Computational and Mathematical Methods in Medicine The latest articles from Hindawi Publishing Corporation © 2015 , Hindawi Publishing Corporation . All rights reserved. Application of Phase Congruency for Discriminating Some Lung Diseases Using Chest Radiograph Tue, 31 Mar 2015 15:42:04 +0000 A novel procedure using phase congruency is proposed for discriminating some lung disease using chest radiograph. Phase congruency provides information about transitions between adjacent pixels. Abrupt changes of phase congruency values between pixels may suggest a possible boundary or another feature that may be used for discrimination. This property of phase congruency may have potential for deciding between disease present and disease absent where the regions of infection on the images have no obvious shape, size, or configuration. Five texture measures calculated from phase congruency and Gabor were shown to be normally distributed. This gave good indicators of discrimination errors in the form of the probability of Type I Error (δ) and the probability of Type II Error (β). However, since 1 −  δ is the true positive fraction (TPF) and β is the false positive fraction (FPF), an ROC analysis was used to decide on the choice of texture measures. Given that features are normally distributed, for the discrimination between disease present and disease absent, energy, contrast, and homogeneity from phase congruency gave better results compared to those using Gabor. Similarly, for the more difficult problem of discriminating lobar pneumonia and lung cancer, entropy and homogeneity from phase congruency gave better results relative to Gabor. Omar Mohd Rijal, Hossein Ebrahimian, Norliza Mohd Noor, Amran Hussin, Ashari Yunus, and Aziah Ahmad Mahayiddin Copyright © 2015 Omar Mohd Rijal et al. All rights reserved. Classification of Parkinsonian Syndromes from FDG-PET Brain Data Using Decision Trees with SSM/PCA Features Tue, 31 Mar 2015 10:49:33 +0000 Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data. D. Mudali, L. K. Teune, R. J. Renken, K. L. Leenders, and J. B. T. M. Roerdink Copyright © 2015 D. Mudali et al. All rights reserved. Binary Matrix Shuffling Filter for Feature Selection in Neuronal Morphology Classification Sun, 29 Mar 2015 11:23:37 +0000 A prerequisite to understand neuronal function and characteristic is to classify neuron correctly. The existing classification techniques are usually based on structural characteristic and employ principal component analysis to reduce feature dimension. In this work, we dedicate to classify neurons based on neuronal morphology. A new feature selection method named binary matrix shuffling filter was used in neuronal morphology classification. This method, coupled with support vector machine for implementation, usually selects a small amount of features for easy interpretation. The reserved features are used to build classification models with support vector classification and another two commonly used classifiers. Compared with referred feature selection methods, the binary matrix shuffling filter showed optimal performance and exhibited broad generalization ability in five random replications of neuron datasets. Besides, the binary matrix shuffling filter was able to distinguish each neuron type from other types correctly; for each neuron type, private features were also obtained. Congwei Sun, Zhijun Dai, Hongyan Zhang, Lanzhi Li, and Zheming Yuan Copyright © 2015 Congwei Sun et al. All rights reserved. Stability Analysis of a Model of Atherosclerotic Plaque Growth Wed, 25 Mar 2015 06:22:36 +0000 Atherosclerosis, the formation of life-threatening plaques in blood vessels, is a form of cardiovascular disease. In this paper, we analyze a simplified model of plaque growth to derive physically meaningful results about the growth of plaques. In particular, the main results of this paper are two conditions, which express that the immune response increases as LDL cholesterol levels increase and that diffusion prevails over inflammation in a healthy artery. Sushruth Reddy and Padmanabhan Seshaiyer Copyright © 2015 Sushruth Reddy and Padmanabhan Seshaiyer. All rights reserved. Biomedical Signal Processing and Modeling Complexity of Living Systems 2014 Tue, 24 Mar 2015 12:05:42 +0000 Carlo Cattani, Shengyong Chen, and Igor Pantic Copyright © 2015 Carlo Cattani et al. All rights reserved. Quantitative Assessment of Cervical Vertebral Maturation Using Cone Beam Computed Tomography in Korean Girls Mon, 23 Mar 2015 11:28:52 +0000 This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6–18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (). The multiple regression model with the greatest had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status. Bo-Ram Byun, Yong-Il Kim, Tetsutaro Yamaguchi, Koutaro Maki, and Woo-Sung Son Copyright © 2015 Bo-Ram Byun et al. All rights reserved. Feature Quantification