFX software. Parameters describing the magnetorheological fluid as non-Newtonian were determined by combining numerical and experimental research of the resistance force generated by the original magnetorheological damper. A structural scheme of the device control system was developed and the variation of the strength of magnetic field that affects the magnetorheological fluid circulating in the device was determined, ensuring a variation of the resistance force on the oar handle adequate for the resistance that occurs during a real boat rowing stroke. Vytautas Grigas, Anatolijus Šulginas, and Pranas Žiliukas Copyright © 2016 Vytautas Grigas et al. All rights reserved. A Novel Medical Freehand Sketch 3D Model Retrieval Method by Dimensionality Reduction and Feature Vector Transformation Tue, 17 May 2016 11:15:46 +0000 To assist physicians to quickly find the required 3D model from the mass medical model, we propose a novel retrieval method, called DRFVT, which combines the characteristics of dimensionality reduction (DR) and feature vector transformation (FVT) method. The DR method reduces the dimensionality of feature vector; only the top low frequency Discrete Fourier Transform coefficients are retained. The FVT method does the transformation of the original feature vector and generates a new feature vector to solve the problem of noise sensitivity. The experiment results demonstrate that the DRFVT method achieves more effective and efficient retrieval results than other proposed methods. Zhang Jing and Kang Bao Sheng Copyright © 2016 Zhang Jing and Kang Bao Sheng. All rights reserved. A Novel Hepatocellular Carcinoma Image Classification Method Based on Voting Ranking Random Forests Tue, 17 May 2016 11:15:02 +0000 This paper proposed a novel voting ranking random forests (VRRF) method for solving hepatocellular carcinoma (HCC) image classification problem. Firstly, in preprocessing stage, this paper used bilateral filtering for hematoxylin-eosin (HE) pathological images. Next, this paper segmented the bilateral filtering processed image and got three different kinds of images, which include single binary cell image, single minimum exterior rectangle cell image, and single cell image with a size of . After that, this paper defined atypia features which include auxiliary circularity, amendment circularity, and cell symmetry. Besides, this paper extracted some shape features, fractal dimension features, and several gray features like Local Binary Patterns (LBP) feature, Gray Level Cooccurrence Matrix (GLCM) feature, and Tamura features. Finally, this paper proposed a HCC image classification model based on random forests and further optimized the model by voting ranking method. The experiment results showed that the proposed features combined with VRRF method have a good performance in HCC image classification problem. Bingbing Xia, Huiyan Jiang, Huiling Liu, and Dehui Yi Copyright © 2016 Bingbing Xia et al. All rights reserved. Ultrasound Shear Wave Simulation of Breast Tumor Using Nonlinear Tissue Elasticity Mon, 16 May 2016 13:23:08 +0000 Shear wave elasticity imaging (SWEI) can assess the elasticity of tissues, but the shear modulus estimated in SWEI is often less sensitive to a subtle change of the stiffness that produces only small mechanical contrast to the background tissues. Because most soft tissues exhibit mechanical nonlinearity that differs in tissue types, mechanical contrast can be enhanced if the tissues are compressed. In this study, a finite element- (FE-) based simulation was performed for a breast tissue model, which consists of a circular (: 10 mm, hard) tumor and surrounding tissue (soft). The SWEI was performed with 0% to 30% compression of the breast tissue model. The shear modulus of the tumor exhibited noticeably high nonlinearity compared to soft background tissue above 10% overall applied compression. As a result, the elastic modulus contrast of the tumor to the surrounding tissue was increased from 0.46 at 0% compression to 1.45 at 30% compression. Dae Woo Park Copyright © 2016 Dae Woo Park. All rights reserved. On the Extremal Wiener Polarity Index of Hückel Graphs Mon, 09 May 2016 16:36:57 +0000 Graphs are used to model chemical compounds and drugs. In the graphs, each vertex represents an atom of molecule and edges between the corresponding vertices are used to represent covalent bounds between atoms. The Wiener polarity index of a graph is the number of unordered pairs of vertices of such that the distance between and is equal to 3. The trees and unicyclic graphs with perfect matching, of which all vertices have degrees not greater than three, are referred to as the Hückel trees and unicyclic Hückel graphs, respectively. In this paper, we first consider the smallest and the largest Wiener polarity index among all Hückel trees on vertices and characterize the corresponding extremal graphs. Then we obtain an upper and lower bound for the Wiener polarity index of unicyclic Hückel graphs on vertices. Hongzhuan Wang Copyright © 2016 