Computational and Mathematical Methods in Medicine The latest articles from Hindawi Publishing Corporation © 2015 , Hindawi Publishing Corporation . All rights reserved. Community-Based Decision Making and Priority Setting Using the R Software: The Community Priority Index Thu, 26 Feb 2015 10:47:17 +0000 This paper outlines how to compute community priority indices in the context of multicriteria decision making in community settings. A simple R function was developed and validated with community needs assessment data. Particularly, the first part of this paper briefly overviews the existing methods for priority setting and reviews the utility of a multicriteria decision-making approach for community-based prioritization. The second part illustrates how community priority indices can be calculated using the freely available R program to handle community data by showing the computational and mathematical steps of CPI (Community Priority Index) with bootstrapped 95% confidence intervals. Hamisu M. Salihu, Abraham A. Salinas-Miranda, Arnut Paothong, Wei Wang, and Lindsey M. King Copyright © 2015 Hamisu M. Salihu et al. All rights reserved. Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China Thu, 26 Feb 2015 07:20:44 +0000 Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method’s feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes. Ruijing Gan, Xiaojun Chen, Yu Yan, and Daizheng Huang Copyright © 2015 Ruijing Gan et al. All rights reserved. Genetic Programming Based Ensemble System for Microarray Data Classification Wed, 25 Feb 2015 14:26:12 +0000 Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved. Kun-Hong Liu, Muchenxuan Tong, Shu-Tong Xie, and Vincent To Yee Ng Copyright © 2015 Kun-Hong Liu et al. All rights reserved. Computer-Aided Assessment of Tumor Grade for Breast Cancer in Ultrasound Images Wed, 25 Feb 2015 06:21:56 +0000 This study involved developing a computer-aided diagnosis (CAD) system for discriminating the grades of breast cancer tumors in ultrasound (US) images. Histological tumor grades of breast cancer lesions are standard prognostic indicators. Tumor grade information enables physicians to determine appropriate treatments for their patients. US imaging is a noninvasive approach to breast cancer examination. In this study, 148 3-dimensional US images of malignant breast tumors were obtained. Textural, morphological, ellipsoid fitting, and posterior acoustic features were quantified to characterize the tumor masses. A support vector machine was developed to classify breast tumor grades as either low or high. The proposed CAD system achieved an accuracy of 85.14% (126/148), a sensitivity of 79.31% (23/29), a specificity of 86.55% (103/119), and an of 0.7940. Dar-Ren Chen, Cheng-Liang Chien, and Yan-Fu Kuo Copyright © 2015 Dar-Ren Chen et al. All rights reserved. Adaptive Thresholding Technique for Retinal Vessel Segmentation Based on GLCM-Energy Information Tue, 24 Feb 2015 07:15:15 +0000 Although retinal vessel segmentation has been extensively researched, a robust and time efficient segmentation method is highly needed. This paper presents a local adaptive thresholding technique based on gray level cooccurrence matrix- (GLCM-) energy information for retinal vessel segmentation. Different thresholds were computed using GLCM-energy information. An experimental evaluation on DRIVE database using the grayscale intensity and Green Channel of the retinal image demonstrates the high performance of the proposed local adaptive thresholding technique. The maximum average accuracy rates of 0.9511 and 0.9510 with maximum average sensitivity rates of 0.7650 and 0.7641 were achieved on DRIVE and STARE databases, respectively. When compared to the widely previously used techniques on the databases, the proposed adaptive thresholding technique is time efficient with a higher average sensitivity and average accuracy rates in the same range of very good specificity. Temitope Mapayi, Serestina Viriri, and Jules-Raymond Tapamo Copyright © 2015 Temitope Mapayi et al. All rights reserved. Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy Diagnosis Tue, 24 Feb 2015 06:47:38 +0000 Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients. DR is mainly caused due to the damage of retinal blood vessels in the diabetic patients. It is essential to detect and segment the retinal blood vessels for DR detection and diagnosis, which prevents earlier vision loss in diabetic patients. The computer aided automatic detection and segmentation of blood vessels through the elimination of optic disc (OD) region in retina are proposed in this paper. The OD region is segmented using anisotropic diffusion filter and subsequentially the retinal blood vessels are detected using mathematical binary morphological operations. The proposed methodology is tested on two different publicly available datasets and achieved 93.99% sensitivity, 98.37% specificity, 98.08% accuracy in DRIVE dataset and 93.6% sensitivity, 98.96% specificity, and 95.94% accuracy in STARE dataset, respectively. D. Siva Sundhara Raja and S. Vasuki Copyright © 2015 D. Siva Sundhara Raja and S. Vasuki. All rights reserved. A Further Finite Element Stress Analysis of Angled Abutments for an Implant Placed in the Anterior Maxilla Mon, 23 Feb 2015 10:07:45 +0000 To systematically measure and compare the stress distribution on the bone around an implant in the anterior maxilla using angled abutments by means of finite element analysis, three-dimensional finite element simplified patient-specific models and simplified models were created and analyzed. Systematically varied angled abutments were simulated, with angulation ranging from 0° to 60°. The materials in the current study were assumed to be homogenous, linearly elastic, and isotropic. Force of 100 N was applied to the central node on the top surface of the abutments to simulate the occlusal force. To simulate axial and oblique loading, the angle of loading was 0°, 15°, and 20° to the long axis of implant, respectively. There was the strong resemblance between the response curves for simplified patient-specific models and simplified models. Response curves under oblique loading were similar in both models. With abutments angulation increased, maximum von Mises stress firstly decreased to minimum point and then gradually increased to higher level. From a biomechanical point of view, favorable peri-implant stress levels could be induced by angled abutments under oblique loading if suitable angulation of abutments was selected. Dong Wu, Kebin Tian, Jiang Chen, Hua Jin, Wenxiu Huang, and Yuyu Liu Copyright © 2015 Dong Wu et al. All rights reserved. A Novel Clinical Decision Support System Using Improved Adaptive Genetic Algorithm for the Assessment of Fetal Well-Being Sun, 22 Feb 2015 14:52:45 +0000 A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm. Sindhu Ravindran, Asral Bahari Jambek, Hariharan Muthusamy, and Siew-Chin Neoh Copyright © 2015 Sindhu Ravindran et al. All rights reserved. Adaptive Localization of Focus Point Regions via Random Patch Probabilistic Density from Whole-Slide, Ki-67-Stained Brain Tumor Tissue Sun, 22 Feb 2015 12:07:37 +0000 Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the -means and fuzzy -means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved. Yazan M. Alomari, Siti Norul Huda Sheikh Abdullah, Reena Rahayu MdZin, and Khairuddin Omar Copyright © 2015 Yazan M. Alomari et al. All rights reserved. Comparative Study of Retinal Vessel Segmentation Based on Global Thresholding Techniques Sun, 22 Feb 2015 09:55:52 +0000 Due to noise from uneven contrast and illumination during acquisition process of retinal fundus images, the use of efficient preprocessing techniques is highly desirable to produce good retinal vessel segmentation results. This paper develops and compares the performance of different vessel segmentation techniques based on global thresholding using phase congruency and contrast limited adaptive histogram equalization (CLAHE) for the preprocessing of the retinal images. The results obtained show that the combination of preprocessing technique, global thresholding, and postprocessing techniques must be carefully chosen to achieve a good segmentation performance. Temitope Mapayi, Serestina Viriri, and Jules-Raymond Tapamo Copyright © 2015 Temitope Mapayi et al. All rights reserved. Comment on “Impact of Dose and Sensitivity Heterogeneity on TCP” Sun, 22 Feb 2015 08:57:31 +0000 Erik Grusell Copyright © 2015 Erik Grusell. All rights reserved. PET-Specific Parameters and Radiotracers in Theoretical Tumour Modelling Thu, 19 Feb 2015 