Computational and Mathematical Methods in Medicine The latest articles from Hindawi Publishing Corporation © 2015 , Hindawi Publishing Corporation . All rights reserved. On Better Estimating and Normalizing the Relationship between Clinical Parameters: Comparing Respiratory Modulations in the Photoplethysmogram and Blood Pressure Signal (DPOP versus PPV) Tue, 27 Jan 2015 09:37:12 +0000 DPOP (ΔPOP or Delta-POP) is a noninvasive parameter which measures the strength of respiratory modulations present in the pulse oximeter waveform. It has been proposed as a noninvasive alternative to pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. We considered a number of simple techniques for better determining the underlying relationship between the two parameters. It was shown numerically that baseline-induced signal errors were asymmetric in nature, which corresponded to observation, and we proposed a method which combines a least-median-of-squares estimator with the requirement that the relationship passes through the origin (the LMSO method). We further developed a method of normalization of the parameters through rescaling DPOP using the inverse gradient of the linear fitted relationship. We propose that this normalization method (LMSO-N) is applicable to the matching of a wide range of clinical parameters. It is also generally applicable to the self-normalizing of parameters whose behaviour may change slightly due to algorithmic improvements. Paul S. Addison, Rui Wang, Alberto A. Uribe, and Sergio D. Bergese Copyright © 2015 Paul S. Addison et al. All rights reserved. Nonlinear Bayesian Estimation of BOLD Signal under Non-Gaussian Noise Mon, 26 Jan 2015 07:40:25 +0000 Modeling the blood oxygenation level dependent (BOLD) signal has been a subject of study for over a decade in the neuroimaging community. Inspired from fluid dynamics, the hemodynamic model provides a plausible yet convincing interpretation of the BOLD signal by amalgamating effects of dynamic physiological changes in blood oxygenation, cerebral blood flow and volume. The nonautonomous, nonlinear set of differential equations of the hemodynamic model constitutes the process model while the weighted nonlinear sum of the physiological variables forms the measurement model. Plagued by various noise sources, the time series fMRI measurement data is mostly assumed to be affected by additive Gaussian noise. Though more feasible, the assumption may cause the designed filter to perform poorly if made to work under non-Gaussian environment. In this paper, we present a data assimilation scheme that assumes additive non-Gaussian noise, namely, the -mixture noise, affecting the measurements. The proposed filter MAGSF and the celebrated EKF are put to test by performing joint optimal Bayesian filtering to estimate both the states and parameters governing the hemodynamic model under non-Gaussian environment. Analyses using both the synthetic and real data reveal superior performance of the MAGSF as compared to EKF. Ali Fahim Khan, Muhammad Shahzad Younis, and Khalid Bashir Bajwa Copyright © 2015 Ali Fahim Khan et al. All rights reserved. Autoradiography Imaging in Targeted Alpha Therapy with Timepix Detector Thu, 22 Jan 2015 14:30:25 +0000 There is a lack of data related to activity uptake and particle track distribution in targeted alpha therapy. These data are required to estimate the absorbed dose on a cellular level as alpha particles have a limited range and traverse only a few cells. Tracking of individual alpha particles is possible using the Timepix semiconductor radiation detector. We investigated the feasibility of imaging alpha particle emissions in tumour sections from mice treated with Thorium-227 (using APOMAB), with and without prior chemotherapy and Timepix detector. Additionally, the sensitivity of the Timepix detector to monitor variations in tumour uptake based on the necrotic tissue volume was also studied. Compartmental analysis model was used, based on the obtained imaging data, to assess the Th-227 uptake. Results show that alpha particle, photon, electron, and muon tracks were detected and resolved by Timepix detector. The current study demonstrated that individual alpha particle emissions, resulting from targeted alpha therapy, can be visualised and quantified using Timepix