Computational and Mathematical Methods in Medicine http://www.hindawi.com The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. Ant Colony Optimization Approaches to Clustering of Lung Nodules from CT Images Wed, 26 Nov 2014 08:09:02 +0000 http://www.hindawi.com/journals/cmmm/2014/572494/ 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 http://www.hindawi.com/journals/cmmm/2014/864979/ 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 http://www.hindawi.com/journals/cmmm/2014/241647/ 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 http://www.hindawi.com/journals/cmmm/2014/857398/ 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 http://www.hindawi.com/journals/cmmm/2014/985789/ 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 http://www.hindawi.com/journals/cmmm/2014/172923/ 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 http://www.hindawi.com/journals/cmmm/2014/427826/ 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 http://www.hindawi.com/journals/cmmm/2014/492383/ 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 http://www.hindawi.com/journals/cmmm/2014/393908/ 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 http://www.hindawi.com/journals/cmmm/2014/316153/ 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 http://www.hindawi.com/journals/cmmm/2014/959753/ 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. Multiscale Coupling of Transcranial Direct Current Stimulation to Neuron Electrodynamics: Modeling the Influence of the Transcranial Electric Field on Neuronal Depolarization Thu, 23 Oct 2014 09:26:26 +0000 http://www.hindawi.com/journals/cmmm/2014/360179/ Transcranial direct current stimulation (tDCS) continues to demonstrate success as a medical intervention for neurodegenerative diseases, psychological conditions, and traumatic brain injury recovery. One aspect of tDCS still not fully comprehended is the influence of the tDCS electric field on neural functionality. To address this issue, we present a mathematical, multiscale model that couples tDCS administration to neuron electrodynamics. We demonstrate the model’s validity and medical applicability with computational simulations using an idealized two-dimensional domain and then an MRI-derived, three-dimensional human head geometry possessing inhomogeneous and anisotropic tissue conductivities. We exemplify the capabilities of these simulations with real-world tDCS electrode configurations and treatment parameters and compare the model’s predictions to those attained from medical research studies. The model is implemented using efficient numerical strategies and solution techniques to allow the use of fine computational grids needed by the medical community. Edward T. Dougherty, James C. Turner, and Frank Vogel Copyright © 2014 Edward T. Dougherty et al. All rights reserved. A Statistical-Textural-Features Based Approach for Classification of Solid Drugs Using Surface Microscopic Images Mon, 13 Oct 2014 07:57:08 +0000 http://www.hindawi.com/journals/cmmm/2014/791246/ The quality of pharmaceutical products plays an important role in pharmaceutical industry as well as in our lives. Usage of defective tablets can be harmful for patients. In this research we proposed a nondestructive method to identify defective and nondefective tablets using their surface morphology. Three different environmental factors temperature, humidity and moisture are analyzed to evaluate the performance of the proposed method. Multiple textural features are extracted from the surface of the defective and nondefective tablets. These textural features are gray level cooccurrence matrix, run length matrix, histogram, autoregressive model and HAAR wavelet. Total textural features extracted from images are 281. We performed an analysis on all those 281, top 15, and top 2 features. Top 15 features are extracted using three different feature reduction techniques: chi-square, gain ratio and relief-F. In this research we have used three different classifiers: support vector machine, -nearest neighbors and naïve Bayes to calculate the accuracies against proposed method using two experiments, that is, leave-one-out cross-validation technique and train test models. We tested each classifier against all selected features and then performed the comparison of their results. The experimental work resulted in that in most of the cases SVM performed better than the other two classifiers. Fahima Tahir and Muhammad Abuzar Fahiem Copyright © 2014 Fahima Tahir and Muhammad Abuzar Fahiem. All rights reserved. Steady-State Analysis of Necrotic Core Formation for Solid Avascular Tumors with Time Delays in Regulatory Apoptosis Mon, 13 Oct 2014 07:32:38 +0000 http://www.hindawi.com/journals/cmmm/2014/467158/ A mathematical model for the growth of solid avascular tumor with time delays in regulatory apoptosis is studied. The existence of stationary solutions and the mechanism of formation of necrotic cores in the growth of the tumors are studied. The results show that if the natural death rate of the tumor cell exceeds a fixed positive constant, then the dormant tumor is nonnecrotic; otherwise, the dormant tumor is necrotic. Fangwei Zhang and Shihe Xu Copyright © 2014 Fangwei Zhang and Shihe Xu. All rights reserved. Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity Mon, 13 Oct 2014 07:05:27 +0000 http://www.hindawi.com/journals/cmmm/2014/958671/ Compressed sensing (CS) based methods make it possible to reconstruct magnetic resonance (MR) images from undersampled measurements, which is known as CS-MRI. The reference-driven CS-MRI reconstruction schemes can further decrease the sampling ratio by exploiting the sparsity of the difference image between the target and the reference MR images in pixel domain. Unfortunately existing methods do not work well given that contrast changes are incorrectly estimated or motion compensation is inaccurate. In this paper, we propose to reconstruct MR images by utilizing the sparsity of the difference image between the target and the motion-compensated reference images in wavelet transform and gradient domains. The idea is attractive because it requires neither the estimation of the contrast changes nor multiple times motion compensations. In addition, we apply total generalized variation (TGV) regularization to eliminate the staircasing artifacts caused by conventional total variation (TV). Fast composite splitting algorithm (FCSA) is used to solve the proposed reconstruction problem in order to improve computational efficiency. Experimental results demonstrate that the proposed method can not only reduce the computational cost but also decrease sampling ratio or improve the reconstruction quality alternatively. Di Zhao, Huiqian Du, Yu Han, and Wenbo Mei Copyright © 2014 Di Zhao et al. All rights reserved. Systematic Analysis of Time-Series Gene Expression Data on Tumor Cell-Selective Apoptotic Responses to HDAC Inhibitors Mon, 13 Oct 2014 06:44:30 +0000 http://www.hindawi.com/journals/cmmm/2014/867289/ SAHA (suberoylanilide hydroxamic acid or vorinostat) is the first nonselective histone deacetylase (HDAC) inhibitor approved by the US Food and Drug Administration (FDA). SAHA affects histone acetylation in chromatin and a variety of nonhistone substrates, thus influencing many cellular processes. In particularly, SAHA induces selective apoptosis of tumor cells, although the mechanism is not well understood. A series of microarray experiments was recently conducted to investigate tumor cell-selective proapoptotic transcriptional responses induced by SAHA. Based on that gene expression time series, we propose a novel framework for detailed analysis of the mechanism of tumor cell apoptosis selectively induced by SAHA. Our analyses indicated that SAHA selectively disrupted the DNA damage response, cell cycle, p53 expression, and mitochondrial integrity of tumor samples to induce selective tumor cell apoptosis. Our results suggest a possible regulation network. Our research extends the existing research. Yun-feng Qi, Yan-xin Huang, Yan Dong, Li-hua Zheng, Yong-li Bao, Lu-guo Sun, Yin Wu, Chun-lei Yu, Hong-yu Jiang, and Yu-xin Li Copyright © 2014 Yun-feng Qi et al. All rights reserved. An Effective Way of J Wave Separation Based on Multilayer NMF Sun, 12 Oct 2014 12:20:41 +0000 http://www.hindawi.com/journals/cmmm/2014/217067/ J wave is getting more and more important in the clinical diagnosis as a new index of the electrocardiogram (ECG) of ventricular bipolar, but its signal often mixed in normal ST segment, using the traditional electrocardiograph, and diagnosed by experience cannot meet the practical requirements. Therefore, a new method of multilayer nonnegative matrix factorization (NMF) in this paper is put forward, taking the hump shape J wave, for example, which can extract the original J wave signal from the ST segment and analyze the accuracy of extraction, showing the characteristics of hump shape J wave from the aspects of frequency domain, power spectrum, and spectral type, providing the basis for clinical diagnosis and increasing the reliability of the diagnosis of J wave. Deng-ao Li, Jing-ang Lv, Ju-min Zhao, and Jin Zhang Copyright © 2014 Deng-ao Li et al. All rights reserved. 