09:54:00 +0000 The innovation of computational techniques serves as an important step toward optimized, patient-specific management of cancer. In particular, in silico simulation of tumour growth and treatment response may eventually yield accurate information on disease progression, enhance the quality of cancer treatment, and explain why certain therapies are effective where others are not. In silico modelling is demonstrated to considerably benefit from information obtainable with PET and PET/CT. In particular, models have successfully integrated tumour glucose metabolism, cell proliferation, and cell oxygenation from multiple tracers in order to simulate tumour behaviour. With the development of novel radiotracers to image additional tumour phenomena, such as pH and gene expression, the value of PET and PET/CT data for use in tumour models will continue to grow. In this work, the use of PET and PET/CT information in in silico tumour models is reviewed. The various parameters that can be obtained using PET and PET/CT are detailed, as well as the radiotracers that may be used for this purpose, their utility, and limitations. The biophysical measures used to quantify PET and PET/CT data are also described. Finally, a list of in silico models that incorporate PET and/or PET/CT data is provided and reviewed. Matthew Jennings, Loredana G. Marcu, and Eva Bezak Copyright © 2015 Matthew Jennings et al. All rights reserved. Multimodality Functional Imaging in Radiation Therapy Planning: Relationships between Dynamic Contrast-Enhanced MRI, Diffusion-Weighted MRI, and 18F-FDG PET Thu, 19 Feb 2015 09:23:04 +0000 Objectives. Biologically guided radiotherapy needs an understanding of how different functional imaging techniques interact and link together. We analyse three functional imaging techniques that can be useful tools for achieving this objective. Materials and Methods. The three different imaging modalities from one selected patient are ADC maps, DCE-MRI, and 18F-FDG PET/CT, because they are widely used and give a great amount of complementary information. We show the relationship between these three datasets and evaluate them as markers for tumour response or hypoxia marker. Thus, vascularization measured using DCE-MRI parameters can determine tumour hypoxia, and ADC maps can be used for evaluating tumour response. Results. ADC and DCE-MRI include information from 18F-FDG, as glucose metabolism is associated with hypoxia and tumour cell density, although 18F-FDG includes more information about the malignancy of the tumour. The main disadvantage of ADC maps is the distortion, and we used only low distorted regions, and extracellular volume calculated from DCE-MRI can be considered equivalent to ADC in well-vascularized areas. Conclusion. A dataset for achieving the biologically guided radiotherapy must include a tumour density study and a hypoxia marker. This information can be achieved using only MRI data or only PET/CT studies or mixing both datasets. Moisés Mera Iglesias, David Aramburu Núñez, José Luis del Olmo Claudio, Antonio López Medina, Iago Landesa-Vázquez, Francisco Salvador Gómez, Brandon Driscoll, Catherine Coolens, José L. Alba Castro, and Victor Muñoz Copyright © 2015 Moisés Mera Iglesias et al. All rights reserved. A Bayesian Inferential Approach to Quantify the Transmission Intensity of Disease Outbreak Sun, 15 Feb 2015 13:58:33 +0000 Background. Emergence of infectious diseases like influenza pandemic (H1N1) 2009 has become great concern, which posed new challenges to the health authorities worldwide. To control these diseases various studies have been developed in the field of mathematical modelling, which is useful tool for understanding the epidemiological dynamics and their dependence on social mixing patterns. Method. We have used Bayesian approach to quantify the disease outbreak through key epidemiological parameter basic reproduction number (), using effective contacts, defined as sum of the product of incidence cases and probability of generation time distribution. We have estimated from daily case incidence data for pandemic influenza A/H1N1 2009 in India, for the initial phase. Result. The estimated with 95% credible interval is consistent with several other studies on the same strain. Through sensitivity analysis our study