detector. Furthermore, the variations in the uptake based on the tumour necrotic volume have been observed with four times higher uptake for tumours pretreated with chemotherapy than for those without chemotherapy. Ruqaya AL Darwish, Alexander Hugo Staudacher, Eva Bezak, and Michael Paul Brown Copyright © 2015 Ruqaya AL Darwish et al. All rights reserved. Computation of Nonlinear Parameters of Heart Rhythm Using Short Time ECG Segments Thu, 22 Jan 2015 12:14:35 +0000 We propose the method to compute the nonlinear parameters of heart rhythm (correlation dimension and correlation entropy ) using 5-minute ECG recordings preferred for screening of population. Conversion of RR intervals’ time series into continuous function allows getting the new time series with different sampling rate dt. It has been shown that for all dt (250, 200, 125, and 100 ms) the cross-plots of and against embedding dimension for phase-space reconstruction start to level off at . The sample size at different sampling rates varied from 1200 at dt = 250 ms to 3000 at dt = 100 ms. Along with, the and means were not statistically different; that is, the sampling rate did not influence the results. We tested the feasibility of the method in two models: nonlinear heart rhythm dynamics in different states of autonomous nervous system and age-related characteristics of nonlinear parameters. According to the acquired data, the heart rhythm is more complex in childhood and adolescence with more influential parasympathetic influence against the background of elevated activity of sympathetic autonomous nervous system. Berik Koichubekov, Ilya Korshukov, Nazgul Omarbekova, Viktor Riklefs, Marina Sorokina, and Xenia Mkhitaryan Copyright © 2015 Berik Koichubekov et al. All rights reserved. Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery Thu, 22 Jan 2015 11:55:22 +0000 Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods. Haemwaan Sivaraks and Chotirat Ann Ratanamahatana Copyright © 2015 Haemwaan Sivaraks and Chotirat Ann Ratanamahatana. All rights reserved. The Direct Assignment Option as a Modular Design Component: An Example for the Setting of Two Predefined Subgroups Thu, 15 Jan 2015 08:20:08 +0000 Background. A phase II design with an option for direct assignment (stop randomization and assign all patients to experimental treatment based on interim analysis, IA) for a predefined subgroup was previously proposed. Here, we illustrate the modularity of the direct assignment option by applying it to the setting of two predefined subgroups and testing for separate subgroup main effects. Methods. We power the 2-subgroup direct assignment option design with 1 IA (DAD-1) to test for separate subgroup main effects, with assessment of power to detect an interaction in a post-hoc test. Simulations assessed the statistical properties of this design compared to the 2-subgroup balanced randomized design with 1 IA, BRD-1. Different response rates for treatment/control in subgroup 1 (0.4/0.2) and in subgroup 2 (0.1/0.2, 0.4/0.2) were considered. Results. The 2-subgroup DAD-1 preserves power and type I error rate compared to the 2-subgroup BRD-1, while exhibiting reasonable power in a post-hoc test for interaction. Conclusion. The direct assignment option is a flexible design component that can be incorporated into broader design frameworks, while maintaining desirable statistical properties, clinical appeal, and logistical simplicity. Ming-Wen An, Xin Lu, Daniel J. Sargent, and Sumithra J. Mandrekar Copyright © 2015 Ming-Wen An et al. All rights reserved. Nominated Texture Based Cervical Cancer Classification Wed, 14 Jan 2015 11:44:46 +0000 Accurate classification of Pap smear images becomes the challenging task in medical image processing. This can be improved in two ways. One way is by selecting suitable well defined specific features and the other is by selecting the best classifier. This paper presents a nominated texture based cervical cancer (NTCC) classification system which classifies the Pap smear images into any one of the seven classes. This can be achieved by extracting well defined texture features and selecting best classifier. Seven sets of texture features (24 features) are extracted which include relative size of nucleus and cytoplasm, dynamic range and first four moments of intensities of nucleus and