3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading Thu, 09 Oct 2014 13:54:31 +0000 http://www.hindawi.com/journals/cmmm/2014/536217/ One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system. Tae-Yun Kim, Nam-Hoon Cho, Goo-Bo Jeong, Ewert Bengtsson, and Heung-Kook Choi Copyright © 2014 Tae-Yun Kim et al. All rights reserved. Parallelized Seeded Region Growing Using CUDA Mon, 22 Sep 2014 00:00:00 +0000 http://www.hindawi.com/journals/cmmm/2014/856453/ This paper presents a novel method for parallelizing the seeded region growing (SRG) algorithm using Compute Unified Device Architecture (CUDA) technology, with intention to overcome the theoretical weakness of SRG algorithm of its computation time being directly proportional to the size of a segmented region. The segmentation performance of the proposed CUDA-based SRG is compared with SRG implementations on single-core CPUs, quad-core CPUs, and shader language programming, using synthetic datasets and 20 body CT scans. Based on the experimental results, the CUDA-based SRG outperforms the other three implementations, advocating that it can substantially assist the segmentation during massive CT screening tests. Seongjin Park, Jeongjin Lee, Hyunna Lee, Juneseuk Shin, Jinwook Seo, Kyoung Ho Lee, Yeong-Gil Shin, and Bohyoung Kim Copyright © 2014 Seongjin Park et al. All rights reserved. Prediction of BP Reactivity to Talking Using Hybrid Soft Computing Approaches Sun, 21 Sep 2014 00:00:00 +0000 http://www.hindawi.com/journals/cmmm/2014/762501/ High blood pressure (BP) is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI), and arm circumference (AC) were used as independent predictor variables for the prediction of BP reactivity to talking. Principal component analysis (PCA) was fused with artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), and least square-support vector machine (LS-SVM) model to remove the multicollinearity effect among anthropometric predictor variables. The statistical tests in terms of coefficient of determination (), root mean square error (RMSE), and mean absolute percentage error (MAPE) revealed that PCA based LS-SVM (PCA-LS-SVM) model produced a more efficient prediction of BP reactivity as compared to other models. This assessment presents the importance and advantages posed by PCA fused prediction models for prediction of biological variables. Gurmanik Kaur, Ajat Shatru Arora, and Vijender Kumar Jain Copyright © 2014 Gurmanik Kaur et al. All rights reserved. Detection and Measurement of the Intracellular Calcium Variation in Follicular Cells Tue, 16 Sep 2014 11:16:50 +0000 http://www.hindawi.com/journals/cmmm/2014/484656/ This work presents a new method for measuring the variation of intracellular calcium in follicular cells. The proposal consists in two stages: (i) the detection of the cell’s nuclei and (ii) the analysis of the fluorescence variations. The first stage is performed via watershed modified transformation, where the process of labeling is controlled. The detection process uses the contours of the cells as descriptors, where they are enhanced with a morphological filter that homogenizes the luminance variation of the image. In the second stage, the fluorescence variations are modeled as an exponential decreasing function, where the fluorescence variations are highly correlated with the changes of intracellular free Ca2+. Additionally, it is introduced a new morphological called medium reconstruction process, which helps to enhance the data for the modeling process. This filter exploits the undermodeling and overmodeling properties of reconstruction operators, such that it preserves the structure of the original signal. Finally, an experimental process shows evidence of the capabilities of the proposal. Ana M. Herrera-Navarro, Iván R. Terol-Villalobos, Hugo Jiménez-Hernández, Hayde Peregrina-Barreto, and José-Joel Gonzalez-Barboza Copyright © 2014 Ana M. Herrera-Navarro et al. All rights reserved. Three-Dimensional Lower Extremity Joint Loading in a Carved Ski and Snowboard Turn: A Pilot Study Mon, 15 Sep 2014 12:04:25 +0000 http://www.hindawi.com/journals/cmmm/2014/340272/ A large number of injuries to the lower extremity occur in skiing and snowboarding. Due to the difficulty of collecting 3D kinematic and kinetic data with high accuracy, a possible relationship between injury statistic and joint loading has not been studied. Therefore, the purpose of the current study was to compare ankle and knee joint loading at the steering leg between carved ski and snowboard turns. Kinetic data were collected using mobile force