indicates that infectiousness affects the estimate of . Conclusion. Basic reproduction number provides the useful information to the public health system to do some effort in controlling the disease by using mitigation strategies like vaccination, quarantine, and so forth. Adiveppa S. Kadi and Shivakumari R. Avaradi Copyright © 2015 Adiveppa S. Kadi and Shivakumari R. Avaradi. All rights reserved. In Silico Design of Human IMPDH Inhibitors Using Pharmacophore Mapping and Molecular Docking Approaches Sun, 15 Feb 2015 12:15:12 +0000 Inosine 5′-monophosphate dehydrogenase (IMPDH) is one of the crucial enzymes in the de novo biosynthesis of guanosine nucleotides. It has served as an attractive target in immunosuppressive, anticancer, antiviral, and antiparasitic therapeutic strategies. In this study, pharmacophore mapping and molecular docking approaches were employed to discover novel Homo sapiens IMPDH (hIMPDH) inhibitors. The Güner-Henry (GH) scoring method was used to evaluate the quality of generated pharmacophore hypotheses. One of the generated pharmacophore hypotheses was found to possess a GH score of 0.67. Ten potential compounds were selected from the ZINC database using a pharmacophore mapping approach and docked into the IMPDH active site. We find two hits (i.e., ZINC02090792 and ZINC00048033) that match well the optimal pharmacophore features used in this investigation, and it is found that they form interactions with key residues of IMPDH. We propose that these two hits are lead compounds for the development of novel hIMPDH inhibitors. Rui-Juan Li, Ya-Li Wang, Qing-He Wang, Jian Wang, and Mao-Sheng Cheng Copyright © 2015 Rui-Juan Li et al. All rights reserved. Method for 3D Airway Topology Extraction Thu, 12 Feb 2015 10:23:24 +0000 In lungs the number of conducting airway generations as well as bifurcation patterns varies across species and shows specific characteristics relating to illnesses or gene variations. A method to characterize the topology of the mouse airway tree using scanning laser optical tomography (SLOT) tomograms is presented in this paper. It is used to test discrimination between two types of mice based on detected differences in their conducting airway pattern. Based on segmentations of the airways in these tomograms, the main spanning tree of the volume skeleton is computed. The resulting graph structure is used to distinguish between wild type and surfactant protein (SP-D) deficient knock-out mice. Roman Grothausmann, Manuela Kellner, Marko Heidrich, Raoul-Amadeus Lorbeer, Tammo Ripken, Heiko Meyer, Mark P. Kuehnel, Matthias Ochs, and Bodo Rosenhahn Copyright © 2015 Roman Grothausmann et al. All rights reserved. Cognitive Behavior Evaluation Based on Physiological Parameters among Young Healthy Subjects with Yoga as Intervention Wed, 11 Feb 2015 09:31:09 +0000 Objective. To investigate the effect of yoga practice on cognitive skills, autonomic nervous system, and heart rate variability by analyzing physiological parameters. Methods. The study was conducted on 30 normal young healthy engineering students. They were randomly selected into two groups: yoga group and control group. The yoga group practiced yoga one and half hour per day for six days in a week, for a period of five months. Results. The yoga practising group showed increased α, β, and δ EEG band powers and significant reduction in θ and γ band powers. The increased α and β power can represent enhanced cognitive functions such as memory and concentration, and that of δ signifies synchronization of brain activity. The heart rate index decreased, neural activity β/θ increased, attention resource index increased, executive load index decreased, and the ratio decreased. The yoga practice group showed improvement in heart rate variability, increased SDNN/RMSSD, and reduction in LF/HF ratio. Conclusion. Yoga practising group showed significant improvement in various cognitive functions, such as performance enhancement, neural activity, attention, and executive function. It also resulted in increase in the heart rate variability, parasympathetic nervous system activity, and balanced autonomic nervous system reactivity. H. Nagendra, Vinod Kumar, and S. Mukherjee Copyright © 2015 H. Nagendra et al. All rights reserved. Tactics and Strategies for Managing Ebola Outbreaks