cytoplasm, relative displacement of nucleus within the cytoplasm, gray level cooccurrence matrix, local binary pattern histogram, tamura features, and edge orientation histogram. Few types of support vector machine (SVM) and neural network (NN) classifiers are used for the classification. The performance of the NTCC algorithm is tested and compared to other algorithms on public image database of Herlev University Hospital, Denmark, with 917 Pap smear images. The output of SVM is found to be best for the most of the classes and better results for the remaining classes. Edwin Jayasingh Mariarputham and Allwin Stephen Copyright © 2015 Edwin Jayasingh Mariarputham and Allwin Stephen. All rights reserved. Image Reconstruction for Diffuse Optical Tomography Based on Radiative Transfer Equation Wed, 14 Jan 2015 08:44:00 +0000 Diffuse optical tomography is a novel molecular imaging technology for small animal studies. Most known reconstruction methods use the diffusion equation (DA) as forward model, although the validation of DA breaks down in certain situations. In this work, we use the radiative transfer equation as forward model which provides an accurate description of the light propagation within biological media and investigate the potential of sparsity constraints in solving the diffuse optical tomography inverse problem. The feasibility of the sparsity reconstruction approach is evaluated by boundary angular-averaged measurement data and internal angular-averaged measurement data. Simulation results demonstrate that in most of the test cases the reconstructions with sparsity regularization are both qualitatively and quantitatively more reliable than those with standard regularization. Results also show the competitive performance of the split Bregman algorithm for the DOT image reconstruction with sparsity regularization compared with other existing algorithms. Bo Bi, Bo Han, Weimin Han, Jinping Tang, and Li Li Copyright © 2015 Bo Bi et al. All rights reserved. Extraction of Heart Rate Variability from Smartphone Photoplethysmograms Mon, 12 Jan 2015 14:34:13 +0000 Heart rate variability (HRV) is a useful clinical tool for autonomic function assessment and cardiovascular diseases diagnosis. It is traditionally calculated from a dedicated medical electrocardiograph (ECG). In this paper, we demonstrate that HRV can also be extracted from photoplethysmograms (PPG) obtained by the camera of a smartphone. Sixteen HRV parameters, including time-domain, frequency-domain, and nonlinear parameters, were calculated from PPG captured by a smartphone for 30 healthy subjects and were compared with those derived from ECG. The statistical results showed that 14 parameters (AVNN, SDNN, CV, RMSSD, SDSD, TP, VLF, LF, HF, LF/HF, nLF, nHF, SD1, and SD2) from PPG were highly correlated (, ) with those from ECG, and 7 parameters (AVNN, TP, VLF, LF, HF, nLF, and nHF) from PPG were in good agreement with those from ECG within the acceptable limits. In addition, five different algorithms to detect the characteristic points of PPG wave were also investigated: peak point (PP), valley point (VP), maximum first derivative (M1D), maximum second derivative (M2D), and tangent intersection (TI). The results showed that M2D and TI algorithms had the best performance. These results suggest that the smartphone might be used for HRV measurement. Rong-Chao Peng, Xiao-Lin Zhou, Wan-Hua Lin, and Yuan-Ting Zhang Copyright © 2015 Rong-Chao Peng et al. All rights reserved. Extracting Information about the Rotator Cuff from Magnetic Resonance Images Using Deterministic and Random Techniques Mon, 12 Jan 2015 11:18:06 +0000 We consider some methods to extract information about the rotator cuff based on magnetic resonance images; the study aims to define an alternative method of display that might facilitate the detection of partial tears in the supraspinatus tendon. Specifically, we are going to use families of ellipsoidal triangular patches to cover the humerus head near the affected area. These patches are going to be textured and displayed with the information of the magnetic resonance images using the trilinear interpolation technique. For the generation of points to texture each patch, we propose a new method that guarantees the uniform distribution of its points using a random statistical method. Its