and Abnormal Detection on Cervical Squamous Epithelial Cells Sun, 22 Mar 2015 11:11:14 +0000 Feature analysis and classification detection of abnormal cells from images for pathological analysis are an important issue for the realization of computer assisted disease diagnosis. This paper studies a method for cervical squamous epithelial cells. Based on cervical cytological classification standard and expert diagnostic experience, expressive descriptors are extracted according to morphology, color, and texture features of cervical scales epithelial cells. Further, quantificational descriptors related to cytopathology are derived as well, including morphological difference degree, cell hyperkeratosis, and deeply stained degree. The relationship between quantified value and pathological feature can be established by these descriptors. Finally, an effective method is proposed for detecting abnormal cells based on feature quantification. Integrated with clinical experience, the method can realize fast abnormal cell detection and preliminary cell classification. Mingzhu Zhao, Lei Chen, Linjie Bian, Jianhua Zhang, Chunyan Yao, and Jianwei Zhang Copyright © 2015 Mingzhu Zhao et al. All rights reserved. Identifying Odd/Even-Order Binary Kernel Slices for a Nonlinear System Using Inverse Repeat m-Sequences Sun, 22 Mar 2015 11:00:01 +0000 The study of various living complex systems by system identification method is important, and the identification of the problem is even more challenging when dealing with a dynamic nonlinear system of discrete time. A well-established model based on kernel functions for input of the maximum length sequence (m-sequence) can be used to estimate nonlinear binary kernel slices using cross-correlation method. In this study, we examine the relevant mathematical properties of kernel slices, particularly their shift-and-product property and overlap distortion problem caused by the irregular shifting of the estimated kernel slices in the cross-correlation function between the input m-sequence and the system output. We then derive the properties of the inverse repeat (IR) m-sequence and propose a method of using IR m-sequence as an input to separately estimate odd- and even-order kernel slices to reduce the chance of kernel-slice overlapping. An instance of third-order Wiener nonlinear model is simulated to justify the proposed method. Jin-yan Hu, Gang Yan, and Tao Wang Copyright © 2015 Jin-yan Hu et al. All rights reserved. A Fingerprint Encryption Scheme Based on Irreversible Function and Secure Authentication Sun, 22 Mar 2015 09:51:43 +0000 A fingerprint encryption scheme based on irreversible function has been designed in this paper. Since the fingerprint template includes almost the entire information of users’ fingerprints, the personal authentication can be determined only by the fingerprint features. This paper proposes an irreversible transforming function (using the improved SHA1 algorithm) to transform the original minutiae which are extracted from the thinned fingerprint image. Then, Chinese remainder theorem is used to obtain the biokey from the integration of the transformed minutiae and the private key. The result shows that the scheme has better performance on security and efficiency comparing with other irreversible function schemes. Yijun Yang, Jianping Yu, Peng Zhang, and Shulan Wang Copyright © 2015 Yijun Yang et al. All rights reserved. The Prediction in Computer Color Matching of Dentistry Based on GA+BP Neural Network Sun, 22 Mar 2015 09:00:19 +0000 Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics. Back propagation neural network (BPNN) has already been introduced into the computer color matching in dentistry, but it has disadvantages such as unstable and low accuracy. In our study, we adopt genetic algorithm (GA) to optimize the initial weights and threshold values in BPNN for improving the matching precision. To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry. Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry. Haisheng Li, Long Lai, Li Chen, Cheng Lu, and Qiang Cai Copyright © 2015 Haisheng Li et al. All rights reserved. Motion Estimation Using the Firefly Algorithm in Ultrasonic Image Sequence of Soft Tissue Thu, 19 Mar 2015 08:30:11 +0000 Ultrasonic image sequence of the soft tissue is widely used in disease diagnosis; however, the speckle noises usually influenced the image quality. These images usually have a low signal-to-noise ratio presentation. The phenomenon gives rise to traditional motion estimation algorithms that are not suitable to measure the motion vectors. In this paper, a new motion estimation algorithm is developed for assessing the velocity field of soft tissue in a sequence of ultrasonic B-mode images. The proposed iterative firefly algorithm (IFA) searches for few candidate points to obtain the optimal motion vector, and then compares it to the