Hongzhuan Wang. All rights reserved. Hemodynamic Study of Flow Remodeling Stent Graft for the Treatment of Highly Angulated Abdominal Aortic Aneurysm Mon, 09 May 2016 13:44:49 +0000 This study investigates the effect of a novel flow remodeling stent graft (FRSG) on the hemodynamic characteristics in highly angulated abdominal aortic aneurysm based on computational fluid dynamics (CFD) approach. An idealized aortic aneurysm with varying aortic neck angulations was constructed and CFD simulations were performed on nonstented models and stented models with FRSG. The influence of FRSG intervention on the hemodynamic performance is analyzed and compared in terms of flow patterns, wall shear stress (WSS), and pressure distribution in the aneurysm. The findings showed that aortic neck angulations significantly influence the velocity flow field in nonstented models, with larger angulations shifting the mainstream blood flow towards the center of the aorta. By introducing FRSG treatment into the aneurysm, erratic flow recirculation pattern in the aneurysm sac diminishes while the average velocity magnitude in the aneurysm sac was reduced in the range of 39% to 53%. FRSG intervention protects the aneurysm against the impacts of high velocity concentrated flow and decreases wall shear stress by more than 50%. The simulation results highlighted that FRSG may effectively treat aneurysm with high aortic neck angulations via the mechanism of promoting thrombus formation and subsequently led to the resorption of the aneurysm. Siang Lin Yeow and Hwa Liang Leo Copyright © 2016 Siang Lin Yeow and Hwa Liang Leo. All rights reserved. Can Ambulatory Blood Pressure Variability Contribute to Individual Cardiovascular Risk Stratification? Mon, 09 May 2016 11:29:09 +0000 Objective. The aim of this study is to define the normal range for average real variability (ARV) and to establish whether it can be considered as an additional cardiovascular risk factor. Methods. In this observational study, 110 treated hypertensive patients were included and admitted for antihypertensive treatment adjustment. Circadian blood pressure was recorded with validated devices. Blood pressure variability (BPV) was assessed according to the ARV definition. Based on their variability, patients were classified into low, medium, and high variability groups using the fuzzy -means algorithm. To assess cardiovascular risk, blood samples were collected. Characteristics of the groups were compared by ANOVA tests. Results. Low variability was defined as ARV below 9.8 mmHg (32 patients), medium as 9.8–12.8 mmHg (48 patients), and high variability above 12.8 mmHg (30 patients). Mean systolic blood pressure was 131.2 ± 16.7, 135.0 ± 12.1, and 141.5 ± 11.4 mmHg in the low, medium, and high variability groups, respectively (). Glomerular filtration rate was 78.6 ± 29.3, 74.8 ± 26.4, and in the low, medium, and high variability groups, respectively (). Conclusion. Increased values of average real variability represent an additional cardiovascular risk factor. Therefore, reducing BP variability might be as important as achieving optimal BP levels, but there is need for further studies to define a widely acceptable threshold value. Annamária Magdás, László Szilágyi, and Alexandru Incze Copyright © 2016 Annamária Magdás et al. All rights reserved. A Time-Delayed Mathematical Model for Tumor Growth with the Effect of a Periodic Therapy Thu, 05 May 2016 16:31:05 +0000 A time-delayed mathematical model for tumor growth with the effect of periodic therapy is studied. The establishment of the model is based on the reaction-diffusion dynamics and mass conservation law and is considered with a time delay in cell proliferation process. Sufficient conditions for the global stability of tumor free equilibrium are given. We also prove that if external concentration of nutrients is large the tumor will not disappear and the conditions under which there exist periodic solutions to the model are also determined. Results are illustrated by computer simulations. Shihe Xu, Xiangqing Wei, and Fangwei Zhang Copyright © 2016 Shihe Xu et al. All rights reserved. The Numerical Study of the Hemodynamic Characteristics in the Patient-Specific Intracranial Aneurysms before and after Surgery Thu, 05 May 2016 11:17:53 +0000 The patient-specific pre- and postsurgery cerebral arterial geometries in the study were reconstructed from computed tomography angiography (CTA). Three-dimensional computational fluid dynamics models were used to investigate the hemodynamic phenomena in the cerebral arteries before and after surgery of the aneurysm under realistic conditions. CFD simulations for laminar flow of incompressible Newtonian fluid were conducted by using commercial software, ANSYS v15, with the rigid vascular wall assumption. The study found that the flow patterns with the complex vortical structures inside the aneurysm were similar. We also found that the inflow jet streams were coming strongly in aneurysm sac in the presurgery models, while the flow