WSEAS Transactions on
Biology and Biomedicine
Print ISSN: 1109-9518
Volume 10, 2013
Issue 1, Volume 10, January 2013
Title of the Paper: Mathematical Modeling of Influenza and a Secondary Bacterial Infection
Authors: Kayla Henneman, Dan Van Peursem, Victor C. Huber
Abstract: In this work we model a pandemic where individuals are first infected with the influenza virus and later contract a secondary bacterial infection. The model uses a modified SIR approach with standard analytical and qualitative analysis. Theoretical questions are investigated concerning the proportion of the population to initially vaccinate for influenza, the proportion of the population to quarantine after being infected with influenza, and how improved treatments of bacterial infections all would play into reducing the net number of deaths.
Keywords: SIR Model, Vaccinations, Quarantine
Title of the Paper: Optimizing the Productivity in a Chemostat Model of Plasmid-Bearing Plasmid-Free Competition: The Case of General Uptake Functions
Authors: Neli S. Dimitrova
Abstract: The paper investigates a dynamical model of plasmid-bearing, plasmid-free competition in the chemostat with general specific growth rates. Based on a feedback control, global stabilization of the dynamics towards a practically important coexistence equilibrium point is achieved. The latter result is used to optimize the productivity of the chemostat. Results from computer simulation are reported to illustrate the theoretical studies.
Keywords: chemostat model, plasmid-bearing plasmid-free competition, feedback control, global stability, extremum seeking
Title of the Paper: The Prominence of Local Contrast Enhancement Transformation Approach in Osteoporotic Evaluation
Authors: V. Sapthagirivasan, M. Anburajan, V. Madhavan, T. Angu
Abstract: A computerized analysis of trabecular features was used to gauge the quality of bone from plain radiographs. The novel approach paved the way for detecting osteoporosis by BMD measurement, particularly in the regions, where DXA can’t be accessible. The initial diagnosis of osteoporosis helps in improving the life span of an individual to a great extent. The digital hip radiographs, being analyzed in the framework of trabecular boundness help in figuring out osteoporotic disorder. The aim of the study was to evaluate the capableness of local contrast enhancement transform (LCET) approach, by the extraction of trabecular features from plain radiographs. The sample consisted of 102 pre- and post- menopausal women (50.2 ± 14.2 years), for whom, the right femoral BMD and standard femoral radiographs were acquired. Two regions of interests (ROIs) were cropped from neck region and computerized image analysis was applied to obtain structure related trabecular parameters. Multiple linear regression analysis which is based on trabecular boundness consisted of both ROIs was performed. The trabecular features extracted by the LCET approach displayed considerable significance at the level of p<0.001 with femoral- neck and Ward BMD in both the ROIs. Also, the LCET approach, justified to be superior by 14% with respect to that of the conventional gradient analysis, by displaying trabecular boundness value of area under curve (AUC) of 0.959 (95% CI 0.914 – 1.004). Our findings suggest that the proposed LCET approach could serve as an accessory, in the regions, were DXA cannot be affordable.
Keywords: Osteoporosis, hip radiograph, trabecular boundness, bone micro architecture, femur neck, texture analysis, LCET, gradient approach
Title of the Paper: Identification of Markers from a Set of Spectral Courses
Authors: Jiri Knizek, Ladislav Beranek, Pavel Bouchal, Borivoj Vojtesek, Rudolf Nenutil, Pavel Tomsik
Abstract: A brief introduction of algorithms for the statistical identification of markers from a set of spectral courses is the topic of our paper. Partial results, demonstrated by pictures, are very promising. The proposed algorithm is generally applicable for an arbitrary problem of marker identification by tests in a set of quantifying dependences.
Keywords: Marker, biomarker, regression, tests of hypotheses, software
Title of the Paper: Colour Image Segmentation Approach for Detection of Malaria Parasites Using Various Colour Models and k-Means Clustering
Authors: Aimi Salihah Abdul-Nasir, Mohd Yusoff Mashor, Zeehaida Mohamed
Abstract: Malaria is a serious global health problem that is responsible for nearly one million deaths each year. With the large number of cases diagnosed over the year, rapid detection and accurate diagnosis of malaria infection which facilitates prompt treatment are essential to control malaria. This paper presents a colour image segmentation approach for detection of malaria parasites that has been applied on malaria images of P. vivax species. In order to obtain the segmented red blood cells infected with malaria parasites, the images are first enhanced by using partial contrast stretching. Then, an unsupervised segmentation technique namely k-means clustering has been used to segment the infected cell from the background. Different colour components of RGB, HSI and C-Y colour models have been analysed to identify colour component that can give significant segmentation performance. Finally, median filter and seeded region growing area extraction algorithms have been applied for smoothing the image and remove any unwanted regions from the image, respectively. The proposed segmentation method has been evaluated on 100 malaria images. Overall, segmentation using S component of C-Y colour model has proven to be the best in segmenting the malaria image with segmentation accuracy and F-score of 99.46% and 0.9370, respectively.