plates mounted under the toe and heel part of the binding on skies or snowboard (KISTLER). Kinematic data were collected with five synchronized, panning, tilting, and zooming cameras. An extended version of the Yeadon model was applied to calculate inertial properties of the segments. Ankle and knee joint forces and moments were calculated using inverse dynamic analysis. Results showed higher forces along the longitudinal axis in skiing and similar forces for skiing and snowboarding in anterior-posterior and mediolateral direction. Joint moments were consistently greater during a snowboard turn, but more fluctuations were observed in skiing. Hence, when comparing joint loading between carved ski and snowboard turns, one should differentiate between forces and moments, including the direction of forces and moments and the turn phase. Miriam Klous, Erich Müller, and Hermann Schwameder Copyright © 2014 Miriam Klous et al. All rights reserved. Feature Selection for Better Identification of Subtypes of Guillain-Barré Syndrome Mon, 15 Sep 2014 00:00:00 +0000 http://www.hindawi.com/journals/cmmm/2014/432109/ Guillain-Barré syndrome (GBS) is a neurological disorder which has not been explored using clustering algorithms. Clustering algorithms perform more efficiently when they work only with relevant features. In this work, we applied correlation-based feature selection (CFS), chi-squared, information gain, symmetrical uncertainty, and consistency filter methods to select the most relevant features from a 156-feature real dataset. This dataset contains clinical, serological, and nerve conduction tests data obtained from GBS patients. The most relevant feature subsets, determined with each filter method, were used to identify four subtypes of GBS present in the dataset. We used partitions around medoids (PAM) clustering algorithm to form four clusters, corresponding to the GBS subtypes. We applied the purity of each cluster as evaluation measure. After experimentation, symmetrical uncertainty and information gain determined a feature subset of seven variables. These variables conformed as a dataset were used as input to PAM and reached a purity of 0.7984. This result leads to a first characterization of this syndrome using computational techniques. José Hernández-Torruco, Juana Canul-Reich, Juan Frausto-Solís, and Juan José Méndez-Castillo Copyright © 2014 José Hernández-Torruco et al. All rights reserved. Resistance Training Exercise Program for Intervention to Enhance Gait Function in Elderly Chronically Ill Patients: Multivariate Multiscale Entropy for Center of Pressure Signal Analysis Wed, 10 Sep 2014 08:15:29 +0000 http://www.hindawi.com/journals/cmmm/2014/471356/ Falls are unpredictable accidents, and the resulting injuries can be serious in the elderly, particularly those with chronic diseases. Regular exercise is recommended to prevent and treat hypertension and other chronic diseases by reducing clinical blood pressure. The “complexity index” (CI), based on multiscale entropy (MSE) algorithm, has been applied in recent studies to show a person’s adaptability to intrinsic and external perturbations and widely used measure of postural sway or stability. The multivariate multiscale entropy (MMSE) was advanced algorithm used to calculate the complexity index (CI) values of the center of pressure (COP) data. In this study, we applied the MSE & MMSE to analyze gait function of 24 elderly, chronically ill patients (44% female; 56% male; mean age, years) with either cardiovascular disease, diabetes mellitus, or osteoporosis. After a 12-week training program, postural stability measurements showed significant improvements. Our results showed beneficial effects of resistance training, which can be used to improve postural stability in the elderly and indicated that MMSE algorithms to calculate CI of the COP data were superior to the multiscale entropy (MSE) algorithm to identify the sense of balance in the elderly. Ming-Shu Chen and Bernard C. Jiang Copyright © 2014 Ming-Shu Chen and Bernard C. Jiang. All rights reserved. A Wavelet Transform Based Method to Determine Depth of Anesthesia to Prevent Awareness during General Anesthesia Tue, 09 Sep 2014 11:05:30 +0000 http://www.hindawi.com/journals/cmmm/2014/354739/ Awareness during general anesthesia for its serious psychological effects on patients and some juristically problems for anesthetists has been an important challenge during past decades. Monitoring depth of anesthesia is a fundamental solution to this problem. The induction of anesthesia alters frequency and mean of amplitudes of the