Computational and Mathematical Methods in Medicine The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . 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. 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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. Dynamics of Mycobacterium and bovine tuberculosis in a Human-Buffalo Population Tue, 02 Sep 2014 00:00:00 +0000 A new model for the transmission dynamics of Mycobacterium tuberculosis and bovine tuberculosis in a community, consisting of humans and African buffalos, is presented. The buffalo-only component of the model exhibits the phenomenon of backward bifurcation, which arises due to the reinfection of exposed and recovered buffalos, when the associated reproduction number is less than unity. This model has a unique endemic equilibrium, which is globally asymptotically stable for a special case, when the reproduction number exceeds unity. Uncertainty and sensitivity analyses, using data relevant to the dynamics of the two diseases in the Kruger National Park, show that the distribution of the associated reproduction number is less than unity (hence, the diseases would not persist in the community). Crucial parameters that influence the dynamics of the two diseases are also identified. Both the buffalo-only and the buffalo-human model exhibit the same qualitative dynamics with respect to the local and global asymptotic stability of their respective disease-free equilibrium, as well as with respect to the backward bifurcation phenomenon. Numerical simulations of the buffalo-human model show that the cumulative number of Mycobacterium tuberculosis cases in humans (buffalos) decreases with increasing number of bovine tuberculosis infections in humans (buffalo). A. S. Hassan, S. M. Garba, A. B. Gumel, and J. M.-S. Lubuma Copyright © 2014 A. S. Hassan et al. All rights reserved. A Novel Adaptive Level Set Segmentation Method Mon, 01 Sep 2014 10:47:58 +0000 The adaptive distance preserving level set (ADPLS) method is fast and not dependent on the initial contour for the segmentation of images with intensity inhomogeneity, but it often leads to segmentation with compromised accuracy. And the local binary fitting model (LBF) method can achieve segmentation with higher accuracy but with low speed and sensitivity to initial contour placements. In this paper, a novel and adaptive fusing level set method has been presented to combine the desirable properties of these two methods, respectively. In the proposed method, the weights of the ADPLS and LBF are automatically adjusted according to the spatial information of the image. Experimental results show that the comprehensive performance indicators, such as accuracy, speed, and stability, can be significantly improved by using this improved method. Yazhong Lin, Qian Zheng, Jiaqiang Chen, Qian Cai, and Qianjin Feng Copyright © 2014 Yazhong Lin et al. All rights reserved. A Method of Protein Model Classification and Retrieval Using Bag-of-Visual-Features Mon, 01 Sep 2014 07:44:06 +0000 In this paper we propose a novel visual method for protein model classification and retrieval. Different from the conventional methods, the key idea of the proposed method is to extract image features of proteins and measure the visual similarity between proteins. Firstly, the multiview images are captured by vertices and planes of a given octahedron surrounding the protein. Secondly, the local features are extracted from each image of the different views by the SURF algorithm and are vector quantized into visual words using a visual codebook. Finally, KLD is employed to calculate the similarity distance between two feature vectors. Experimental results show that the proposed method has encouraging performances for protein retrieval and categorization as shown in the comparison with other methods. Jinlin Ma, Ziping Ma, Baosheng Kang, and Ke Lu Copyright © 2014 Jinlin Ma et al. All rights reserved. Performance Optimization of Force Feedback Control System in Virtual Vascular Intervention Surgery Mon, 01 Sep 2014 05:55:33 +0000 In virtual surgery of minimally invasive vascular intervention, the force feedback is transmitted through the flexible guide wire. The disturbance caused by the flexible deformation would affect the fidelity of the VR (virtual reality) training. SMC (sliding mode control) strategy with delayed-output observer is adopted to suppress the effect of flexible deformation. In this study, the control performance of the strategy is assessed when the length of guide wire between actuator and the operating point changes. The performance assessment results demonstrate the effectiveness of the proposed method and find the optimal length of guide wire for the force feedback control. Zhi Hu, Ping Cai, Peng Qin, and Le Xie Copyright © 2014 Zhi Hu et al. All rights reserved. Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior Mon, 01 Sep 2014 00:00:00 +0000 Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. Because the classical MDP segmentation cannot be applied for real-time diagnosis, a new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain-tumor images, we developed the algorithm to segment multimodal brain-tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use. Yisu Lu, Jun Jiang, Wei Yang, Qianjin Feng, and Wufan Chen Copyright © 2014 Yisu Lu et al. All rights reserved. The Influence of Tone Inventory on ERP without Focal Attention: A Cross-Language Study Thu, 28 Aug 2014 09:44:23 +0000 This study investigates the effect of tone inventories on brain activities underlying pitch without focal attention. We find that the electrophysiological responses to across-category stimuli are larger than those to within-category stimuli when the pitch contours are superimposed on nonspeech stimuli; however, there is no electrophysiological response difference associated with category status in speech stimuli. Moreover, this category effect in nonspeech stimuli is stronger for Cantonese speakers. Results of previous and present studies lead us to conclude that brain activities to the same native lexical tone contrasts are modulated by speakers’ language experiences not only in active phonological processing but also in automatic feature detection without focal attention. In contrast to the condition with focal attention, where phonological processing is stronger for speech stimuli, the feature detection (pitch contours in this study) without focal attention as shaped by language background is superior in relatively regular stimuli, that