patterns in postsurgery models were quite different from those in presurgery models. The average wall shear stress after surgery for model 1 was approximately three times greater than that before surgery, while it was about twenty times greater for model 2. The area of low WSS in the daughter saccular aneurysm region in model 2 is associated with aneurysm rupture. Thus the distribution of WSS in aneurysm region provides useful prediction for the risk of aneurysm rupture. Jun Soo Byun, Sun-Young Choi, and Taewon Seo Copyright © 2016 Jun Soo Byun et al. All rights reserved. A Mathematical Model of Regenerative Axon Growing along Glial Scar after Spinal Cord Injury Wed, 04 May 2016 14:30:41 +0000 A major factor in the failure of central nervous system (CNS) axon regeneration is the formation of glial scar after the injury of CNS. Glial scar generates a dense barrier which the regenerative axons cannot easily pass through or by. In this paper, a mathematical model was established to explore how the regenerative axons grow along the surface of glial scar or bypass the glial scar. This mathematical model was constructed based on the spinal cord injury (SCI) repair experiments by transplanting Schwann cells as bridge over the glial scar. The Lattice Boltzmann Method (LBM) was used in this model for three-dimensional numerical simulation. The advantage of this model is that it provides a parallel and easily implemented algorithm and has the capability of handling complicated boundaries. Using the simulated data, two significant conclusions were made in this study: the levels of inhibitory factors on the surface of the glial scar are the main factors affecting axon elongation and when the inhibitory factor levels on the surface of the glial scar remain constant, the longitudinal size of the glial scar has greater influence on the average rate of axon growth than the transverse size. These results will provide theoretical guidance and reference for researchers to design efficient experiments. Xuning Chen and Weiping Zhu Copyright © 2016 Xuning Chen and Weiping Zhu. All rights reserved. Semantic Signature: Comparative Interpretation of Gene Expression on a Semantic Space Tue, 03 May 2016 06:55:02 +0000 Background. Interpretation of microarray data remains challenging because biological meaning should be extracted from enormous numeric matrices and be presented explicitly. Moreover, huge public repositories of microarray dataset are ready to be exploited for comparative analysis. This study aimed to provide a platform where essential implication of a microarray experiment could be visually expressed and various microarray datasets could be intuitively compared. Results. On the semantic space, gene sets from Molecular Signature Database (MSigDB) were plotted as landmarks and their relative distances were calculated by Lin’s semantic similarity measure. By formal concept analysis, a microarray dataset was transformed into a concept lattice with gene clusters as objects and Gene Ontology terms as attributes. Concepts of a lattice were located on the semantic space reflecting semantic distance from landmarks and edges between concepts were drawn; consequently, a specific geographic pattern could be observed from a microarray dataset. We termed a distinctive geography shared by microarray datasets of the same category as “semantic signature.” Conclusions. “Semantic space,” a map of biological entities, could serve as a universal platform for comparative microarray analysis. When microarray data were displayed on the semantic space as concept lattices, “semantic signature,” characteristic geography for a microarray experiment, could be discovered. Jihun Kim, Keewon Kim, and Ju Han Kim Copyright © 2016 Jihun Kim et al. All rights reserved. Lattice Boltzmann Model of 3D Multiphase Flow in Artery Bifurcation Aneurysm Problem Thu, 28 Apr 2016 08:45:32 +0000 This paper simulates and predicts the laminar flow inside the 3D aneurysm geometry, since the hemodynamic situation in the blood vessels is difficult to determine and visualize using standard imaging techniques, for example, magnetic resonance imaging (MRI). Three different types of Lattice Boltzmann (LB) models are computed, namely, single relaxation time (SRT), multiple relaxation time (MRT), and regularized BGK models. The results obtained using these different versions of the LB-based code will then be validated with ANSYS FLUENT, a commercially available finite volume- (FV-) based CFD solver. The simulated flow profiles that include velocity, pressure, and wall shear stress (WSS) are then compared between the two solvers. The predicted outcomes show that all the LB models are comparable and in good agreement with the FVM solver for complex blood flow simulation. The findings also show minor differences in their WSS profiles. The performance of the parallel implementation for each solver is also included and discussed in this paper. In terms of parallelization, it was shown that LBM-based code performed better in terms of the