and the Salience of Immunization Tue, 10 Feb 2015 08:32:44 +0000 We present a stochastic transmission chain simulation model for Ebola viral disease (EVD) in West Africa, with the salutary result that the virus may be more controllable than previously suspected. The ongoing tactics to detect cases as rapidly as possible and isolate individuals as safely as practicable is essential to saving lives in the current outbreaks in Guinea, Liberia, and Sierra Leone. Equally important are educational campaigns that reduce contact rates between susceptible and infectious individuals in the community once an outbreak occurs. However, due to the relatively low of Ebola (around 1.5 to 2.5 next generation cases are produced per current generation case in naïve populations), rapid isolation of infectious individuals proves to be highly efficacious in containing outbreaks in new areas, while vaccination programs, even with low efficacy vaccines, can be decisive in curbing future outbreaks in areas where the Ebola virus is maintained in reservoir populations. Wayne M. Getz, Jean-Paul Gonzalez, Richard Salter, James Bangura, Colin Carlson, Moinya Coomber, Eric Dougherty, David Kargbo, Nathan D. Wolfe, and Nadia Wauquier Copyright © 2015 Wayne M. Getz et al. All rights reserved. Effects of Electrode Position on Spatiotemporal Auditory Nerve Fiber Responses: A 3D Computational Model Study Tue, 10 Feb 2015 07:01:20 +0000 A cochlear implant (CI) is an auditory prosthesis that enables hearing by providing electrical stimuli through an electrode array. It has been previously established that the electrode position can influence CI performance. Thus, electrode position should be considered in order to achieve better CI results. This paper describes how the electrode position influences the auditory nerve fiber (ANF) response to either a single pulse or low- (250 pulses/s) and high-rate (5,000 pulses/s) pulse-trains using a computational model. The field potential in the cochlea was calculated using a three-dimensional finite-element model, and the ANF response was simulated using a biophysical ANF model. The effects were evaluated in terms of the dynamic range, stochasticity, and spike excitation pattern. The relative spread, threshold, jitter, and initiated node were analyzed for single-pulse response; and the dynamic range, threshold, initiated node, and interspike interval were analyzed for pulse-train stimuli responses. Electrode position was found to significantly affect the spatiotemporal pattern of the ANF response, and this effect was significantly dependent on the stimulus rate. We believe that these modeling results can provide guidance regarding perimodiolar and lateral insertion of CIs in clinical settings and help understand CI performance. Soojin Kang, Tanmoy Chwodhury, Il Joon Moon, Sung Hwa Hong, Hyejin Yang, Jong Ho Won, and Jihwan Woo Copyright © 2015 Soojin Kang et al. All rights reserved. A Spatial Shape Constrained Clustering Method for Mammographic Mass Segmentation Sun, 08 Feb 2015 08:54:23 +0000 A novel clustering method is proposed for mammographic mass segmentation on extracted regions of interest (ROIs) by using deterministic annealing incorporating circular shape function (DACF). The objective function reported in this study uses both intensity and spatial shape information, and the dominant dissimilarity measure is controlled by two weighting parameters. As a result, pixels having similar intensity information but located in different regions can be differentiated. Experimental results shows that, by using DACF, the mass segmentation results in digitized mammograms are improved with optimal mass boundaries, less number of noisy patches, and computational efficiency. An average probability of segmentation error of 7.18% for well-defined masses (or 8.06% for ill-defined masses) was obtained by using DACF on MiniMIAS database, with 5.86% (or 5.55%) and 6.14% (or 5.27%) improvements as compared to the standard DA and fuzzy c-means methods. Jian-Yong Lou, Xu-Lei Yang, and Ai-Ze Cao Copyright © 2015 Jian-Yong Lou et al. All rights reserved. On Better Estimating and Normalizing the Relationship between Clinical Parameters: Comparing Respiratory Modulations in the Photoplethysmogram and Blood Pressure Signal (DPOP versus PPV) Tue, 27 Jan 2015 09:37:12 +0000 