electroencephalogram (EEG), and its phase couplings. We analyzed EEG changes for phase coupling between delta and alpha subbands using a new algorithm for depth of general anesthesia measurement based on complex wavelet transform (CWT) in patients anesthetized by Propofol. Entropy and histogram of modulated signals were calculated by taking bispectral index (BIS) values as reference. Entropies corresponding to different BIS intervals using Mann-Whitney test showed that they had different continuous distributions. The results demonstrated that there is a phase coupling between 3 and 4 Hz in delta and 8-9 Hz in alpha subbands and these changes are shown better at the channel of EEG. Moreover, when BIS values increase, the entropy value of modulated signal also increases and vice versa. In addition, measuring phase coupling between delta and alpha subbands of EEG signals through continuous CWT analysis reveals the depth of anesthesia level. As a result, awareness during anesthesia can be prevented. Seyed Mortaza Mousavi, Ahmet Adamoğlu, Tamer Demiralp, and Mahrokh G. Shayesteh Copyright © 2014 Seyed Mortaza Mousavi et al. All rights reserved. Influence of Different Geometric Representations of the Volume Conductor on Nerve Activation during Electrical Stimulation Tue, 09 Sep 2014 09:33:22 +0000 http://www.hindawi.com/journals/cmmm/2014/489240/ Volume conductor models with different geometric representations, such as the parallel layer model (PM), the cylindrical layer model (CM), or the anatomically based model (AM), have been employed during the implementation of bioelectrical models for electrical stimulation (FES). Evaluating their strengths and limitations to predict nerve activation is fundamental to achieve a good trade-off between accuracy and computation time. However, there are no studies aimed at clarifying the following questions. (1) Does the nerve activation differ between CM and PM? (2) How well do CM and PM approximate an AM? (3) What is the effect of the presence of blood vessels and nerve trunk on nerve activation prediction? Therefore, in this study, we addressed these questions by comparing nerve activation between CM, PM, and AM models by FES. The activation threshold was used to evaluate the models under different configurations of superficial electrodes (size and distance), nerve depths, and stimulation sites. Additionally, the influences of the sciatic nerve, femoral artery, and femoral vein were inspected for a human thigh. The results showed that the CM and PM had a high error rate, but the variation of the activation threshold followed the same tendency for electrode size and interelectrode distance variation as AM. José Gómez-Tames, José González, and Wenwei Yu Copyright © 2014 José Gómez-Tames et al. All rights reserved. An Ensemble-of-Classifiers Based Approach for Early Diagnosis of Alzheimer’s Disease: Classification Using Structural Features of Brain Images Tue, 09 Sep 2014 00:00:00 +0000 http://www.hindawi.com/journals/cmmm/2014/862307/ Structural brain imaging is playing a vital role in identification of changes that occur in brain associated with Alzheimer’s disease. This paper proposes an automated image processing based approach for the identification of AD from MRI of the brain. The proposed approach is novel in a sense that it has higher specificity/accuracy values despite the use of smaller feature set as compared to existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. The dataset selected consists of 85 age and gender matched individuals from OASIS database. The features selected are volume of GM, WM, and CSF and size of hippocampus. Three different classification models (SVM, MLP, and J48) are used for identification of patients and controls. In addition, an ensemble of classifiers, based on majority voting, is adopted to overcome the error caused by an independent base classifier. Ten-fold cross validation strategy is applied for the evaluation of our scheme. Moreover, to evaluate the performance of proposed approach, individual features and combination of features are fed to individual classifiers and ensemble based classifier. Using size of left hippocampus as feature, the accuracy achieved with ensemble of classifiers is 93.75%, with 100% specificity and 87.5% sensitivity. Saima Farhan, Muhammad Abuzar Fahiem, and Huma Tauseef Copyright © 2014 Saima Farhan et al. All rights reserved. A Multiatlas Segmentation Using Graph Cuts with Applications to Liver Segmentation in CT Scans Mon, 08 Sep 2014 09:35:31 +0000 http://www.hindawi.com/journals/cmmm/2014/182909/ An atlas-based segmentation approach