computation time required. Aizat Abas, N. Hafizah Mokhtar, M. H. H. Ishak, M. Z. Abdullah, and Ang Ho Tian Copyright © 2016 Aizat Abas et al. All rights reserved. Detecting the Intention to Move Upper Limbs from Electroencephalographic Brain Signals Wed, 27 Apr 2016 09:29:14 +0000 Early decoding of motor states directly from the brain activity is essential to develop brain-machine interfaces (BMI) for natural motor control of neuroprosthetic devices. Hence, this study aimed to investigate the detection of movement information before the actual movement occurs. This information piece could be useful to provide early control signals to drive BMI-based rehabilitation and motor assisted devices, thus providing a natural and active rehabilitation therapy. In this work, electroencephalographic (EEG) brain signals from six healthy right-handed participants were recorded during self-initiated reaching movements of the upper limbs. The analysis of these EEG traces showed that significant event-related desynchronization is present before and during the execution of the movements, predominantly in the motor-related and frequency bands and in electrodes placed above the motor cortex. This oscillatory brain activity was used to continuously detect the intention to move the limbs, that is, to identify the motor phase prior to the actual execution of the reaching movement. The results showed, first, significant classification between relax and movement intention and, second, significant detection of movement intention prior to the onset of the executed movement. On the basis of these results, detection of movement intention could be used in BMI settings to reduce the gap between mental motor processes and the actual movement performed by an assisted or rehabilitation robotic device. Berenice Gudiño-Mendoza, Gildardo Sanchez-Ante, and Javier M. Antelis Copyright © 2016 Berenice Gudiño-Mendoza et al. All rights reserved. Control Law Design for Propofol Infusion to Regulate Depth of Hypnosis: A Nonlinear Control Strategy Wed, 27 Apr 2016 07:25:35 +0000 Maintaining the depth of hypnosis (DOH) during surgery is one of the major objectives of anesthesia infusion system. Continuous administration of Propofol infusion during surgical procedures is essential but increases the undue load of an anesthetist in operating room working in a multitasking setup. Manual and target controlled infusion (TCI) systems are not good at handling instabilities like blood pressure changes and heart rate variability arising due to interpatient variability. Patient safety, large interindividual variability, and less postoperative effects are the main factors to motivate automation in anesthesia. The idea of automated system for Propofol infusion excites the control engineers to come up with a more sophisticated and safe system that handles optimum delivery of drug during surgery and avoids postoperative effects. In contrast to most of the investigations with linear control strategies, the originality of this research work lies in employing a nonlinear control technique, backstepping, to track the desired hypnosis level of patients during surgery. This effort is envisioned to unleash the true capabilities of this nonlinear control technique for anesthesia systems used today in biomedical field. The working of the designed controller is studied on the real dataset of five patients undergoing surgery. The controller tracks the desired hypnosis level within the acceptable range for surgery. Ali Khaqan, Muhammad Bilal, Muhammad Ilyas, Bilal Ijaz, and Raja Ali Riaz Copyright © 2016 Ali Khaqan et al. All rights reserved. Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography Sun, 24 Apr 2016 14:23:59 +0000 The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, -nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while -nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present. İlhan Umut and Güven Çentik Copyright © 2016 İlhan Umut and Güven Çentik. All rights reserved. Frequency and Time Domain Analysis of Foetal Heart Rate Variability with Traditional Indexes: A Critical Survey Mon, 18 Apr 2016 07:09:44 +0000 Monitoring of foetal heart rate and its variability (FHRV) covers an important role in assessing health of foetus. Many analysis methods have been used to get quantitative measures of FHRV. FHRV has been studied in time and in frequency domain and interesting clinical results have been obtained. Nevertheless, a standardized definition of FHRV and a precise methodology to be used for its evaluation are lacking. We carried out a literature overview about both frequency domain analysis (FDA) and time domain analysis (TDA). Then, by using simulated FHR signals, we defined the methodology for FDA. Further, employing more than 400 real FHR signals, we analysed some of the most common indexes, Short Term Variability for TDA and power content of the spectrum bands and sympathovagal balance for FDA, and evaluated their ranges of values, which in many cases are a novelty. Finally, we verified the