is, the nonspeech stimuli. The results suggest that Cantonese listeners outperform Mandarin listeners in automatic detection of pitch features because of the denser Cantonese tone system. Hong-Ying Zheng, Gang Peng, Jian-Yong Chen, Caicai Zhang, James W. Minett, and William S-Y. Wang Copyright © 2014 Hong-Ying Zheng et al. All rights reserved. Automated Image Analysis of Lung Branching Morphogenesis from Microscopic Images of Fetal Rat Explants Thu, 28 Aug 2014 00:00:00 +0000 Background. Regulating mechanisms of branching morphogenesis of fetal lung rat explants have been an essential tool for molecular research. This work presents a new methodology to accurately quantify the epithelial, outer contour, and peripheral airway buds of lung explants during cellular development from microscopic images. Methods. The outer contour was defined using an adaptive and multiscale threshold algorithm whose level was automatically calculated based on an entropy maximization criterion. The inner lung epithelium was defined by a clustering procedure that groups small image regions according to the minimum description length principle and local statistical properties. Finally, the number of peripheral buds was counted as the skeleton branched ends from a skeletonized image of the lung inner epithelia. Results. The time for lung branching morphometric analysis was reduced in 98% in contrast to the manual method. Best results were obtained in the first two days of cellular development, with lesser standard deviations. Nonsignificant differences were found between the automatic and manual results in all culture days. Conclusions. The proposed method introduces a series of advantages related to its intuitive use and accuracy, making the technique suitable to images with different lighting characteristics and allowing a reliable comparison between different researchers. Pedro L. Rodrigues, Nuno F. Rodrigues, Duarte Duque, Sara Granja, Jorge Correia-Pinto, and João L. Vilaça Copyright © 2014 Pedro L. Rodrigues et al. All rights reserved. An Efficient Frequency Recognition Method Based on Likelihood Ratio Test for SSVEP-Based BCI Thu, 28 Aug 2014 00:00:00 +0000 An efficient frequency recognition method is very important for SSVEP-based BCI systems to improve the information transfer rate (ITR). To address this aspect, for the first time, likelihood ratio test (LRT) was utilized to propose a novel multichannel frequency recognition method for SSVEP data. The essence of this new method is to calculate the association between multichannel EEG signals and the reference signals which were constructed according to the stimulus frequency with LRT. For the simulation and real SSVEP data, the proposed method yielded higher recognition accuracy with shorter time window length and was more robust against noise in comparison with the popular canonical correlation analysis- (CCA-) based method and the least absolute shrinkage and selection operator- (LASSO-) based method. The recognition accuracy and information transfer rate (ITR) obtained by the proposed method was higher than those of the CCA-based method and LASSO-based method. The superior results indicate that the LRT method is a promising candidate for reliable frequency recognition in future SSVEP-BCI. Yangsong Zhang, Li Dong, Rui Zhang, Dezhong Yao, Yu Zhang, and Peng Xu Copyright © 2014 Yangsong Zhang et al. All rights reserved. Crossing Fibers Detection with an Analytical High Order Tensor Decomposition Wed, 27 Aug 2014 08:20:42 +0000 Diffusion magnetic resonance imaging (dMRI) is the only technique to probe in vivo and noninvasively the fiber structure of human brain white matter. Detecting the crossing of neuronal fibers remains an exciting challenge with an important impact in tractography. In this work, we tackle this challenging problem and propose an original and efficient technique to extract all crossing fibers from diffusion signals. To this end, we start by estimating, from the dMRI signal, the so-called Cartesian tensor fiber orientation distribution (CT-FOD) function, whose maxima correspond exactly to the orientations of the fibers. The fourth order symmetric positive definite tensor that represents the CT-FOD is then analytically decomposed via the application of a new theoretical approach and this decomposition is used to accurately extract all the fibers orientations. Our proposed high order tensor decomposition based approach is minimal and allows recovering the whole crossing fibers without any a priori information on the total number of fibers. Various experiments performed on noisy synthetic data, on phantom diffusion, data and on human brain data validate our approach and clearly demonstrate that it is efficient, robust to noise and performs favorably in terms of angular resolution and accuracy when compared to some classical and state-of-the-art approaches. T. Megherbi, M. Kachouane, F. Oulebsir-Boumghar, and R. Deriche Copyright © 2014 T. Megherbi et al. All rights reserved. Patient Specific Seizure Prediction System Using Hilbert Spectrum and Bayesian Networks Classifiers Wed, 27 Aug 2014 08:14:56 +0000 The aim of this paper is to develop an automated system for epileptic seizure prediction from intracranial EEG signals based on Hilbert-Huang transform (HHT) and Bayesian classifiers. Proposed system includes decomposition of the signals into intrinsic mode functions for obtaining features and use of Bayesian networks with correlation based feature selection for binary classification of preictal and interictal recordings. The system was trained and tested on Freiburg EEG database. 58 hours of preictal data, 40-minute data blocks prior to each of 87 seizures collected from 21 patients, and 503.1 hours of interictal data were examined resulting in 96.55% sensitivity with 0.21 false alarms per hour, 13.896% average proportion of time spent in warning, and 33.21 minutes of average detection latency using 30-second EEG segments with 50% overlap and a simple postprocessing technique resulting in a decision (a seizure is expected/not expected) every 5 minutes. High sensitivity and low false positive rate with reasonable detection latency show that HHT based features are acceptable for patient specific seizure prediction from intracranial EEG data. Time spent for testing an EEG segment was 4.1451 seconds on average, which makes the system viable for use in real-time seizure control systems. Nilufer Ozdemir and Esen Yildirim Copyright © 2014 Nilufer Ozdemir and Esen Yildirim. All rights reserved.