is presented that combines low-level operations, an affine probabilistic atlas, and a multiatlas-based segmentation. The proposed combination provides highly accurate segmentation due to registrations and atlas selections based on the regions of interest (ROIs) and coarse segmentations. Our approach shares the following common elements between the probabilistic atlas and multiatlas segmentation: (a) the spatial normalisation and (b) the segmentation method, which is based on minimising a discrete energy function using graph cuts. The method is evaluated for the segmentation of the liver in computed tomography (CT) images. Low-level operations define a ROI around the liver from an abdominal CT. We generate a probabilistic atlas using an affine registration based on geometry moments from manually labelled data. Next, a coarse segmentation of the liver is obtained from the probabilistic atlas with low computational effort. Then, a multiatlas segmentation approach improves the accuracy of the segmentation. Both the atlas selections and the nonrigid registrations of the multiatlas approach use a binary mask defined by coarse segmentation. We experimentally demonstrate that this approach performs better than atlas selections and nonrigid registrations in the entire ROI. The segmentation results are comparable to those obtained by human experts and to other recently published results. Carlos Platero and M. Carmen Tobar Copyright © 2014 Carlos Platero and M. Carmen Tobar. All rights reserved. Modeling the Relationship between Fluorodeoxyglucose Uptake and Tumor Radioresistance as a Function of the Tumor Microenvironment Mon, 08 Sep 2014 05:38:41 +0000 http://www.hindawi.com/journals/cmmm/2014/847162/ High fluorodeoxyglucose positron emission tomography (FDG-PET) uptake in tumors has often been correlated with increasing local failure and shorter overall survival, but the radiobiological mechanisms of this uptake are unclear. We explore the relationship between FDG-PET uptake and tumor radioresistance using a mechanistic model that considers cellular status as a function of microenvironmental conditions, including proliferating cells with access to oxygen and glucose, metabolically active cells with access to glucose but not oxygen, and severely hypoxic cells that are starving. However, it is unclear what the precise uptake levels of glucose should be for cells that receive oxygen and glucose versus cells that only receive glucose. Different potential FDG uptake profiles, as a function of the microenvironment, were simulated. Predicted tumor doses for 50% control (TD50) in 2 Gy fractions were estimated for each assumed uptake profile and for various possible cell mixtures. The results support the hypothesis of an increased avidity of FDG for cells in the intermediate stress state (those receiving glucose but not oxygen) compared to well-oxygenated (and proliferating) cells. Jeho Jeong and Joseph O. Deasy Copyright © 2014 Jeho Jeong and Joseph O. Deasy. All rights reserved. Validation in Principal Components Analysis Applied to EEG Data Mon, 08 Sep 2014 00:00:00 +0000 http://www.hindawi.com/journals/cmmm/2014/413801/ The well-known multivariate technique Principal Components Analysis (PCA) is usually applied to a sample, and so component scores are subjected to sampling variability. However, few studies address their stability, an important topic when the sample size is small. This work presents three validation procedures applied to PCA, based on confidence regions generated by a variant of a nonparametric bootstrap called the partial bootstrap: (i) the assessment of PC scores variability by the spread and overlapping of “confidence regions” plotted around these scores; (ii) the use of the confidence regions centroids as a validation set; and (iii) the definition of the number of nontrivial axes to be retained for analysis. The methods were applied to EEG data collected during a postural control protocol with twenty-four volunteers. Two axes were retained for analysis, with 91.6% of explained variance. Results showed that the area of the confidence regions provided useful insights on the variability of scores and suggested that some subjects were not distinguishable from others, which was not evident from the principal planes. In addition, potential outliers, initially suggested by an analysis of the first principal plane, could not be confirmed by the confidence regions. João Carlos G. D. Costa, Paulo José G. Da-Silva, Renan Moritz V. R. Almeida, and Antonio Fernando C. Infantosi Copyright © 2014 João Carlos G. D. Costa et al. All rights reserved.