Keywords: Malaria, Colour Segmentation, Colour Models, k-Means Clustering, Seeded Region Growing Area Extraction
Issue 2, Volume 10, July 2013
Title of the Paper: Detecting and Locating of Brain Abnormality in MR Images Using Texture Feature Analysis and Improved Probabilistic Relaxation Methods
Authors: Yao-Ming Yu
Abstract: Medical imaging has become a major tool in clinical trials since it enables rapid diagnosis with visualization and quantitative assessment. In the study, a detecting method of brain abnormality is proposed through magnetic resonance imaging. The proposed method is composed of four procedures. First the preprocessing is employed to remove noises and enhance the homogeneity of soft tissues. After preprocessing, we adopt the spatial gray level dependence method to compute four texture features of each image. Then, the improved probability relaxation method is applied to discriminate the brain abnormality with extracted texture information. The isolated noises are removed by using neighborhood processing. Final the performance of the improved method has been evaluated and compared to the original method. This proposed method performs better results than the other one, which can be used in further processing stages. We have developed a computer-aided detection system to distinguish the tumor and find the location and coarse contour from brain MRIs. The system can assist doctors to diagnose whether the brain has abnormal and train inexperienced doctors. The proposed algorithm can play a useful role for storage, filtering and indexing of mass MRI data, and furthermore it provides an initial step to find accurate tumor boundaries.
Keywords: Computer-Aided Detection System, Texture Feature Analysis, Spatial Gray Level Dependence, Probability Relaxation Method, Magnetic Resonance Image, Brain Tumor
Title of the Paper: Controlling Plague Among Prairie Dogs: A Two Colony Epidemiological Model
Authors: Stephanie Jensen, Dan Van Peursem
Abstract: In this work we model the spread of sylvatic plague between two prairie dog colonies. The mathematical model is one where a population of fleas transmits the plague to the host population of prairie dogs. Distance between the two colonies is examined as well as using insecticide to increase the death rates on the fleas that spread the disease between prairie dogs. Semi-analytical solutions for equilibria are derived using a perturbation series approach.
Keywords: Sylvatic Plague, Black Tailed Prairie Dog, Host, Mathematical Model, Epidemiology
Title of the Paper: Numerical-Analytical Solutions of Predator-Prey Models
Authors: Gilberto Gonzalez-Parra, Abraham J. Arenas, Myladis R. Cogollo
Abstract: This paper deals with the construction of piecewise analytic approximate solutions for nonlinear initial value problems modeled by a system of nonlinear ordinary differential equations. In real world several biological and environmental parameters in the predator-prey model vary in time. Thus, non-autonomous systems are impor- tant to be studied. We show the effectiveness of the method for autonomous and non-autonomous predator-prey systems. The method we have used is called the differential transformation method which has some suitable prop- erties such as accuracy, low computational cost, easiness of implementation and simulation as well as preserving properties of the exact theoretical solution of the problem. The accuracy of the method is checked by numerical comparison with fourth-order Runge-Kutta results applied to several predator-prey examples.
Keywords: Differential transformation method, Population dynamics, Nonlinear differential system, Predator-prey system
Title of the Paper: Knowledge-based Modeling of Multi-factor Processes in Biotechnology and Microbial Ecology
Authors: Svetla Vassileva
Abstract: Biotechnological and ecological processes are multi-factor nonlinear system, its dynamic could be considered as sequence of phases. Bacterial growth in batch culture can be modeled as a sequence of four integrated phases: lag phase, exponential or log-phase, stationary phase, and death phase. Ecological processes are connected with the seasonal changes for certain period of time – one season, one year, a decade or a century. Methodologies which can provide their adequate mathematical descriptions are based on the synthesis of local MIMO-models; the transition between phases is realized by using time or state conditions markers in form of IF-THEN rules, expressing complex relations between influential input-output variables. Obtaining of such relations is a nontrivial task. For this reason human expertise and learning capacity of modern AIapproaches is embedded. Main purpose of the presented paper is to demonstrate these opportunities on some multi-factor and multiphase biotechnological processes. The application of knowledge-based system on the multiphase processes is presented in connection with monitoring and inferential measurements systems development.
Keywords: Knowledge-based systems, intelligent industry, multi-factor nonlinear system, multiphase modeling, artificial intelligence, biotechnology, microbial ecology