computational cost, defined as the average computing time to generate a fixed number of points, is significantly lower as compared with deterministic and other standard statistical techniques. F. A. De Los Ríos and M. Paluszny Copyright © 2015 F. A. De Los Ríos and M. Paluszny. All rights reserved. A Computer-Aided Diagnosis System for Dynamic Contrast-Enhanced MR Images Based on Level Set Segmentation and ReliefF Feature Selection Tue, 06 Jan 2015 06:31:17 +0000 This study established a fully automated computer-aided diagnosis (CAD) system for the classification of malignant and benign masses via breast magnetic resonance imaging (BMRI). A breast segmentation method consisting of a preprocessing step to identify the air-breast interfacing boundary and curve fitting for chest wall line (CWL) segmentation was included in the proposed CAD system. The Chan-Vese (CV) model level set (LS) segmentation method was adopted to segment breast mass and demonstrated sufficiently good segmentation performance. The support vector machine (SVM) classifier with ReliefF feature selection was used to merge the extracted morphological and texture features into a classification score. The accuracy, sensitivity, and specificity measurements for the leave-half-case-out resampling method were 92.3%, 98.2%, and 76.2%, respectively. For the leave-one-case-out resampling method, the measurements were 90.0%, 98.7%, and 73.8%, respectively. Zhiyong Pang, Dongmei Zhu, Dihu Chen, Li Li, and Yuanzhi Shao Copyright © 2015 Zhiyong Pang et al. All rights reserved. Integrated Approach in Systems Biology Wed, 31 Dec 2014 11:44:42 +0000 Huiru Zheng, Rui Jiang, and Zhongming Zhao Copyright © 2014 Huiru Zheng et al. All rights reserved. Mathematical Methods in Biomedical Imaging 2014 Sun, 28 Dec 2014 07:56:43 +0000 Peng Feng, Kumar Durai, Fenglin Liu, and Xiaobo Qu Copyright © 2014 Peng Feng et al. All rights reserved. Human Atrial Cell Models to Analyse Haemodialysis-Related Effects on Cardiac Electrophysiology: Work in Progress Tue, 23 Dec 2014 07:45:26 +0000 During haemodialysis (HD) sessions, patients undergo alterations in the extracellular environment, mostly concerning plasma electrolyte concentrations, pH, and volume, together with a modification of sympathovagal balance. All these changes affect cardiac electrophysiology, possibly leading to an increased arrhythmic risk. Computational modeling may help to investigate the impact of HD-related changes on atrial electrophysiology. However, many different human atrial action potential (AP) models are currently available, all validated only with the standard electrolyte concentrations used in experiments. Therefore, they may respond in different ways to the same environmental changes. After an overview on how the computational approach has been used in the past to investigate the effect of HD therapy on cardiac electrophysiology, the aim of this work has been to assess the current state of the art in human atrial AP models, with respect to the HD context. All the published human atrial AP models have been considered and tested for electrolytes, volume changes, and different acetylcholine concentrations. Most of them proved to be reliable for single modifications, but all of them showed some drawbacks. Therefore, there is room for a new human atrial AP model, hopefully able to physiologically reproduce all the HD-related effects. At the moment, work is still in progress in this specific field. Elisa Passini, Simonetta Genovesi, and Stefano Severi Copyright © 2014 Elisa Passini et al. All rights reserved. Automatic Segmentation of Anatomical Structures from CT Scans of Thorax for RTP Thu, 18 Dec 2014 07:53:06 +0000 Modern radiotherapy techniques are vulnerable to delineation inaccuracies owing to the steep dose gradient around the target. In this aspect, accurate contouring comprises an indispensable part of optimal radiation treatment planning (RTP). We suggest a fully automated method to segment the lungs, trachea/main bronchi, and spinal canal accurately from computed tomography (CT) scans of patients with lung cancer to use for RTP. For this purpose, we developed a new algorithm for inclusion of excluded pathological areas into the segmented lungs and a modified version of the fuzzy segmentation by morphological reconstruction for spinal