Computational and Mathematical Methods in Medicine The latest articles from Hindawi Publishing Corporation © 2015 , Hindawi Publishing Corporation . All rights reserved. MRI Brain Images Healthy and Pathological Tissues Classification with the Aid of Improved Particle Swarm Optimization and Neural Network Wed, 22 Apr 2015 07:41:43 +0000 The advantages of magnetic resonance imaging (MRI) over other diagnostic imaging modalities are its higher spatial resolution and its better discrimination of soft tissue. In the previous tissues classification method, the healthy and pathological tissues are classified from the MRI brain images using HGANN. But the method lacks sensitivity and accuracy measures. The classification method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new classification method is proposed in this paper. Here, new tissues classification method is proposed with improved particle swarm optimization (IPSO) technique to classify the healthy and pathological tissues from the given MRI images. Our proposed classification method includes the same four stages, namely, tissue segmentation, feature extraction, heuristic feature selection, and tissue classification. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of the proposed classification method in classifying the tissues and the achieved improvement in sensitivity and accuracy measures. Furthermore, the performance of the proposed technique is evaluated by comparing it with the other segmentation methods. V. Sheejakumari and B. Sankara Gomathi Copyright © 2015 V. Sheejakumari and B. Sankara Gomathi. All rights reserved. Exploring Sampling in the Detection of Multicategory EEG Signals Tue, 21 Apr 2015 05:54:00 +0000 The paper presents a structure based on samplings and machine leaning techniques for the detection of multicategory EEG signals where random sampling (RS) and optimal allocation sampling (OS) are explored. In the proposed framework, before using the RS and OS scheme, the entire EEG signals of each class are partitioned into several groups based on a particular time period. The RS and OS schemes are used in order to have representative observations from each group of each category of EEG data. Then all of the selected samples by the RS from the groups of each category are combined in a one set named RS set. In the similar way, for the OS scheme, an OS set is obtained. Then eleven statistical features are extracted from the RS and OS set, separately. Finally this study employs three well-known classifiers: k-nearest neighbor (k-NN), multinomial logistic regression with a ridge estimator (MLR), and support vector machine (SVM) to evaluate the performance for the RS and OS feature set. The experimental outcomes demonstrate that the RS scheme well represents the EEG signals and the k-NN with the RS is the optimum choice for detection of multicategory EEG signals. Siuly Siuly, Enamul Kabir, Hua Wang, and Yanchun Zhang Copyright © 2015 Siuly Siuly et al. All rights reserved. Prediction of High-Risk Types of Human Papillomaviruses Using Statistical Model of Protein “Sequence Space” Mon, 20 Apr 2015 10:27:37 +0000 Discrimination of high-risk types of human papillomaviruses plays an important role in the diagnosis and remedy of cervical cancer. Recently, several computational methods have been proposed based on protein sequence-based and structure-based information, but the information of their related proteins has not been used until now. In this paper, we proposed using protein “sequence space” to explore this information and used it to predict high-risk types of HPVs. The proposed method was tested on 68 samples with known HPV types and 4 samples without HPV types and further compared with the available approaches. The results show that the proposed method achieved the best performance among all the evaluated methods with accuracy 95.59% and F1-score 90.91%, which indicates that protein “sequence space” could potentially be used to improve prediction of high-risk types of HPVs. Cong Wang, Yabing Hai, Xiaoqing Liu, Nanfang Liu, Yuhua Yao, Pingan He, and Qi Dai Copyright © 2015 Cong Wang et al. All rights reserved. Effect of Fluid Friction on Interstitial Fluid Flow Coupled with Blood Flow through Solid Tumor Microvascular Network Sun, 19 Apr 2015 11:57:35 +0000 A solid tumor is investigated as porous media for fluid flow simulation. Most of the studies use Darcy model for porous media. In Darcy model, the fluid friction is neglected and a few simplified assumptions are implemented. In this study, the effect of these assumptions is studied by considering Brinkman model. A multiscale mathematical method which calculates fluid flow to a solid tumor is used in this study to investigate how neglecting fluid friction affects the solid tumor simulation. The mathematical method involves processes such as blood flow through vessels and solute and fluid diffusion, convective transport in extracellular matrix, and extravasation from blood vessels. The sprouting angiogenesis model is used for generating capillary network and then fluid flow governing equations are implemented to calculate blood flow through the tumor-induced capillary network. Finally, the two models of porous media are used for modeling fluid flow in normal and tumor tissues in three different shapes of tumors. Simulations of interstitial fluid transport in a solid tumor demonstrate that the simplifications used in Darcy model affect the interstitial velocity and Brinkman model predicts a lower value for interstitial velocity than the values that Darcy model predicts. Mostafa Sefidgar, M. Soltani, Kaamran Raahemifar, and Hossein Bazmara Copyright © 2015 Mostafa Sefidgar et al. All rights reserved. Network-Based Inference Methods for Drug Repositioning Sun, 12 Apr 2015 08:28:34 +0000 Mining potential drug-disease associations can speed up