relationship between these indexes and two important parameters: week of gestation, indicator of foetal growth, and foetal state, classified as active or at rest. Our results indicate that, according to literature, it is necessary to standardize the procedure for FHRV evaluation and to consider week of gestation and foetal state before FHR analysis. Maria Romano, Luigi Iuppariello, Alfonso Maria Ponsiglione, Giovanni Improta, Paolo Bifulco, and Mario Cesarelli Copyright © 2016 Maria Romano et al. All rights reserved. A Novel Automatic Rapid Diagnostic Test Reader Platform Thu, 14 Apr 2016 16:05:29 +0000 A novel automatic Rapid Diagnostic Test (RDT) reader platform is designed to analyze and diagnose target disease by using existing consumer cameras of a laptop-computer or a tablet. The RDT reader is useable with numerous lateral immunochromatographic assays and similar biomedical tests. The system has two different components, which are 3D-printed, low-cost, tiny, and compact stand and a decision program named RDT-AutoReader 2.0. The program takes the image of RDT, crops the region of interest (ROI), and extracts the features from the control end test lines to classify the results as invalid, positive, or negative. All related patient’s personal information, image of ROI, and the e-report are digitally saved and transferred to the related clinician. Condition of the patient and the progress of the disease can be monitored by using the saved data. The reader platform has been tested by taking image from used cassette RDTs of rotavirus (RtV)/adenovirus (AdV) and lateral flow strip RDTs of Helicobacter pylori (H. pylori) before discarding them. The created RDT reader can also supply real-time statistics of various illnesses by using databases and Internet. This can help to inhibit propagation of contagious diseases and to increase readiness against epidemic diseases worldwide. Haydar Ozkan and Osman Semih Kayhan Copyright © 2016 Haydar Ozkan and Osman Semih Kayhan. All rights reserved. Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis Mon, 11 Apr 2016 14:09:21 +0000 Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing. David Cárdenas-Peña, Diego Collazos-Huertas, and German Castellanos-Dominguez Copyright © 2016 David Cárdenas-Peña et al. All rights reserved. Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts Tue, 05 Apr 2016 09:48:36 +0000 Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases. Weiwei Wu, Zhuhuang Zhou, Shuicai Wu, and Yanhua Zhang Copyright © 2016 Weiwei Wu et al. All rights reserved. Mathematical Model of Three Age-Structured Transmission Dynamics of Chikungunya Virus Tue, 05 Apr 2016 06:13:29 +0000 We developed a new age-structured deterministic model for the transmission dynamics of chikungunya virus. The model is analyzed to gain insights into the qualitative features of its associated equilibria. Some of the theoretical and epidemiological findings indicate that the stable disease-free equilibrium is globally asymptotically stable when the associated reproduction number is less than unity. Furthermore, the model undergoes, in the presence of disease induced mortality, the phenomenon of backward bifurcation, where the stable disease-free equilibrium of the model coexists with a stable endemic equilibrium when the associated reproduction number is less than unity. Further analysis of the model indicates that the qualitative dynamics of the model are not altered by the inclusion of age structure. This is further emphasized by the sensitivity analysis results, which shows that the dominant parameters of the model are not altered by the inclusion of age structure. However, the numerical simulations show the flaw of the exclusion of age in the transmission dynamics of chikungunya with regard to control implementations. The exclusion of age structure fails to show the age distribution needed for an effective age based control strategy, leading to a one size fits all blanket control for the entire population. Folashade B. Agusto, Shamise Easley, Kenneth Freeman, and Madison Thomas Copyright © 2016 Folashade B. Agusto et al. All rights reserved. The Virtual Screening of the Drug Protein with a Few Crystal Structures Based on the Adaboost-SVM Sun, 03 Apr 2016 12:42:09 +0000 Using the theory of machine learning to assist the virtual screening (VS) has been an effective plan. However, the quality of the training set may reduce because of mixing with the wrong docking poses and it will affect the screening efficiencies. To solve this problem, we present a method using the ensemble learning to improve the support vector machine to process the generated protein-ligand interaction fingerprint (IFP). By combining multiple classifiers, ensemble learning is able to avoid the limitations of the single classifier’s performance and obtain better generalization. According to the research of virtual screening experiment with SRC and Cathepsin K as the target, the results show that the ensemble learning method can effectively reduce the error because