canal segmentation and implemented some image processing algorithms along with them. To assess the accuracy, we performed two comparisons between the automatically obtained results and the results obtained manually by an expert. The average volume overlap ratio values range between 94.30 ± 3.93% and 99.11 ± 0.26% on the two different datasets. We obtained the average symmetric surface distance values between the ranges of 0.28 ± 0.21 mm and 0.89 ± 0.32 mm by using the same datasets. Our method provides favorable results in the segmentation of CT scans of patients with lung cancer and can avoid heavy computational load and might offer expedited segmentation that can be used in RTP. Emin Emrah Özsavaş, Ziya Telatar, Bahar Dirican, Ömer Sağer, and Murat Beyzadeoğlu Copyright © 2014 Emin Emrah Özsavaş et al. All rights reserved. Mathematical Modeling of Radiofrequency Ablation for Varicose Veins Thu, 18 Dec 2014 00:10:54 +0000 We present a three-dimensional mathematical model for the study of radiofrequency ablation (RFA) with blood flow for varicose vein. The model designed to analyze temperature distribution heated by radiofrequency energy and cooled by blood flow includes a cylindrically symmetric blood vessel with a homogeneous vein wall. The simulated blood velocity conditions are U = 0, 1, 2.5, 5, 10, 20, and 40 mm/s. The lower the blood velocity, the higher the temperature in the vein wall and the greater the tissue damage. The region that is influenced by temperature in the case of the stagnant flow occupies approximately 28.5% of the whole geometry, while the region that is influenced by temperature in the case of continuously moving electrode against the flow direction is about 50%. The generated RF energy induces a temperature rise of the blood in the lumen and leads to an occlusion of the blood vessel. The result of the study demonstrated that higher blood velocity led to smaller thermal region and lower ablation efficiency. Since the peak temperature along the venous wall depends on the blood velocity and pullback velocity, the temperature distribution in the model influences ablation efficiency. The vein wall absorbs more energy in the low pullback velocity than in the high one. Sun Young Choi, Byung Kook Kwak, and Taewon Seo Copyright © 2014 Sun Young Choi et al. All rights reserved. Mathematical Analysis of Non-Newtonian Blood Flow in Stenosis Narrow Arteries Wed, 17 Dec 2014 07:04:37 +0000 The flow of blood in narrow arteries with bell-shaped mild stenosis is investigated that treats blood as non-Newtonian fluid by using the K-L model. When skin friction and resistance of blood flow are normalized with respect to non-Newtonian blood in normal artery, the results present the effect of stenosis length. When skin friction and resistance of blood flow are normalized with respect to Newtonian blood in stenosis artery, the results present the effect of non-Newtonian blood. The effect of stenosis length and effect of non-Newtonian fluid on skin friction are consistent with the Casson model in which the skin friction increases with the increase of ither stenosis length or the yield stress but the skin friction decreases with the increase of plasma viscosity coefficient. The effect of stenosis length and effect of non-Newtonian fluid on resistance of blood flow are contradictory. The resistance of blood flow (when normalized by non-Newtonian blood in normal artery) increases when either the plasma viscosity coefficient or the yield stress increases, but it decreases with the increase of stenosis length. The resistance of blood flow (when normalized by Newtonian blood in stenosis artery) decreases when either the plasma viscosity coefficient or the yield stress increases, but it decreases with the increase of stenosis length. Somchai Sriyab Copyright © 2014 Somchai Sriyab. All rights reserved. Effect of the Polydispersity of RBCs on the Recovery Rate of RBCs during the Removal of CPAs Mon, 15 Dec 2014 07:29:16 +0000 In the process of removing cryoprotectants from cryopreserved blood, the theoretically optimal operating condition, which is based on the assumption that the distribution of red blood cells is uniform, is often used to reduce or even avoid the hypotonic damage to cells. However, due to the polydispersity of cells, the optimal condition is actually