traditional iterative full search algorithm (IFSA) via a series of experiments of in vivo ultrasonic image sequences. The experimental results show that the IFA can assess the vector with better efficiency and almost equal estimation quality compared to the traditional IFSA method. Chih-Feng Chao, Ming-Huwi Horng, and Yu-Chan Chen Copyright © 2015 Chih-Feng Chao et al. All rights reserved. Molecular Docking of Potential Inhibitors for Influenza H7N9 Sun, 15 Mar 2015 07:42:37 +0000 As a new strain of virus emerged in 2013, avian influenza A (H7N9) virus is a threat to the public health, due to its high lethality and pathogenicity. Furthermore, H7N9 has already generated various mutations such as neuraminidase R294K mutation which could make the anti-influenza oseltamivir less effective or ineffective. In this regard, it is urgent to develop new effective anti-H7N9 drug. In this study, we used the general H7N9 neuraminidase and oseltamivir-resistant influenza virus neuraminidase as the acceptors and employed the small molecules including quercetin, chlorogenic acid, baicalein, and oleanolic acid as the donors to perform the molecular docking for exploring the binding abilities between these small molecules and neuraminidase. The results showed that quercetin, chlorogenic acid, oleanolic acid, and baicalein present oseltamivir-comparable high binding potentials with neuraminidase. Further analyses showed that R294K mutation in neuraminidase could remarkably decrease the binding energies for oseltamivir, while other small molecules showed stable binding abilities with mutated neuraminidase. Taken together, the molecular docking studies identified four potential inhibitors for neuraminidase of H7N9, which might be effective for the drug-resistant mutants. Zekun Liu, Junpeng Zhao, Weichen Li, Xinkun Wang, Jingxuan Xu, Jin Xie, Ke Tao, Li Shen, and Ran Zhang Copyright © 2015 Zekun Liu et al. All rights reserved. Global Dynamics of Avian Influenza Epidemic Models with Psychological Effect Thu, 12 Mar 2015 13:20:56 +0000 Cross-sectional surveys conducted in Thailand and China after the outbreaks of the avian influenza A H5N1 and H7N9 viruses show a high degree of awareness of human avian influenza in both urban and rural populations, a higher level of proper hygienic practice among urban residents, and in particular a dramatically reduced number of visits to live markets in urban population after the influenza A H7N9 outbreak in China in 2013. In this paper, taking into account the psychological effect toward avian influenza in the human population, a bird-to-human transmission model in which the avian population exhibits saturation effect is constructed. The dynamical behavior of the model is studied by using the basic reproduction number. The results demonstrate that the saturation effect within avian population and the psychological effect in human population cannot change the stability of equilibria but can affect the number of infected humans if the disease is prevalent. Numerical simulations are given to support the theoretical results and sensitivity analyses of the basic reproduction number in terms of model parameters that are performed to seek for effective control measures for avian influenza. Sanhong Liu, Liuyong Pang, Shigui Ruan, and Xinan Zhang Copyright © 2015 Sanhong Liu et al. All rights reserved. Feature Engineering for Drug Name Recognition in Biomedical Texts: Feature Conjunction and Feature Selection Thu, 12 Mar 2015 08:12:18 +0000 Drug name recognition (DNR) is a critical step for drug information extraction. Machine learning-based methods have been widely used for DNR with various types of features such as part-of-speech, word shape, and dictionary feature. Features used in current machine learning-based methods are usually singleton features which may be due to explosive features and a large number of noisy features when singleton features are combined into conjunction features. However, singleton features that can only capture one linguistic characteristic of a word are not sufficient to describe the information for DNR when multiple characteristics should be considered. In this study, we explore feature conjunction and feature selection for DNR, which have never been reported. We intuitively select 8 types of singleton features and combine them into conjunction features in two ways. Then, Chi-square, mutual information, and information gain are used to mine effective features. Experimental results show that feature conjunction and feature selection can improve the performance of the DNR system with a moderate number of features and our DNR system significantly outperforms the best system in the DDIExtraction 2013 challenge. Shengyu Liu, Buzhou Tang, Qingcai Chen, Xiaolong Wang, and Xiaoming Fan Copyright © 2015 Shengyu Liu et al. All rights reserved. Chagas Parasite Detection in Blood Images Using AdaBoost Wed, 