DPOP (ΔPOP or Delta-POP) is a noninvasive parameter which measures the strength of respiratory modulations present in the pulse oximeter waveform. It has been proposed as a noninvasive alternative to pulse pressure variation (PPV) used in the prediction of the response to volume expansion in hypovolemic patients. We considered a number of simple techniques for better determining the underlying relationship between the two parameters. It was shown numerically that baseline-induced signal errors were asymmetric in nature, which corresponded to observation, and we proposed a method which combines a least-median-of-squares estimator with the requirement that the relationship passes through the origin (the LMSO method). We further developed a method of normalization of the parameters through rescaling DPOP using the inverse gradient of the linear fitted relationship. We propose that this normalization method (LMSO-N) is applicable to the matching of a wide range of clinical parameters. It is also generally applicable to the self-normalizing of parameters whose behaviour may change slightly due to algorithmic improvements. Paul S. Addison, Rui Wang, Alberto A. Uribe, and Sergio D. Bergese Copyright © 2015 Paul S. Addison et al. All rights reserved. Nonlinear Bayesian Estimation of BOLD Signal under Non-Gaussian Noise Mon, 26 Jan 2015 07:40:25 +0000 Modeling the blood oxygenation level dependent (BOLD) signal has been a subject of study for over a decade in the neuroimaging community. Inspired from fluid dynamics, the hemodynamic model provides a plausible yet convincing interpretation of the BOLD signal by amalgamating effects of dynamic physiological changes in blood oxygenation, cerebral blood flow and volume. The nonautonomous, nonlinear set of differential equations of the hemodynamic model constitutes the process model while the weighted nonlinear sum of the physiological variables forms the measurement model. Plagued by various noise sources, the time series fMRI measurement data is mostly assumed to be affected by additive Gaussian noise. Though more feasible, the assumption may cause the designed filter to perform poorly if made to work under non-Gaussian environment. In this paper, we present a data assimilation scheme that assumes additive non-Gaussian noise, namely, the -mixture noise, affecting the measurements. The proposed filter MAGSF and the celebrated EKF are put to test by performing joint optimal Bayesian filtering to estimate both the states and parameters governing the hemodynamic model under non-Gaussian environment. Analyses using both the synthetic and real data reveal superior performance of the MAGSF as compared to EKF. Ali Fahim Khan, Muhammad Shahzad Younis, and Khalid Bashir Bajwa Copyright © 2015 Ali Fahim Khan et al. All rights reserved. Autoradiography Imaging in Targeted Alpha Therapy with Timepix Detector Thu, 22 Jan 2015 14:30:25 +0000 There is a lack of data related to activity uptake and particle track distribution in targeted alpha therapy. These data are required to estimate the absorbed dose on a cellular level as alpha particles have a limited range and traverse only a few cells. Tracking of individual alpha particles is possible using the Timepix semiconductor radiation detector. We investigated the feasibility of imaging alpha particle emissions in tumour sections from mice treated with Thorium-227 (using APOMAB), with and without prior chemotherapy and Timepix detector. Additionally, the sensitivity of the Timepix detector to monitor variations in tumour uptake based on the necrotic tissue volume was also studied. Compartmental analysis model was used, based on the obtained imaging data, to assess the Th-227 uptake. Results show that alpha particle, photon, electron, and muon tracks were detected and resolved by Timepix detector. The current study demonstrated that individual alpha particle emissions, resulting from targeted alpha therapy, can be visualised and quantified using Timepix detector. Furthermore, the variations in the uptake based on the tumour necrotic volume have been observed with four times higher uptake for tumours pretreated with chemotherapy than for those without chemotherapy. Ruqaya AL Darwish, Alexander Hugo Staudacher, Eva Bezak, and Michael Paul Brown Copyright © 2015 Ruqaya AL Darwish et