not reliable. In this study, based on the discrete concept developed in our previous work, the effect of the polydispersity on the recovery rate of cells in the dilution-filtration system was statistically investigated by assigning three random parameters, isotonic cell volume, cell surface area, and osmotically inactive cell volume, to cells in small units of blood. The results show that, due to the polydispersity, the real recovery rate deviates from the ideal value that is based on uniform distribution. The deviation significantly increases with the standard errors of cell parameters, and it can be also magnified by high cryoprotectant concentrations. Under the effect of polydispersity, the uniform distribution-based optimized blood or diluent flow rate is not perfect. In practice, one should adopt a more conservative blood or diluent flow rate so that the hypotonic damage to cells can be further reduced. Heyuan Qiao, Weiping Ding, Yuncong Ma, Sijie Sun, and Dayong Gao Copyright © 2014 Heyuan Qiao et al. All rights reserved. Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition Tue, 09 Dec 2014 09:31:01 +0000 Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition. S. Mala and K. Latha Copyright © 2014 S. Mala and K. Latha. All rights reserved. Ant Colony Optimization Approaches to Clustering of Lung Nodules from CT Images Wed, 26 Nov 2014 08:09:02 +0000 Lung cancer is becoming a threat to mankind. Applying machine learning algorithms for detection and segmentation of irregular shaped lung nodules remains a remarkable milestone in CT scan image analysis research. In this paper, we apply ACO algorithm for lung nodule detection. We have compared the performance against three other algorithms, namely, Otsu algorithm, watershed algorithm, and global region based segmentation. In addition, we suggest a novel approach which involves variations of ACO, namely, refined ACO, logical ACO, and variant ACO. Variant ACO shows better reduction in false positives. In addition we propose black circular neighborhood approach to detect nodule centers from the edge detected image. Genetic algorithm based clustering is performed to cluster the nodules based on intensity, shape, and size. The performance of the overall approach is compared with hierarchical clustering to establish the improvisation in the proposed approach. Ravichandran C. Gopalakrishnan and Veerakumar Kuppusamy Copyright © 2014 Ravichandran C. Gopalakrishnan and Veerakumar Kuppusamy. All rights reserved. Altered Intrinsic Connectivity Networks in Frontal Lobe Epilepsy: A Resting-State fMRI Study Wed, 26 Nov 2014 00:10:09 +0000 Examining the resting-state networks (RSNs) may help us to understand the neural mechanism of the frontal lobe epilepsy (FLE). Resting-state functional MRI (fMRI) data were acquired from 46 patients with FLE (study group) and 46 age- and gender-matched healthy subjects (control group). The independent component analysis (ICA) method was used to identify RSNs from each group. Compared with the healthy subjects, decreased functional connectivity was observed in all the networks; however, in some areas of RSNs, functional connectivity was increased in patients with FLE. The duration of epilepsy and the seizure frequency were used to analyze correlation with the regions of interest (ROIs) in the nine RSNs to determine their influence on FLE. The functional network connectivity (FNC) was used to study the impact on the disturbance and reorganization of FLE. The results of this study may offer new insight into the neuropathophysiological mechanisms of FLE. Xinzhi Cao, Zhiyu Qian, Qiang Xu, Junshu Shen, Zhiqiang Zhang, and Guangming Lu Copyright © 2014 Xinzhi Cao et al. All rights reserved. Potential Lung Nodules Identification for Characterization by Variable Multistep Threshold and Shape Indices from CT Images Tue, 25 Nov 2014 00:00:00 +0000 Computed tomography (CT) is an important imaging modality. Physicians, surgeons, and oncologists prefer CT scan for diagnosis of lung cancer. However, some nodules are missed in CT scan. Computer aided diagnosis methods are useful for radiologists for detection of these nodules and early diagnosis of lung cancer. Early detection of malignant nodule is helpful for treatment. Computer aided diagnosis of lung cancer involves lung segmentation, potential