11 Mar 2015 08:32:05 +0000 The Chagas disease is a potentially life-threatening illness caused by the protozoan parasite, Trypanosoma cruzi. Visual detection of such parasite through microscopic inspection is a tedious and time-consuming task. In this paper, we provide an AdaBoost learning solution to the task of Chagas parasite detection in blood images. We give details of the algorithm and our experimental setup. With this method, we get 100% and 93.25% of sensitivity and specificity, respectively. A ROC comparison with the method most commonly used for the detection of malaria parasites based on support vector machines (SVM) is also provided. Our experimental work shows mainly two things: (1) Chagas parasites can be detected automatically using machine learning methods with high accuracy and (2) AdaBoost + SVM provides better overall detection performance than AdaBoost or SVMs alone. Such results are the best ones known so far for the problem of automatic detection of Chagas parasites through the use of machine learning, computer vision, and image processing methods. Víctor Uc-Cetina, Carlos Brito-Loeza, and Hugo Ruiz-Piña Copyright © 2015 Víctor Uc-Cetina et al. All rights reserved. Finding Top- Covering Irreducible Contrast Sequence Rules for Disease Diagnosis Tue, 10 Mar 2015 09:53:46 +0000 Diagnostic genes are usually used to distinguish different disease phenotypes. Most existing methods for diagnostic genes finding are based on either the individual or combinatorial discriminative power of gene(s). However, they both ignore the common expression trends among genes. In this paper, we devise a novel sequence rule, namely, top- irreducible covering contrast sequence rules (TopIRs for short), which helps to build a sample classifier of high accuracy. Furthermore, we propose an algorithm called MineTopIRs to efficiently discover TopIRs. Extensive experiments conducted on synthetic and real datasets show that MineTopIRs is significantly faster than the previous methods and is of a higher classification accuracy. Additionally, many diagnostic genes discovered provide a new insight into disease diagnosis. Yuhai Zhao, Yuan Li, Ying Yin, and Gang Sheng Copyright © 2015 Yuhai Zhao et al. All rights reserved. Predictive Models of Tumour Response to Treatment Using Functional Imaging Techniques Mon, 09 Mar 2015 14:22:10 +0000 Loredana G. Marcu, Eva Bezak, Iuliana Toma-Dasu, and Alexandru Dasu Copyright © 2015 Loredana G. Marcu et al. All rights reserved. Automatic Segmentation of Colon in 3D CT Images and Removal of Opacified Fluid Using Cascade Feed Forward Neural Network Mon, 09 Mar 2015 14:12:50 +0000 Purpose. Colon segmentation is an essential step in the development of computer-aided diagnosis systems based on computed tomography (CT) images. The requirement for the detection of the polyps which lie on the walls of the colon is much needed in the field of medical imaging for diagnosis of colorectal cancer. Methods. The proposed work is focused on designing an efficient automatic colon segmentation algorithm from abdominal slices consisting of colons, partial volume effect, bowels, and lungs. The challenge lies in determining the exact colon enhanced with partial volume effect of the slice. In this work, adaptive thresholding technique is proposed for the segmentation of air packets, machine learning based cascade feed forward neural network enhanced with boundary detection algorithms are used which differentiate the segments of the lung and the fluids which are sediment at the side wall of colon and by rejecting bowels based on the slice difference removal method. The proposed neural network method is trained with Bayesian regulation algorithm to determine the partial volume effect. Results. Experiment was conducted on CT database images which results in 98% accuracy and minimal error rate. Conclusions. The main contribution of this work is the exploitation of neural network algorithm for removal of opacified fluid to attain desired colon segmentation result. K. Gayathri Devi and R. Radhakrishnan Copyright © 2015 K. Gayathri Devi and R. Radhakrishnan. All rights reserved. A Novel Hybrid Dimension Reduction Technique for Undersized High Dimensional Gene Expression Data Sets Using Information Complexity Criterion for Cancer Classification Mon, 09 Mar 2015 07:06:09 +0000 Gene expression data typically are large, complex, and highly noisy. Their dimension is high with several thousand genes (i.e., features) but with only a limited number of observations (i.e., samples). Although the classical principal component analysis (PCA) method is widely used as a first standard step in dimension reduction and in supervised and unsupervised classification, it suffers from several shortcomings in the case of data sets involving undersized samples, since the sample covariance matrix degenerates and becomes singular. In this paper we address