the sample quality is not high and improve the effect of the whole virtual screening process. Meng-yu Wang, Peng Li, and Pei-li Qiao Copyright © 2016 Meng-yu Wang et al. All rights reserved. Fluid Structural Analysis of Urine Flow in a Stented Ureter Thu, 31 Mar 2016 13:08:20 +0000 Many urologists are currently studying new designs of ureteral stents to improve the quality of their operations and the subsequent recovery of the patient. In order to help during this design process, many computational models have been developed to simulate the behaviour of different biological tissues and provide a realistic computational environment to evaluate the stents. However, due to the high complexity of the involved tissues, they usually introduce simplifications to make these models less computationally demanding. In this study, the interaction between urine flow and a double-J stented ureter with a simplified geometry has been analysed. The Fluid-Structure Interaction (FSI) of urine and the ureteral wall was studied using three models for the solid domain: Mooney-Rivlin, Yeoh, and Ogden. The ureter was assumed to be quasi-incompressible and isotropic. Data obtained in previous studies from ex vivo and in vivo mechanical characterization of different ureters were used to fit the mentioned models. The results show that the interaction between the stented ureter and urine is negligible. Therefore, we can conclude that this type of models does not need to include the FSI and could be solved quite accurately assuming that the ureter is a rigid body and, thus, using the more simple Computational Fluid Dynamics (CFD) approach. J. Carlos Gómez-Blanco, F. Javier Martínez-Reina, Domingo Cruz, J. Blas Pagador, Francisco M. Sánchez-Margallo, and Federico Soria Copyright © 2016 J. Carlos Gómez-Blanco et al. All rights reserved. A Bayesian Approach for Evaluation of Determinants of Health System Efficiency Using Stochastic Frontier Analysis and Beta Regression Tue, 29 Mar 2016 07:35:00 +0000 In today’s world, Public expenditures on health are one of the most important issues for governments. These increased expenditures are putting pressure on public budgets. Therefore, health policy makers have focused on the performance of their health systems and many countries have introduced reforms to improve the performance of their health systems. This study investigates the most important determinants of healthcare efficiency for OECD countries using second stage approach for Bayesian Stochastic Frontier Analysis (BSFA). There are two steps in this study. First we measure 29 OECD countries’ healthcare efficiency by BSFA using the data from the OECD Health Database. At second stage, we expose the multiple relationships between the healthcare efficiency and characteristics of healthcare systems across OECD countries using Bayesian beta regression. Talat Şenel and Mehmet Ali Cengiz Copyright © 2016 Talat Şenel and Mehmet Ali Cengiz. All rights reserved. Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques Sun, 27 Mar 2016 09:02:31 +0000 With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC. Kemal Akyol, Baha Şen, and Şafak Bayır Copyright © 2016 Kemal Akyol et al. All rights reserved. The Dynamics of Avian Influenza: Individual-Based Model with Intervention Strategies in Traditional Trade Networks in Phitsanulok Province, Thailand Wed, 23 Mar 2016 09:58:10 +0000 Avian influenza virus subtype H5N1 is endemic to Southeast Asia. In Thailand, avian influenza viruses continue to cause large poultry stock losses. The spread of the disease has a serious impact on poultry production especially among rural households with backyard chickens. The movements and activities of chicken traders result in the spread of the disease through traditional trade networks. In this study, we investigate the dynamics of avian influenza in the traditional trade network in Phitsanulok Province, Thailand. We also propose an individual-based model with intervention strategies to control the spread of the disease. We found that the dynamics of the disease mainly depend on the transmission probability and the virus inactivation period. This study also illustrates the appropriate virus disinfection period and the target for intervention strategies on traditional trade network. The results suggest that good hygiene and cleanliness among household traders and trader of trader areas and ensuring that any equipment used is clean can lead to a decrease in transmission and final epidemic size. These results may be useful to epidemiologists, researchers, and relevant authorities in understanding the spread of avian influenza through traditional trade networks. Chaiwat Wilasang, Anuwat Wiratsudakul, and Sudarat Chadsuthi Copyright © 2016 Chaiwat Wilasang et al. All rights reserved. Signal and Image Processing of Physiological Data: Methods for Diagnosis and Treatment Purposes Thu, 17 Mar 2016 14:15:47 +0000 Anne Humeau-Heurtier, Edite Figueiras, and Joao Cardoso Copyright © 2016 Anne Humeau-Heurtier et al. All rights reserved.