nodules identification, features extraction from the potential nodules, and classification of the nodules. In this paper, we are presenting an automatic method for detection and segmentation of lung nodules from CT scan for subsequent features extraction and classification. Contribution of the work is the detection and segmentation of small sized nodules, low and high contrast nodules, nodules attached with vasculature, nodules attached to pleura membrane, and nodules in close vicinity of the diaphragm and lung wall in one-go. The particular techniques of the method are multistep threshold for the nodule detection and shape index threshold for false positive reduction. We used 60 CT scans of “Lung Image Database Consortium-Image Database Resource Initiative” taken by GE medical systems LightSpeed16 scanner as dataset and correctly detected 92% nodules. The results are reproducible. Saleem Iqbal, Khalid Iqbal, Fahim Arif, Arslan Shaukat, and Aasia Khanum Copyright © 2014 Saleem Iqbal et al. All rights reserved. Novel Harmonic Regularization Approach for Variable Selection in Cox’s Proportional Hazards Model Mon, 24 Nov 2014 00:00:00 +0000 Variable selection is an important issue in regression and a number of variable selection methods have been proposed involving nonconvex penalty functions. In this paper, we investigate a novel harmonic regularization method, which can approximate nonconvex regularizations, to select key risk factors in the Cox’s proportional hazards model using microarray gene expression data. The harmonic regularization method can be efficiently solved using our proposed direct path seeking approach, which can produce solutions that closely approximate those for the convex loss function and the nonconvex regularization. Simulation results based on the artificial datasets and four real microarray gene expression datasets, such as real diffuse large B-cell lymphoma (DCBCL), the lung cancer, and the AML datasets, show that the harmonic regularization method can be more accurate for variable selection than existing Lasso series methods. Ge-Jin Chu, Yong Liang, and Jia-Xuan Wang Copyright © 2014 Ge-Jin Chu et al. All rights reserved. An Efficient Diagnosis System for Parkinson’s Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach Tue, 18 Nov 2014 00:00:00 +0000 A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance. Chao Ma, Jihong Ouyang, Hui-Ling Chen, and Xue-Hua Zhao Copyright © 2014 Chao Ma et al. All rights reserved. A Mathematical Model of Cancer Treatment by Radiotherapy Thu, 13 Nov 2014 07:15:21 +0000 A periodic mathematical model of cancer treatment by radiotherapy is presented and studied in this paper. Conditions on the coexistence of the healthy and cancer cells are obtained. Furthermore, sufficient conditions on the existence and globally asymptotic stability of the positive periodic solution, the cancer eradication periodic solution, and the cancer win periodic solution are established. Some numerical examples are shown to verify the validity of the results. A discussion is presented for further study. Zijian Liu and Chenxue Yang Copyright © 2014 Zijian Liu and Chenxue Yang. All rights reserved. Objectifying Facial Expressivity Assessment of Parkinson’s Patients: Preliminary Study Thu, 13 Nov 2014 00:00:00 +0000 Patients with Parkinson’s disease (PD) can exhibit a reduction of spontaneous facial expression, designated as “facial masking,” a symptom in which facial muscles become rigid. To improve clinical assessment of facial expressivity of PD, this work attempts to quantify the dynamic facial expressivity (facial activity) of PD by automatically recognizing facial action units (AUs) and estimating their intensity. Spontaneous facial expressivity was assessed by comparing 7 PD patients with 8 control participants. To voluntarily produce spontaneous facial expressions that resemble those typically triggered by emotions, six emotions (amusement, sadness, anger, disgust, surprise, and fear) were elicited using movie clips. During the movie clips, physiological signals (facial electromyography (EMG) and electrocardiogram (ECG)) and frontal face video of the participants were recorded. The participants were asked to report on their emotional states throughout the experiment. We first examined the effectiveness of the emotion