these limitations within the context of probabilistic PCA (PPCA) by introducing and developing a new and novel approach using maximum entropy covariance matrix and its hybridized smoothed covariance estimators. To reduce the dimensionality of the data and to choose the number of probabilistic PCs (PPCs) to be retained, we further introduce and develop celebrated Akaike’s information criterion (AIC), consistent Akaike’s information criterion (CAIC), and the information theoretic measure of complexity (ICOMP) criterion of Bozdogan. Six publicly available undersized benchmark data sets were analyzed to show the utility, flexibility, and versatility of our approach with hybridized smoothed covariance matrix estimators, which do not degenerate to perform the PPCA to reduce the dimension and to carry out supervised classification of cancer groups in high dimensions. Esra Pamukçu, Hamparsum Bozdogan, and Sinan Çalık Copyright © 2015 Esra Pamukçu et al. All rights reserved. Equilibrium Analysis of a Yellow Fever Dynamical Model with Vaccination Thu, 05 Mar 2015 13:40:11 +0000 We propose an equilibrium analysis of a dynamical model of yellow fever transmission in the presence of a vaccine. The model considers both human and vector populations. We found thresholds parameters that affect the development of the disease and the infectious status of the human population in the presence of a vaccine whose protection may wane over time. In particular, we derived a threshold vaccination rate, above which the disease would be eradicated from the human population. We show that if the mortality rate of the mosquitoes is greater than a given threshold, then the disease is naturally (without intervention) eradicated from the population. In contrast, if the mortality rate of the mosquitoes is less than that threshold, then the disease is eradicated from the populations only when the growing rate of humans is less than another threshold; otherwise, the disease is eradicated only if the reproduction number of the infection after vaccination is less than 1. When this reproduction number is greater than 1, the disease will be eradicated from the human population if the vaccination rate is greater than a given threshold; otherwise, the disease will establish itself among humans, reaching a stable endemic equilibrium. The analysis presented in this paper can be useful, both to the better understanding of the disease dynamics and also for the planning of vaccination strategies. Silvia Martorano Raimundo, Marcos Amaku, and Eduardo Massad Copyright © 2015 Silvia Martorano Raimundo et al. All rights reserved. Computational Intelligence Techniques in Medicine Thu, 05 Mar 2015 12:25:52 +0000 Ezequiel López-Rubio, David A. Elizondo, Martin Grootveld, José M. Jerez, and Rafael M. Luque-Baena Copyright © 2015 Ezequiel López-Rubio et al. All rights reserved. Towards Multidimensional Radiotherapy: Key Challenges for Treatment Individualisation Thu, 05 Mar 2015 07:15:51 +0000 Functional and molecular imaging of tumours have offered the possibility of redefining the target in cancer therapy and individualising the treatment with a multidimensional approach that aims to target the adverse processes known to impact negatively upon treatment result. Following the first theoretical attempts to include imaging information into treatment planning, it became clear that the biological features of interest for targeting exhibit considerable heterogeneity with respect to magnitude, spatial, and temporal distribution, both within one patient and between patients, which require more advanced solutions for the way the treatment is planned and adapted. Combining multiparameter information from imaging with predictive information from biopsies and molecular analyses as well as in treatment monitoring of tumour responsiveness appears to be the key approach to maximise the individualisation of treatment. This review paper aims to discuss some of the key challenges for incorporating into treatment planning and optimisation the radiobiological features of the tumour derived from pretreatment PET imaging of tumour metabolism, proliferation, and hypoxia and combining them with intreatment monitoring of responsiveness and other predictive factors with the ultimate aim of individualising the treatment towards the maximisation of response. Iuliana Toma-Dasu and Alexandru Dasu Copyright © 2015 Iuliana Toma-Dasu and Alexandru Dasu. All rights reserved. Transmission Dynamics of Resistant Bacteria in a Predator-Prey System Wed, 04 Mar 2015 13:03:40 +0000 This paper discusses the impact on human health caused by the addition of antibiotics in the feed of food animals. We use the established transmission rule of resistant bacteria and combine it with a predator-prey system to determine a differential equations model. The equations have three steady equilibrium points corresponding to three population