al. All rights reserved. Computation of Nonlinear Parameters of Heart Rhythm Using Short Time ECG Segments Thu, 22 Jan 2015 12:14:35 +0000 We propose the method to compute the nonlinear parameters of heart rhythm (correlation dimension and correlation entropy ) using 5-minute ECG recordings preferred for screening of population. Conversion of RR intervals’ time series into continuous function allows getting the new time series with different sampling rate dt. It has been shown that for all dt (250, 200, 125, and 100 ms) the cross-plots of and against embedding dimension for phase-space reconstruction start to level off at . The sample size at different sampling rates varied from 1200 at dt = 250 ms to 3000 at dt = 100 ms. Along with, the and means were not statistically different; that is, the sampling rate did not influence the results. We tested the feasibility of the method in two models: nonlinear heart rhythm dynamics in different states of autonomous nervous system and age-related characteristics of nonlinear parameters. According to the acquired data, the heart rhythm is more complex in childhood and adolescence with more influential parasympathetic influence against the background of elevated activity of sympathetic autonomous nervous system. Berik Koichubekov, Ilya Korshukov, Nazgul Omarbekova, Viktor Riklefs, Marina Sorokina, and Xenia Mkhitaryan Copyright © 2015 Berik Koichubekov et al. All rights reserved. Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery Thu, 22 Jan 2015 11:55:22 +0000 Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods. Haemwaan Sivaraks and Chotirat Ann Ratanamahatana Copyright © 2015 Haemwaan Sivaraks and Chotirat Ann Ratanamahatana. All rights reserved. The Direct Assignment Option as a Modular Design Component: An Example for the Setting of Two Predefined Subgroups Thu, 15 Jan 2015 08:20:08 +0000 Background. A phase II design with an option for direct assignment (stop randomization and assign all patients to experimental treatment based on interim analysis, IA) for a predefined subgroup was previously proposed. Here, we illustrate the modularity of the direct assignment option by applying it to the setting of two predefined subgroups and testing for separate subgroup main effects. Methods. We power the 2-subgroup direct assignment option design with 1 IA (DAD-1) to test for separate subgroup main effects, with assessment of power to detect an interaction in a post-hoc test. Simulations assessed the statistical properties of this design compared to the 2-subgroup balanced randomized design with 1 IA, BRD-1. Different response rates for treatment/control in subgroup 1 (0.4/0.2) and in subgroup 2 (0.1/0.2, 0.4/0.2) were considered. Results. The 2-subgroup DAD-1 preserves power and type I error rate compared to the 2-subgroup BRD-1, while exhibiting reasonable power in a post-hoc test for interaction. Conclusion. The direct assignment option is a flexible design component that can be incorporated into broader design frameworks, while maintaining desirable statistical properties, clinical appeal, and logistical simplicity. Ming-Wen An, Xin Lu, Daniel J. Sargent, and Sumithra J. Mandrekar Copyright © 2015 Ming-Wen An et al. All rights reserved. Nominated Texture Based Cervical Cancer Classification Wed, 14 Jan 2015 11:44:46 +0000 Accurate classification of Pap smear images becomes the challenging task in medical image processing. This can be improved in two ways. One way is by selecting suitable well defined specific features and the other is by selecting the best classifier. This paper presents a nominated texture based cervical cancer (NTCC) classification system which classifies the Pap smear images into any one of the seven classes. This can be achieved by extracting well defined texture features and selecting best classifier. Seven sets of texture features (24 features) are extracted which include relative size of nucleus and cytoplasm, dynamic range and first four moments of intensities of nucleus and cytoplasm, relative displacement of nucleus within the cytoplasm, gray level cooccurrence matrix, local binary pattern histogram, tamura features, and edge orientation histogram. Few types of support vector machine (SVM) and neural network (NN) classifiers are used for the classification. The performance of