manipulation by evaluating the participant’s self-reports. Disgust-induced emotions were significantly higher than the other emotions. Thus we focused on the analysis of the recorded data during watching disgust movie clips. The proposed facial expressivity assessment approach captured differences in facial expressivity between PD patients and controls. Also differences between PD patients with different progression of Parkinson’s disease have been observed. Peng Wu, Isabel Gonzalez, Georgios Patsis, Dongmei Jiang, Hichem Sahli, Eric Kerckhofs, and Marie Vandekerckhove Copyright © 2014 Peng Wu et al. All rights reserved. Tolerance and Nature of Residual Refraction in Symmetric Power Space as Principal Lens Powers and Meridians Change Wed, 12 Nov 2014 00:00:00 +0000 Unacceptable principal powers in well-centred lenses may require a toric over-refraction which differs in nature from the one where correct powers have misplaced meridians. This paper calculates residual (over) refractions and their natures. The magnitude of the power of the over-refraction serves as a general, reliable, real scalar criterion for acceptance or tolerance of lenses whose surface relative curvatures change or whose meridians are rotated and cause powers to differ. Principal powers and meridians of lenses are analogous to eigenvalues and eigenvectors of symmetric matrices, which facilitates the calculation of powers and their residuals. Geometric paths in symmetric power space link intended refractive correction and these carefully chosen, undue refractive corrections. Principal meridians alone vary along an arc of a circle centred at the origin and corresponding powers vary autonomously along select diameters of that circle in symmetric power space. Depending on the path of the power change, residual lenses different from their prescription in principal powers and meridians are pure cross-cylindrical or spherocylindrical in nature. The location of residual power in symmetric dioptric power space and its optical cross-representation characterize the lens that must be added to the compensation to attain the power in the prescription. Herven Abelman and Shirley Abelman Copyright © 2014 Herven Abelman and Shirley Abelman. All rights reserved. Pin-Align: A New Dynamic Programming Approach to Align Protein-Protein Interaction Networks Mon, 10 Nov 2014 00:00:00 +0000 To date, few tools for aligning protein-protein interaction networks have been suggested. These tools typically find conserved interaction patterns using various local or global alignment algorithms. However, the improvement of the speed, scalability, simplification, and accuracy of network alignment tools is still the target of new researches. In this paper, we introduce Pin-Align, a new tool for local alignment of protein-protein interaction networks. Pin-Align accuracy is tested on protein interaction networks from IntAct, DIP, and the Stanford Network Database and the results are compared with other well-known algorithms. It is shown that Pin-Align has higher sensitivity and specificity in terms of KEGG Ortholog groups. Farid Amir-Ghiasvand, Abbas Nowzari-Dalini, and Vida Momenzadeh Copyright © 2014 Farid Amir-Ghiasvand et al. All rights reserved. Advances in Statistical Medicine Sun, 09 Nov 2014 13:12:36 +0000 Sujay Datta, Xiao-Qin Xia, Samsiddhi Bhattacharjee, and Zhenyu Jia Copyright © 2014 Sujay Datta et al. All rights reserved. ADLD: A Novel Graphical Representation of Protein Sequences and Its Application Thu, 30 Oct 2014 09:47:09 +0000 To facilitate the intuitional analysis of protein sequences, a novel graphical representation of protein sequences called ADLD (Alignment Diagonal Line Diagram) is introduced in this paper first, and then a new ADLD based method is proposed and utilized to analyze the similarity/dissimilarity of protein sequences. Comparing with existing methods, our ADLD based method is proved to be effective in the similarity/dissimilarity analysis of protein sequences and have the merits of good intuition, visuality, and simplicity. The examinations of the similarities/dissimilarities for both the 16 different ND5 proteins and the 29 different spike proteins illustrate the utility of our ADLD based approach. Lei Wang, Hui Peng, and Jinhua Zheng Copyright © 2014 Lei Wang et al. All rights reserved.