dynamics states under the influence of resistant bacteria. In order to quantitatively analyze the stability of the equilibrium points, we focused on the basic reproduction numbers. Then, both the local and global stability of the equilibrium points were quantitatively analyzed by using essential mathematical methods. Numerical results are provided to relate our model properties to some interesting biological cases. Finally, we discuss the effect of the two main parameters of the model, the proportion of antibiotics added to feed and the predation rate, and estimate the human health impacts related to the amount of feed antibiotics used. We further propose an approach for the prevention of the large-scale spread of resistant bacteria and illustrate the necessity of controlling the amount of in-feed antibiotics used. Xubin Gao, Qiuhui Pan, and Mingfeng He Copyright © 2015 Xubin Gao et al. All rights reserved. Knowledge Mining from Clinical Datasets Using Rough Sets and Backpropagation Neural Network Wed, 04 Mar 2015 12:48:36 +0000 The availability of clinical datasets and knowledge mining methodologies encourages the researchers to pursue research in extracting knowledge from clinical datasets. Different data mining techniques have been used for mining rules, and mathematical models have been developed to assist the clinician in decision making. The objective of this research is to build a classifier that will predict the presence or absence of a disease by learning from the minimal set of attributes that has been extracted from the clinical dataset. In this work rough set indiscernibility relation method with backpropagation neural network (RS-BPNN) is used. This work has two stages. The first stage is handling of missing values to obtain a smooth data set and selection of appropriate attributes from the clinical dataset by indiscernibility relation method. The second stage is classification using backpropagation neural network on the selected reducts of the dataset. The classifier has been tested with hepatitis, Wisconsin breast cancer, and Statlog heart disease datasets obtained from the University of California at Irvine (UCI) machine learning repository. The accuracy obtained from the proposed method is 97.3%, 98.6%, and 90.4% for hepatitis, breast cancer, and heart disease, respectively. The proposed system provides an effective classification model for clinical datasets. Kindie Biredagn Nahato, Khanna Nehemiah Harichandran, and Kannan Arputharaj Copyright © 2015 Kindie Biredagn Nahato et al. All rights reserved. Windowed Multitaper Correlation Analysis of Multimodal Brain Monitoring Parameters Tue, 03 Mar 2015 16:46:39 +0000 Although multimodal monitoring sets the standard in daily practice of neurocritical care, problem-oriented analysis tools to interpret the huge amount of data are lacking. Recently a mathematical model was presented that simulates the cerebral perfusion and oxygen supply in case of a severe head trauma, predicting the appearance of distinct correlations between arterial blood pressure and intracranial pressure. In this study we present a set of mathematical tools that reliably detect the predicted correlations in data recorded at a neurocritical care unit. The time resolved correlations will be identified by a windowing technique combined with Fourier-based coherence calculations. The phasing of the data is detected by means of Hilbert phase difference within the above mentioned windows. A statistical testing method is introduced that allows tuning the parameters of the windowing method in such a way that a predefined accuracy is reached. With this method the data of fifteen patients were examined in which we found the predicted correlation in each patient. Additionally it could be shown that the occurrence of a distinct correlation parameter, called scp, represents a predictive value of high quality for the patients outcome. Rupert Faltermeier, Martin A. Proescholdt, Sylvia Bele, and Alexander Brawanski Copyright © 2015 Rupert Faltermeier et al. All rights reserved. A Sparse Representation Based Method to Classify Pulmonary Patterns of Diffuse Lung Diseases Tue, 03 Mar 2015 08:48:33 +0000 We applied and optimized the sparse representation (SR) approaches in the computer-aided diagnosis (CAD) to classify normal tissues and five kinds of diffuse lung disease (DLD) patterns: consolidation, ground-glass opacity, honeycombing, emphysema, and nodule. By using the K-SVD which is based on the singular value decomposition (SVD) and orthogonal matching pursuit (OMP), it can achieve a satisfied recognition rate, but too much time was spent in the experiment. To reduce the runtime of the method, the K-Means algorithm was substituted for the K-SVD, and the OMP was simplified by searching the desired atoms at one time (OMP1). We proposed three SR based methods for evaluation: SR1 (K-SVD+OMP), SR2 (K-Means+OMP), and SR3 (K-Means+OMP1). 