the NTCC algorithm is tested and compared to other algorithms on public image database of Herlev University Hospital, Denmark, with 917 Pap smear images. The output of SVM is found to be best for the most of the classes and better results for the remaining classes. Edwin Jayasingh Mariarputham and Allwin Stephen Copyright © 2015 Edwin Jayasingh Mariarputham and Allwin Stephen. All rights reserved. Image Reconstruction for Diffuse Optical Tomography Based on Radiative Transfer Equation Wed, 14 Jan 2015 08:44:00 +0000 Diffuse optical tomography is a novel molecular imaging technology for small animal studies. Most known reconstruction methods use the diffusion equation (DA) as forward model, although the validation of DA breaks down in certain situations. In this work, we use the radiative transfer equation as forward model which provides an accurate description of the light propagation within biological media and investigate the potential of sparsity constraints in solving the diffuse optical tomography inverse problem. The feasibility of the sparsity reconstruction approach is evaluated by boundary angular-averaged measurement data and internal angular-averaged measurement data. Simulation results demonstrate that in most of the test cases the reconstructions with sparsity regularization are both qualitatively and quantitatively more reliable than those with standard regularization. Results also show the competitive performance of the split Bregman algorithm for the DOT image reconstruction with sparsity regularization compared with other existing algorithms. Bo Bi, Bo Han, Weimin Han, Jinping Tang, and Li Li Copyright © 2015 Bo Bi et al. All rights reserved. Extraction of Heart Rate Variability from Smartphone Photoplethysmograms Mon, 12 Jan 2015 14:34:13 +0000 Heart rate variability (HRV) is a useful clinical tool for autonomic function assessment and cardiovascular diseases diagnosis. It is traditionally calculated from a dedicated medical electrocardiograph (ECG). In this paper, we demonstrate that HRV can also be extracted from photoplethysmograms (PPG) obtained by the camera of a smartphone. Sixteen HRV parameters, including time-domain, frequency-domain, and nonlinear parameters, were calculated from PPG captured by a smartphone for 30 healthy subjects and were compared with those derived from ECG. The statistical results showed that 14 parameters (AVNN, SDNN, CV, RMSSD, SDSD, TP, VLF, LF, HF, LF/HF, nLF, nHF, SD1, and SD2) from PPG were highly correlated (, ) with those from ECG, and 7 parameters (AVNN, TP, VLF, LF, HF, nLF, and nHF) from PPG were in good agreement with those from ECG within the acceptable limits. In addition, five different algorithms to detect the characteristic points of PPG wave were also investigated: peak point (PP), valley point (VP), maximum first derivative (M1D), maximum second derivative (M2D), and tangent intersection (TI). The results showed that M2D and TI algorithms had the best performance. These results suggest that the smartphone might be used for HRV measurement. Rong-Chao Peng, Xiao-Lin Zhou, Wan-Hua Lin, and Yuan-Ting Zhang Copyright © 2015 Rong-Chao Peng et al. All rights reserved. Extracting Information about the Rotator Cuff from Magnetic Resonance Images Using Deterministic and Random Techniques Mon, 12 Jan 2015 11:18:06 +0000 We consider some methods to extract information about the rotator cuff based on magnetic resonance images; the study aims to define an alternative method of display that might facilitate the detection of partial tears in the supraspinatus tendon. Specifically, we are going to use families of ellipsoidal triangular patches to cover the humerus head near the affected area. These patches are going to be textured and displayed with the information of the magnetic resonance images using the trilinear interpolation technique. For the generation of points to texture each patch, we propose a new method that guarantees the uniform distribution of its points using a random statistical method. Its computational cost, defined as the average computing time to generate a fixed number of points, is significantly lower as compared with deterministic and other standard statistical techniques. F. A. De Los Ríos and M. Paluszny Copyright © 2015 F. A. De Los Ríos and M. Paluszny. All rights reserved.