drug repositioning for pharmaceutical companies. Previous computational strategies focused on prior biological information for association inference. However, such information may not be comprehensively available and may contain errors. Different from previous research, two inference methods, ProbS and HeatS, were introduced in this paper to predict direct drug-disease associations based only on the basic network topology measure. Bipartite network topology was used to prioritize the potentially indicated diseases for a drug. Experimental results showed that both methods can receive reliable prediction performance and achieve AUC values of 0.9192 and 0.9079, respectively. Case studies on real drugs indicated that some of the strongly predicted associations were confirmed by results in the Comparative Toxicogenomics Database (CTD). Finally, a comprehensive prediction of drug-disease associations enables us to suggest many new drug indications for further studies. Hailin Chen, Heng Zhang, Zuping Zhang, Yiqin Cao, and Wenliang Tang Copyright © 2015 Hailin Chen et al. All rights reserved. Optimal Control Strategy for Abnormal Innate Immune Response Wed, 08 Apr 2015 06:14:16 +0000 Innate immune response plays an important role in control and clearance of pathogens following viral infection. However, in the majority of virus-infected individuals, the response is insufficient because viruses are known to use different evasion strategies to escape immune response. In this study, we use optimal control theory to investigate how to control the innate immune response. We present an optimal control model based on an ordinary-differential-equation system from a previous study, which investigated the dynamics and regulation of virus-triggered innate immune signaling pathways, and we prove the existence of a solution to the optimal control problem involving antiviral treatment or/and interferon therapy. We conduct numerical experiments to investigate the treatment effects of different control strategies through varying the cost function and control efficiency. The results show that a separate treatment, that is, only inhibiting viral replication () or enhancing interferon activity (), has more advantages for controlling viral infection than a mixed treatment, that is, controlling both () and () simultaneously, including the smallest cost and operability. These findings would provide new insight for developing effective strategies for treatment of viral infectious diseases. Jinying Tan and Xiufen Zou Copyright © 2015 Jinying Tan and Xiufen Zou. All rights reserved. Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification Tue, 07 Apr 2015 13:05:06 +0000 The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms. Hui Liu, Cai-Ming Zhang, Zhi-Yuan Su, Kai Wang, and Kai Deng Copyright © 2015 Hui Liu et al. All rights reserved. Computer-Aided Lung Nodule Recognition by SVM Classifier Based on Combination of Random Undersampling and SMOTE Mon, 06 Apr 2015 15:05:19 +0000 In lung cancer computer-aided detection/diagnosis (CAD) systems, classification of regions of interest (ROI) is often used to detect/diagnose lung nodule accurately. However, problems of unbalanced datasets often have detrimental effects on the performance of classification. In this paper, both minority and majority classes are resampled to increase the generalization ability. We propose a novel SVM classifier combined with random undersampling (RU) and SMOTE for lung nodule recognition. The combinations of the two resampling methods not only achieve a balanced training samples but also remove noise and duplicate information in the training sample and retain useful information to improve the effective data utilization, hence improving performance of SVM algorithm for pulmonary nodules classification under the unbalanced data. Eight features including 2D and 3D features are extracted for training and classification. Experimental results show that for different sizes of training datasets our RU-SMOTE-SVM classifier gets the highest classification accuracy among the four kinds of classifiers, and the average classification accuracy is more than 92.94%. Yuan Sui, Ying Wei, and Dazhe Zhao Copyright © 2015 Yuan Sui et al. All rights reserved. Structural, Dynamical, and Energetical Consequences of Rett Syndrome Mutation R133C in MeCP2 Sun, 05 Apr 2015 11:33:00 +0000 Rett Syndrome (RTT) is a progressive neurodevelopmental disease affecting females. RTT is caused by mutations in the MECP2 gene and various amino acid substitutions have been identified clinically in different domains of the multifunctional MeCP2 protein encoded by this gene. The R133C variant in the methylated-CpG-binding domain (MBD) of MeCP2 is the second most common disease-causing mutation in the MBD. Comparative molecular dynamics simulations of R133C mutant and wild-type MBD have been performed to understand the impact of the mutation on structure, dynamics, and interactions of the protein and subsequently understand the disease mechanism. Two salt bridges within the protein and two critical hydrogen bonds between the protein and DNA are lost upon the R133C mutation. The mutation was found to weaken the interaction with DNA and also cause loss of helicity within the 141-144 region. The structural, dynamical, and energetical consequences of R133C mutation were investigated in detail at the atomic resolution. Several important implications of this have been shown regarding protein stability and hydration dynamics as well as its interaction with DNA. The results are in agreement with previous experimental studies and further provide atomic level understanding of the molecular origin of RTT associated with R133C variant. Tugba G. Kucukkal and Emil Alexov Copyright © 2015 Tugba G. Kucukkal and Emil Alexov. 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.