1161 volumes of interest (VOIs) were used to optimize the parameters and train each method, and 1049 VOIs were adopted to evaluate the performances of the methods. The SR based methods were powerful to recognize the DLD patterns (SR1: 96.1%, SR2: 95.6%, SR3: 96.4%) and significantly better than the baseline methods. Furthermore, when the K-Means and OMP1 were applied, the runtime of the SR based methods can be reduced by 98.2% and 55.2%, respectively. Therefore, we thought that the method using the K-Means and OMP1 (SR3) was efficient for the CAD of the DLDs. Wei Zhao, Rui Xu, Yasushi Hirano, Rie Tachibana, and Shoji Kido Copyright © 2015 Wei Zhao et al. All rights reserved. Method for Detecting Core Malware Sites Related to Biomedical Information Systems Tue, 03 Mar 2015 07:27:58 +0000 Most advanced persistent threat attacks target web users through malicious code within landing (exploit) or distribution sites. There is an urgent need to block the affected websites. Attacks on biomedical information systems are no exception to this issue. In this paper, we present a method for locating malicious websites that attempt to attack biomedical information systems. Our approach uses malicious code crawling to rearrange websites in the order of their risk index by analyzing the centrality between malware sites and proactively eliminates the root of these sites by finding the core-hub node, thereby reducing unnecessary security policies. In particular, we dynamically estimate the risk index of the affected websites by analyzing various centrality measures and converting them into a single quantified vector. On average, the proactive elimination of core malicious websites results in an average improvement in zero-day attack detection of more than 20%. Dohoon Kim, Donghee Choi, and Jonghyun Jin Copyright © 2015 Dohoon Kim et al. All rights reserved. Handling Diagnosis of Schizophrenia by a Hybrid Method Mon, 02 Mar 2015 13:36:51 +0000 Psychotics disorders, most commonly known as schizophrenia, have incapacitated professionals in different sectors of activities. Those disorders have caused damage in a microlevel to the individual and his/her family and in a macrolevel to the economic and production system of the country. The lack of early and sometimes very late diagnosis has provided reactive measures, when the professional is already showing psychological signs of incapacity to work. This study aims to help the early diagnosis of psychotics’ disorders with a hybrid proposal of an expert system that is integrated to structured methodologies in decision support (multicriteria decision analysis: MCDA) and knowledge structured representations into production rules and probabilities (artificial intelligence: AI). Luciano Comin Nunes, Plácido Rogério Pinheiro, Tarcísio Pequeno Cavalcante, and Mirian Calíope Dantas Pinheiro Copyright © 2015 Luciano Comin Nunes et al. All rights reserved. A Hybrid Intelligent Diagnosis Approach for Quick Screening of Alzheimer’s Disease Based on Multiple Neuropsychological Rating Scales Sun, 01 Mar 2015 13:30:56 +0000 Neuropsychological testing is an effective means for the screening of Alzheimer’s disease. Multiple neuropsychological rating scales should be used together to get subjects’ comprehensive cognitive state due to the limitation of a single scale, but it is difficult to operate in primary clinical settings because of the inadequacy of time and qualified clinicians. Aiming at identifying AD’s stages more accurately and conveniently in screening, we proposed a computer-aided diagnosis approach based on critical items extracted from multiple neuropsychological scales. The proposed hybrid intelligent approach combines the strengths of rough sets, genetic algorithm, and Bayesian network. There are two stages: one is attributes reduction technique based on rough sets and genetic algorithm, which can find out the most discriminative items for AD diagnosis in scales; the other is uncertain reasoning technique based on Bayesian network, which can forecast the probability of suffering from AD. The experimental data set consists of 500 cases collected by a top hospital in China and each case is determined by the expert panel. The results showed that the proposed approach could not only reduce items drastically with the same classification precision, but also perform better on identifying different stages of AD comparing with other existing scales. Ziming Yin, Yinhong Zhao, Xudong Lu, and Huilong Duan Copyright © 2015 Ziming Yin et al. All rights reserved. MRI Segmentation of the Human Brain: Challenges, Methods, and Applications Sun, 01 Mar 2015 11:59:10 +0000 Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain’s anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation. Ivana Despotović, Bart Goossens, and Wilfried Philips Copyright © 2015 Ivana Despotović et al. All rights reserved.